Hbase官方文档中文版

http://abloz.com/hbase/book.html#quickstart

http://yankaycom-wordpress.stor.sinaapp.com/hbase/book.html?q=/wp-content/hbase/book.html#hbase_default_configurations

序

这本书是 HBase 的官方指南。 版本为 0.95-SNAPSHOT 。可以在HBase官网上找到它。也可以在 javadoc, JIRA 和 wiki 找到更多的资料。

此书正在编辑中。 可以向 HBase 官方提供补丁JIRA.

这个版本系译者水平限制，没有理解清楚或不需要翻译的地方保留英文原文。

最前面的话

若这是你第一次踏入分布式计算的精彩世界，你会感到这是一个有趣的年代。分布式计算是很难的，做一个分布式系统需要很多软硬件和网络的技能。你的集群可以会因为各式各样的错误发生故障。比如HBase本身的Bug,错误的配置(包括操作系统)，硬件的故障(网卡和磁盘甚至内存) 如果你一直在写单机程序的话，你需要重新开始学习。这里就是一个好的起点: 分布式计算的谬论.

Chapter 1. 入门

1.1. 介绍

Section 1.2, “快速开始”会介绍如何运行一个单机版的HBase.他运行在本地磁盘上。 Section 2, “配置” 会介绍如何运行一个分布式的HBase。他运行在HDFS上

1.2. 快速开始

本指南介绍了在单机安装HBase的方法。会引导你通过shell创建一个表，插入一行，然后删除它，最后停止HBase。只要10分钟就可以完成以下的操作。

1.2.1. 下载解压最新版本

选择一个 Apache 下载镜像，下载 HBase Releases. 点击 stable目录，然后下载后缀为 .tar.gz 的文件; 例如 hbase-0.95-SNAPSHOT.tar.gz.

解压缩，然后进入到那个要解压的目录.

$ tar xfz hbase-0.95-SNAPSHOT.tar.gz
$ cd hbase-0.95-SNAPSHOT

现在你已经可以启动HBase了。但是你可能需要先编辑 conf/hbase-site.xml 去配置hbase.rootdir，来选择HBase将数据写到哪个目录 .

  
    hbase.rootdir
    file:///DIRECTORY/hbase
  

将 DIRECTORY 替换成你期望写文件的目录. 默认 hbase.rootdir 是指向 /tmp/hbase-${user.name} ，也就说你会在重启后丢失数据(重启的时候操作系统会清理/tmp目录)

1.2.2. 启动 HBase

现在启动HBase:

$ ./bin/start-hbase.sh
starting Master, logging to logs/hbase-user-master-example.org.out

现在你运行的是单机模式的Hbaes。所有的服务都运行在一个JVM上，包括HBase和Zookeeper。HBase的日志放在logs目录,当你启动出问题的时候，可以检查这个日志。

是否安装了 java ?

你需要确认安装了Oracle的1.6 版本的java.如果你在命令行键入java有反应说明你安装了Java。如果没有装，你需要先安装，然后编辑conf/hbase-env.sh，将其中的JAVA_HOME指向到你Java的安装目录。

1.2.3. Shell 练习

用shell连接你的HBase

$ ./bin/hbase shell
HBase Shell; enter 'help' for list of supported commands.
Type "exit" to leave the HBase Shell
Version: 0.90.0, r1001068, Fri Sep 24 13:55:42 PDT 2010

hbase(main):001:0> 

输入 help 然后  可以看到一列shell命令。这里的帮助很详细，要注意的是表名，行和列需要加引号。

创建一个名为 test 的表，这个表只有一个 列族 为 cf。可以列出所有的表来检查创建情况，然后插入些值。

hbase(main):003:0> create 'test', 'cf'
0 row(s) in 1.2200 seconds
hbase(main):003:0> list 'table'
test
1 row(s) in 0.0550 seconds
hbase(main):004:0> put 'test', 'row1', 'cf:a', 'value1'
0 row(s) in 0.0560 seconds
hbase(main):005:0> put 'test', 'row2', 'cf:b', 'value2'
0 row(s) in 0.0370 seconds
hbase(main):006:0> put 'test', 'row3', 'cf:c', 'value3'
0 row(s) in 0.0450 seconds

以上我们分别插入了3行。第一个行key为row1, 列为 cf:a， 值是 value1。HBase中的列是由 列族前缀和列的名字组成的，以冒号间隔。例如这一行的列名就是a.

检查插入情况.

Scan这个表，操作如下

hbase(main):007:0> scan 'test'
ROW        COLUMN+CELL
row1       column=cf:a, timestamp=1288380727188, value=value1
row2       column=cf:b, timestamp=1288380738440, value=value2
row3       column=cf:c, timestamp=1288380747365, value=value3
3 row(s) in 0.0590 seconds

Get一行，操作如下

hbase(main):008:0> get 'test', 'row1'
COLUMN      CELL
cf:a        timestamp=1288380727188, value=value1
1 row(s) in 0.0400 seconds

disable 再 drop 这张表，可以清除你刚刚的操作

hbase(main):012:0> disable 'test'
0 row(s) in 1.0930 seconds
hbase(main):013:0> drop 'test'
0 row(s) in 0.0770 seconds 

关闭shell

hbase(main):014:0> exit

1.2.4. 停止 HBase

运行停止脚本来停止HBase.

$ ./bin/stop-hbase.sh
stopping hbase...............

1.2.5. 下一步该做什么

以上步骤仅仅适用于实验和测试。接下来你可以看 Section 2., “配置” ，我们会介绍不同的HBase运行模式，运行分布式HBase中需要的软件 和如何配置。

2. 配置

 

本章是慢速开始配置指导。

HBase有如下需要，请仔细阅读本章节以确保所有的需要都被满足。如果需求没有能满足，就有可能遇到莫名其妙的错误甚至丢失数据。

HBase使用和Hadoop一样配置系统。要配置部署，编辑conf/hbase-env.sh文件中的环境变量——该配置文件主要启动脚本用于获取已启动的集群——然后增加配置到XML文件，如同覆盖HBase缺省配置，告诉HBase用什么文件系统， 全部ZooKeeper位置 [1] 。

在分布模式下运行时，在编辑HBase配置文件之后，确认将conf目录复制到集群中的每个节点。HBase不会自动同步。使用rsync.

[1] 小心编辑XML。确认关闭所有元素。采用 xmllint 或类似工具确认文档编辑后是良好格式化的。

2.1. 基础条件

This section lists required services and some required system configuration.

2.1.1 java

和Hadoop一样，HBase需要Oracle版本的Java6.除了那个有问题的u18版本其他的都可以用，最好用最新的。

2.1. 操作系统

2.1.2.1. ssh

必须安装ssh ， sshd 也必须运行，这样Hadoop的脚本才可以远程操控其他的Hadoop和HBase进程。ssh之间必须都打通，不用密码都可以登录，详细方法可以Google一下 ("ssh passwordless login").

2.1.2.2. DNS

HBase使用本地 hostname 才获得IP地址. 正反向的DNS都是可以的.

如果你的机器有多个接口，HBase会使用hostname指向的主接口.

如果还不够，你可以设置 hbase.regionserver.dns.interface 来指定主接口。当然你的整个集群的配置文件都必须一致，每个主机都使用相同的网络接口

还有一种方法是设置 hbase.regionserver.dns.nameserver来指定nameserver，不使用系统带的.

2.1.2.3. Loopback IP

HBase expects the loopback IP address to be 127.0.0.1. Ubuntu and some other distributions, for example, will default to 127.0.1.1 and this will cause problems for you.

/etc/hosts should look something like this:

            127.0.0.1 localhost
            127.0.0.1 ubuntu.ubuntu-domain ubuntu

2.1.2.4. NTP

集群的时钟要保证基本的一致。稍有不一致是可以容忍的，但是很大的不一致会造成奇怪的行为。 运行 NTP 或者其他什么东西来同步你的时间.

如果你查询的时候或者是遇到奇怪的故障，可以检查一下系统时间是否正确!

2.1.2.5.  ulimit 和 nproc

HBase是数据库，会在同一时间使用很多的文件句柄。大多数linux系统使用的默认值1024是不能满足的，会导致FAQ: Why do I see "java.io.IOException...(Too many open files)" in my logs?异常。还可能会发生这样的异常

      2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream java.io.EOFException
      2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block blk_-6935524980745310745_1391901
      

所以你需要修改你的最大文件句柄限制。可以设置到10k。大致的数学运算如下：每列族至少有1个存储文件(StoreFile) 可能达到5-6个如果区域有压力。将每列族的存储文件平均数目和每区域服务器的平均区域数目相乘。例如：假设一个模式有3个列族，每个列族有3个存储文件，每个区域服务器有100个区域，JVM 将打开3 * 3 * 100 = 900 个文件描述符(不包含打开的jar文件，配置文件等)

你还需要修改 hbase 用户的 nproc，在压力下，如果过低会造成 OutOfMemoryError异常[3] [4]。

需要澄清的，这两个设置是针对操作系统的，不是HBase本身的。有一个常见的错误是HBase运行的用户，和设置最大值的用户不是一个用户。在HBase启动的时候，第一行日志会现在ulimit信息，确保其正确。[5]

2.1.2.5.1. 在Ubuntu上设置ulimit

如果你使用的是Ubuntu,你可以这样设置:

在文件 /etc/security/limits.conf 添加一行，如:

hadoop  -       nofile  32768

可以把 hadoop 替换成你运行HBase和Hadoop的用户。如果你用两个用户，你就需要配两个。还有配nproc hard 和 soft limits. 如:

hadoop soft/hard nproc 32000

.

在 /etc/pam.d/common-session 加上这一行:

session required  pam_limits.so

否则在 /etc/security/limits.conf上的配置不会生效.

还有注销再登录，这些配置才能生效!

2.1.2.6. Windows

HBase没有怎么在Windows下测试过。所以不推荐在Windows下运行.

如果你实在是想运行，需要安装Cygwin 并虚拟一个unix环境.详情请看 Windows 安装指导 . 或者 搜索邮件列表找找最近的关于windows的注意点

2.1.3. hadoop

选择 Hadoop 版本对HBase部署很关键。下表显示不同HBase支持的Hadoop版本信息。基于HBase版本，应该选择合适的Hadoop版本。我们没有绑定 Hadoop 发行版选择。可以从Apache使用 Hadoop 发行版，或了解一下Hadoop发行商产品： http://wiki.apache.org/hadoop/Distributions%20and%20Commercial%20Support

Table 2.1. Hadoop version support matrix

	HBase-0.92.x	HBase-0.94.x	HBase-0.96
Hadoop-0.20.205	S	X	X
Hadoop-0.22.x	S	X	X
Hadoop-1.0.x	S	S	S
Hadoop-1.1.x	NT	S	S
Hadoop-0.23.x	X	S	NT
Hadoop-2.x	X	S	S

S = supported and tested,支持

X = not supported,不支持

NT = not tested enough.可以运行但测试不充分

由于 HBase 依赖 Hadoop，它配套发布了一个Hadoop jar 文件在它的 lib 下。该套装jar仅用于独立模式。在分布式模式下，Hadoop版本必须和HBase下的版本一致。用你运行的分布式Hadoop版本jar文件替换HBase lib目录下的Hadoop jar文件，以避免版本不匹配问题。确认替换了集群中所有HBase下的jar文件。Hadoop版本不匹配问题有不同表现，但看起来都像挂掉了。

2.1.3.1. Apache HBase 0.92 and 0.94

HBase 0.92 and 0.94 versions can work with Hadoop versions, 0.20.205, 0.22.x, 1.0.x, and 1.1.x. HBase-0.94 can additionally work with Hadoop-0.23.x and 2.x, but you may have to recompile the code using the specific maven profile (see top level pom.xml)

2.1.3.2. Apache HBase 0.96

Apache HBase 0.96.0 requires Apache Hadoop 1.x at a minimum, and it can run equally well on hadoop-2.0. As of Apache HBase 0.96.x, Apache Hadoop 1.0.x at least is required. We will no longer run properly on older Hadoops such as 0.20.205 or branch-0.20-append. Do not move to Apache HBase 0.96.x if you cannot upgrade your Hadoop[6].

2.1.3.3. Hadoop versions 0.20.x - 1.x

HBase will lose data unless it is running on an HDFS that has a durable sync implementation. DO NOT use Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0 which DO NOT have this attribute. Currently only Hadoop versions 0.20.205.x or any release in excess of this version -- this includes hadoop-1.0.0 -- have a working, durable sync[7]. Sync has to be explicitly enabled by setting dfs.support.append equal to true on both the client side -- in hbase-site.xml -- and on the serverside in hdfs-site.xml (The sync facility HBase needs is a subset of the append code path).

        dfs.support.append      true              

You will have to restart your cluster after making this edit. Ignore the chicken-little comment you'll find in the hdfs-default.xml in the description for thedfs.support.append configuration.

2.1.3.4. Hadoop 安全性

HBase运行在Hadoop 0.20.x上，就可以使用其中的安全特性 -- 只要你用这两个版本0.20S 和CDH3B3，然后把hadoop.jar替换掉就可以了.

2.1.3.5. dfs.datanode.max.xcievers

一个 Hadoop HDFS Datanode 有一个同时处理文件的上限. 这个参数叫 xcievers (Hadoop的作者把这个单词拼错了). 在你加载之前，先确认下你有没有配置这个文件conf/hdfs-site.xml里面的xceivers参数，至少要有4096:

      
        dfs.datanode.max.xcievers
        4096
      
      

对于HDFS修改配置要记得重启.

如果没有这一项配置，你可能会遇到奇怪的失败。你会在Datanode的日志中看到xcievers exceeded，但是运行起来会报 missing blocks错误。例如: 10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node: java.io.IOException: No live nodes contain current block. Will get new block locations from namenode and retry... ^[5]

See also Section 13.3.4, “Case Study #4 (xcievers Config)”

2.2. HBase运行模式:单机和分布式

HBase有两个运行模式: Section 2.4.1, “单机模式” 和 Section 2.4.2, “分布式模式”. 默认是单机模式，如果要分布式模式你需要编辑 conf 文件夹中的配置文件.

不管是什么模式，你都需要编辑 conf/hbase-env.sh来告知HBase java的安装路径.在这个文件里你还可以设置HBase的运行环境，诸如 heapsize和其他 JVM有关的选项, 还有Log文件地址，等等. 设置 JAVA_HOME指向 java安装的路径.

2.21. 单机模式

这是默认的模式，在 Section 1.2, “快速开始” 一章中介绍的就是这个模式. 在单机模式中，HBase使用本地文件系统，而不是HDFS ，所有的服务和zooKeeper都运作在一个JVM中。zookeep监听一个端口，这样客户端就可以连接HBase了。

2.2.2. 分布式模式

分布式模式分两种。伪分布式模式是把进程运行在一台机器上，但不是一个JVM.而完全分布式模式就是把整个服务被分布在各个节点上了 ^[6].

分布式模式需要使用 Hadoop Distributed File System (HDFS).可以参见 HDFS需求和指导来获得关于安装HDFS的指导。在操作HBase之前，你要确认HDFS可以正常运作。

在我们安装之后，你需要确认你的伪分布式模式或者 完全分布式模式的配置是否正确。这两个模式可以使用同一个验证脚本Section 2.2.3, “运行和确认你的安装”。

2.2.2.1. 伪分布式模式

伪分布式模式是一个相对简单的分布式模式。这个模式是用来测试的。不能把这个模式用于生产环节，也不能用于测试性能。

你确认HDFS安装成功之后，就可以先编辑 conf/hbase-site.xml。在这个文件你可以加入自己的配置，这个配置会覆盖 Section 2.6.1.1, “HBase 默认配置” and Section 2.2.2.2.3, “HDFS客户端配置”. 运行HBase需要设置hbase.rootdir 属性.该属性是指HBase在HDFS中使用的目录的位置。例如，要想 /hbase 目录，让namenode 监听locahost的9000端口，只有一份数据拷贝(HDFS默认是3份拷贝)。可以在 hbase-site.xml 写上如下内容

  ...
  
    hbase.rootdir
    hdfs://localhost:9000/hbase
    The directory shared by RegionServers.
    
  
  
    dfs.replication
    1
    The replication count for HLog & HFile storage. Should not be greater than HDFS datanode count.
    
  
  ...

Note

让HBase自己创建 hbase.rootdir

Note

上面我们绑定到 localhost. 也就是说除了本机，其他机器连不上HBase。所以你需要设置成别的，才能使用它。

现在可以跳到 Section 2.2.3, “运行和确认你的安装” 来运行和确认你的伪分布式模式安装了。 ^[7]

2.2.2.1.1. 伪分布模式配置文件

下面是伪分布模式设置的配置文件示例。

hdfs-site.xml

    ...          dfs.name.dir      /Users/local/user.name/hdfs-data-name              dfs.data.dir      /Users/local/user.name/hdfs-data              dfs.replication      1        ...    

hbase-site.xml

    ...          hbase.rootdir      hdfs://localhost:8020/hbase              hbase.zookeeper.quorum      localhost              hbase.cluster.distributed      true        ...    

2.2.2.1.2. 伪分布模式附加

2.2.2.1.2.1. 启动

启动初始 HBase 集群...

% bin/start-hbase.sh

在同一服务器启动额外备份主服务器

% bin/local-master-backup.sh start 1

... '1' 表示使用端口 60001 & 60011, 该备份主服务器及其log文件放在logs/hbase-${USER}-1-master-${HOSTNAME}.log.

启动多个备份主服务器...

% bin/local-master-backup.sh start 2 3

可以启动到 9 个备份服务器 (总数10 个).

启动更多 regionservers...

% bin/local-regionservers.sh start 1

'1' 表示使用端口 60201 & 60301 ，log文件在 logs/hbase-${USER}-1-regionserver-${HOSTNAME}.log.

在刚运行的regionserver上增加 4 个额外 regionservers ...

% bin/local-regionservers.sh start 2 3 4 5

支持到 99 个额外regionservers (总100个).

2.2.2.1.2.2. 停止

假设想停止备份主服务器 # 1, 运行...

% cat /tmp/hbase-${USER}-1-master.pid |xargs kill -9

注意 bin/local-master-backup.sh 停止 1 会尝试停止主服务器相关集群。

停止单独 regionserver, 运行...

% bin/local-regionservers.sh stop 1  	                

2.2.2.2. 完全分布式模式

要想运行完全分布式模式，你要进行如下配置，先在 hbase-site.xml, 加一个属性 hbase.cluster.distributed 设置为 true 然后把 hbase.rootdir 设置为HDFS的NameNode的位置。 例如，你的namenode运行在namenode.example.org，端口是9000 你期望的目录是 /hbase,使用如下的配置

  ...
  
    hbase.rootdir
    hdfs://namenode.example.org:9000/hbase
    The directory shared by RegionServers.
    
  
  
    hbase.cluster.distributed
    true
    The mode the cluster will be in. Possible values are
      false: standalone and pseudo-distributed setups with managed Zookeeper
      true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
    
  
  ...

2.2.2.2.1. regionservers

完全分布式模式的还需要修改conf/regionservers. 在 Section 2.7.1.2, “regionservers” 列出了你希望运行的全部 HRegionServer，一行写一个host (就像Hadoop里面的 slaves 一样). 列在这里的server会随着集群的启动而启动，集群的停止而停止.

2.2.2.2.2. ZooKeeper 和 HBase

 

2.2.2.2.3. HDFS客户端配置

如果你希望Hadoop集群上做HDFS 客户端配置 ，例如你的HDFS客户端的配置和服务端的不一样。按照如下的方法配置，HBase就能看到你的配置信息:

• 在hbase-env.sh里将HBASE_CLASSPATH环境变量加上HADOOP_CONF_DIR 。

• 在${HBASE_HOME}/conf下面加一个 hdfs-site.xml (或者 hadoop-site.xml) ，最好是软连接

• 如果你的HDFS客户端的配置不多的话，你可以把这些加到 hbase-site.xml上面.

例如HDFS的配置 dfs.replication.你希望复制5份，而不是默认的3份。如果你不照上面的做的话，HBase只会复制3份。

2.2.3. 运行和确认你的安装

首先确认你的HDFS是运行着的。你可以运行HADOOP_HOME中的 bin/start-hdfs.sh 来启动HDFS.你可以通过put命令来测试放一个文件，然后有get命令来读这个文件。通常情况下HBase是不会运行mapreduce的。所以比不需要检查这些。

如果你自己管理ZooKeeper集群，你需要确认它是运行着的。如果是HBase托管，ZoopKeeper会随HBase启动。

用如下命令启动HBase:

bin/start-hbase.sh

这个脚本在 HBASE_HOME目录里面。

你现在已经启动HBase了。HBase把log记在 logs 子目录里面. 当HBase启动出问题的时候，可以看看Log.

HBase也有一个界面，上面会列出重要的属性。默认是在Master的60010端口上H (HBase RegionServers 会默认绑定 60020端口，在端口60030上有一个展示信息的界面 ).如果Master运行在 master.example.org，端口是默认的话，你可以用浏览器在 http://master.example.org:60010看到主界面. .

一旦HBase启动，参见Section 1.2.3, “Shell 练习”可以看到如何建表，插入数据，scan你的表，还有disable这个表，最后把它删掉。

可以在HBase Shell停止HBase

$ ./bin/stop-hbase.sh
stopping hbase...............

停止操作需要一些时间，你的集群越大，停的时间可能会越长。如果你正在运行一个分布式的操作，要确认在HBase彻底停止之前，Hadoop不能停.

---

 

2.3. 配置文件

 

HBase的配置系统和Hadoop一样。在conf/hbase-env.sh配置系统的部署信息和环境变量。 -- 这个配置会被启动shell使用 -- 然后在XML文件里配置信息，覆盖默认的配置。告知HBase使用什么目录地址，ZooKeeper的位置等等信息。 ^[10] .

当你使用分布式模式的时间，当你编辑完一个文件之后，记得要把这个文件复制到整个集群的conf 目录下。HBase不会帮你做这些，你得用 rsync.

2.3.1. hbase-site.xml 和 hbase-default.xml

正如Hadoop放置HDFS的配置文件hdfs-site.xml，HBase的配置文件是 conf/hbase-site.xml. 你可以在 Section 2.3.1.1, “HBase 默认配置”找到配置的属性列表。你也可以看有代码里面的hbase-default.xml文件，他在src/main/resources目录下。

不是所有的配置都在 hbase-default.xml出现.只要改了代码，配置就有可能改变，所以唯一了解这些被改过的配置的办法是读源代码本身。

要注意的是，要重启集群才能是配置生效。

2.3.1.1. HBase 默认配置

HBase 默认配置

该文档是用hbase默认配置文件生成的，文件源是 hbase-default.xml

hbase.rootdir

这个目录是region server的共享目录，用来持久化HBase。URL需要是'完全正确'的，还要包含文件系统的scheme。例如，要表示hdfs中的'/hbase'目录，namenode 运行在namenode.example.org的9090端口。则需要设置为hdfs://namenode.example.org:9000/hbase。默认情况下HBase是写到/tmp的。不改这个配置，数据会在重启的时候丢失。

默认: file:///tmp/hbase-${user.name}/hbase

hbase.master.port

HBase的Master的端口.

默认: 60000

hbase.cluster.distributed

HBase的运行模式。false是单机模式，true是分布式模式。若为false,HBase和Zookeeper会运行在同一个JVM里面。

默认: false

hbase.tmp.dir

本地文件系统的临时文件夹。可以修改到一个更为持久的目录上。(/tmp会在重启时清楚)

默认:${java.io.tmpdir}/hbase-${user.name}

hbase.local.dir

作为本地存储，位于本地文件系统的路径。

默认: ${hbase.tmp.dir}/local/

hbase.master.info.port

HBase Master web 界面端口. 设置为-1 意味着你不想让他运行。

默认: 60010

hbase.master.info.bindAddress

HBase Master web 界面绑定的端口

默认: 0.0.0.0

hbase.client.write.buffer

HTable客户端的写缓冲的默认大小。这个值越大，需要消耗的内存越大。因为缓冲在客户端和服务端都有实例，所以需要消耗客户端和服务端两个地方的内存。得到的好处是，可以减少RPC的次数。可以这样估算服务器端被占用的内存： hbase.client.write.buffer * hbase.regionserver.handler.count

默认: 2097152

hbase.regionserver.port

HBase RegionServer绑定的端口

默认: 60020

hbase.regionserver.info.port

HBase RegionServer web 界面绑定的端口 设置为 -1 意味这你不想与运行 RegionServer 界面.

默认: 60030

hbase.regionserver.info.port.auto

Master或RegionServer是否要动态搜一个可以用的端口来绑定界面。当hbase.regionserver.info.port已经被占用的时候，可以搜一个空闲的端口绑定。这个功能在测试的时候很有用。默认关闭。

默认: false

hbase.regionserver.info.bindAddress

HBase RegionServer web 界面的IP地址

默认: 0.0.0.0

hbase.regionserver.class

RegionServer 使用的接口。客户端打开代理来连接region server的时候会使用到。

默认: org.apache.hadoop.hbase.ipc.HRegionInterface

hbase.client.pause

通常的客户端暂停时间。最多的用法是客户端在重试前的等待时间。比如失败的get操作和region查询操作等都很可能用到。

默认: 1000

hbase.client.retries.number

最大重试次数。所有需重试操作的最大值。例如从root region服务器获取root region，Get单元值，行Update操作等等。这是最大重试错误的值。  Default: 10.

默认: 10

hbase.bulkload.retries.number

最大重试次数。 原子批加载尝试的迭代最大次数。 0 永不放弃。默认: 0.

默认: 0

 

hbase.client.scanner.caching

当调用Scanner的next方法，而值又不在缓存里的时候，从服务端一次获取的行数。越大的值意味着Scanner会快一些，但是会占用更多的内存。当缓冲被占满的时候，next方法调用会越来越慢。慢到一定程度，可能会导致超时。例如超过了hbase.regionserver.lease.period。

默认: 100

hbase.client.keyvalue.maxsize

一个KeyValue实例的最大size.这个是用来设置存储文件中的单个entry的大小上界。因为一个KeyValue是不能分割的，所以可以避免因为数据过大导致region不可分割。明智的做法是把它设为可以被最大region size整除的数。如果设置为0或者更小，就会禁用这个检查。默认10MB。

默认: 10485760

hbase.regionserver.lease.period

客户端租用HRegion server 期限，即超时阀值。单位是毫秒。默认情况下，客户端必须在这个时间内发一条信息，否则视为死掉。

默认: 60000

hbase.regionserver.handler.count

RegionServers受理的RPC Server实例数量。对于Master来说，这个属性是Master受理的handler数量

默认: 10

hbase.regionserver.msginterval

RegionServer 发消息给 Master 时间间隔，单位是毫秒

默认: 3000

hbase.regionserver.optionallogflushinterval

将Hlog同步到HDFS的间隔。如果Hlog没有积累到一定的数量，到了时间，也会触发同步。默认是1秒，单位毫秒。

默认: 1000

hbase.regionserver.regionSplitLimit

region的数量到了这个值后就不会在分裂了。这不是一个region数量的硬性限制。但是起到了一定指导性的作用，到了这个值就该停止分裂了。默认是MAX_INT.就是说不阻止分裂。

默认: 2147483647

hbase.regionserver.logroll.period

提交commit log的间隔，不管有没有写足够的值。

默认: 3600000

hbase.regionserver.hlog.reader.impl

HLog file reader 的实现.

默认: org.apache.hadoop.hbase.regionserver.wal.SequenceFileLogReader

hbase.regionserver.hlog.writer.impl

HLog file writer 的实现.

默认: org.apache.hadoop.hbase.regionserver.wal.SequenceFileLogWriter

 

 

hbase.regionserver.nbreservationblocks

储备的内存block的数量(译者注:就像石油储备一样)。当发生out of memory 异常的时候，我们可以用这些内存在RegionServer停止之前做清理操作。

默认: 4

hbase.zookeeper.dns.interface

当使用DNS的时候，Zookeeper用来上报的IP地址的网络接口名字。

默认: default

hbase.zookeeper.dns.nameserver

当使用DNS的时候，Zookeepr使用的DNS的域名或者IP 地址，Zookeeper用它来确定和master用来进行通讯的域名.

默认: default

hbase.regionserver.dns.interface

当使用DNS的时候，RegionServer用来上报的IP地址的网络接口名字。

默认: default

hbase.regionserver.dns.nameserver

当使用DNS的时候，RegionServer使用的DNS的域名或者IP 地址，RegionServer用它来确定和master用来进行通讯的域名.

默认: default

hbase.master.dns.interface

当使用DNS的时候，Master用来上报的IP地址的网络接口名字。

默认: default

hbase.master.dns.nameserver

当使用DNS的时候，RegionServer使用的DNS的域名或者IP 地址，Master用它来确定用来进行通讯的域名.

默认: default

hbase.balancer.period

Master执行region balancer的间隔。

默认: 300000

hbase.regions.slop

当任一区域服务器有average + (average * slop)个分区，将会执行重新均衡。默认 20% slop .

默认:0.2

hbase.master.logcleaner.ttl

Hlog存在于.oldlogdir 文件夹的最长时间, 超过了就会被 Master 的线程清理掉.

默认: 600000

hbase.master.logcleaner.plugins

LogsCleaner服务会执行的一组LogCleanerDelegat。值用逗号间隔的文本表示。这些WAL/HLog cleaners会按顺序调用。可以把先调用的放在前面。你可以实现自己的LogCleanerDelegat，加到Classpath下，然后在这里写下类的全称。一般都是加在默认值的前面。

默认: org.apache.hadoop.hbase.master.TimeToLiveLogCleaner

hbase.regionserver.global.memstore.upperLimit

单个region server的全部memtores的最大值。超过这个值，一个新的update操作会被挂起，强制执行flush操作。

默认: 0.4

hbase.regionserver.global.memstore.lowerLimit

当强制执行flush操作的时候，当低于这个值的时候，flush会停止。默认是堆大小的 35% . 如果这个值和 hbase.regionserver.global.memstore.upperLimit 相同就意味着当update操作因为内存限制被挂起时，会尽量少的执行flush(译者注:一旦执行flush，值就会比下限要低，不再执行)

默认: 0.35

hbase.server.thread.wakefrequency

service工作的sleep间隔，单位毫秒。 可以作为service线程的sleep间隔，比如log roller.

默认: 10000

hbase.server.versionfile.writeattempts

退出前尝试写版本文件的次数。每次尝试由 hbase.server.thread.wakefrequency 毫秒数间隔。

默认: 3

 

hbase.hregion.memstore.flush.size

当memstore的大小超过这个值的时候，会flush到磁盘。这个值被一个线程每隔hbase.server.thread.wakefrequency检查一下。

默认:134217728

hbase.hregion.preclose.flush.size

当一个region中的memstore的大小大于这个值的时候，我们又触发了close.会先运行“pre-flush”操作，清理这个需要关闭的memstore，然后将这个region下线。当一个region下线了，我们无法再进行任何写操作。如果一个memstore很大的时候，flush操作会消耗很多时间。"pre-flush"操作意味着在region下线之前，会先把memstore清空。这样在最终执行close操作的时候，flush操作会很快。

默认: 5242880

hbase.hregion.memstore.block.multiplier

如果memstore有hbase.hregion.memstore.block.multiplier倍数的hbase.hregion.flush.size的大小，就会阻塞update操作。这是为了预防在update高峰期会导致的失控。如果不设上界，flush的时候会花很长的时间来合并或者分割，最坏的情况就是引发out of memory异常。(译者注:内存操作的速度和磁盘不匹配，需要等一等。原文似乎有误)

默认: 2

hbase.hregion.memstore.mslab.enabled

体验特性：启用memStore分配本地缓冲区。这个特性是为了防止在大量写负载的时候堆的碎片过多。这可以减少GC操作的频率。(GC有可能会Stop the world)(译者注：实现的原理相当于预分配内存，而不是每一个值都要从堆里分配)

默认: true

hbase.hregion.max.filesize

最大HStoreFile大小。若某个列族的HStoreFile增长达到这个值，这个Hegion会被切割成两个。 默认: 10G.

默认:10737418240

hbase.hstore.compactionThreshold

当一个HStore含有多于这个值的HStoreFiles(每一个memstore flush产生一个HStoreFile)的时候，会执行一个合并操作，把这HStoreFiles写成一个。这个值越大，需要合并的时间就越长。

默认: 3

hbase.hstore.blockingStoreFiles

当一个HStore含有多于这个值的HStoreFiles(每一个memstore flush产生一个HStoreFile)的时候，会执行一个合并操作，update会阻塞直到合并完成，直到超过了hbase.hstore.blockingWaitTime的值

默认: 7

hbase.hstore.blockingWaitTime

hbase.hstore.blockingStoreFiles所限制的StoreFile数量会导致update阻塞，这个时间是来限制阻塞时间的。当超过了这个时间，HRegion会停止阻塞update操作，不过合并还有没有完成。默认为90s.

默认: 90000

hbase.hstore.compaction.max

每个“小”合并的HStoreFiles最大数量。

默认: 10

hbase.hregion.majorcompaction

一个Region中的所有HStoreFile的major compactions的时间间隔。默认是1天。 设置为0就是禁用这个功能。

默认: 86400000

hbase.storescanner.parallel.seek.enable

允许 StoreFileScanner 并行搜索 StoreScanner, 一个在特定条件下降低延迟的特性。

默认: false

 

hbase.storescanner.parallel.seek.threads

并行搜索特性打开后，默认线程池大小。

默认: 10

 

hbase.mapreduce.hfileoutputformat.blocksize

MapReduce中HFileOutputFormat可以写 storefiles/hfiles. 这个值是hfile的blocksize的最小值。通常在HBase写Hfile的时候，bloocksize是由table schema(HColumnDescriptor)决定的，但是在mapreduce写的时候，我们无法获取schema中blocksize。这个值越小，你的索引就越大，你随机访问需要获取的数据就越小。如果你的cell都很小，而且你需要更快的随机访问，可以把这个值调低。

默认: 65536

hfile.block.cache.size

分配给HFile/StoreFile的block cache占最大堆(-Xmx setting)的比例。默认0.25意思是分配25%，设置为0就是禁用，但不推荐。

默认:0.25

hbase.hash.type

哈希函数使用的哈希算法。可以选择两个值:: murmur (MurmurHash) 和 jenkins (JenkinsHash). 这个哈希是给 bloom filters用的.

默认: murmur

hfile.block.index.cacheonwrite

This allows to put non-root multi-level index blocks into the block cache at the time the index is being written.

Default: false

hfile.index.block.max.size

When the size of a leaf-level, intermediate-level, or root-level index block in a multi-level block index grows to this size, the block is written out and a new block is started.

Default: 131072

hfile.format.version

The HFile format version to use for new files. Set this to 1 to test backwards-compatibility. The default value of this option should be consistent with FixedFileTrailer.MAX_VERSION.

Default: 2

io.storefile.bloom.block.size

The size in bytes of a single block ("chunk") of a compound Bloom filter. This size is approximate, because Bloom blocks can only be inserted at data block boundaries, and the number of keys per data block varies.

Default: 131072

hfile.block.bloom.cacheonwrite

Enables cache-on-write for inline blocks of a compound Bloom filter.

Default: false

hbase.rs.cacheblocksonwrite

Whether an HFile block should be added to the block cache when the block is finished.

Default: false

hbase.rpc.server.engine

Implementation of org.apache.hadoop.hbase.ipc.RpcServerEngine to be used for server RPC call marshalling.

Default: org.apache.hadoop.hbase.ipc.ProtobufRpcServerEngine

hbase.ipc.client.tcpnodelay

Set no delay on rpc socket connections. See http://docs.oracle.com/javase/1.5.0/docs/api/java/net/Socket.html#getTcpNoDelay()

Default: true

 

hbase.master.keytab.file

HMaster server验证登录使用的kerberos keytab 文件路径。(译者注：HBase使用Kerberos实现安全)

默认:

hbase.master.kerberos.principal

例如. "hbase/[email protected]". HMaster运行需要使用 kerberos principal name. principal name 可以在: user/hostname@DOMAIN 中获取. 如果 "_HOST" 被用做hostname portion，需要使用实际运行的hostname来替代它。

默认:

hbase.regionserver.keytab.file

HRegionServer验证登录使用的kerberos keytab 文件路径。

默认:

hbase.regionserver.kerberos.principal

例如. "hbase/[email protected]". HRegionServer运行需要使用 kerberos principal name. principal name 可以在: user/hostname@DOMAIN 中获取. 如果 "_HOST" 被用做hostname portion，需要使用实际运行的hostname来替代它。在这个文件中必须要有一个entry来描述 hbase.regionserver.keytab.file

默认:

hadoop.policy.file

The policy configuration file used by RPC servers to make authorization decisions on client requests. Only used when HBase security is enabled.

Default: hbase-policy.xml

hbase.superuser

List of users or groups (comma-separated), who are allowed full privileges, regardless of stored ACLs, across the cluster. Only used when HBase security is enabled.

Default:

hbase.auth.key.update.interval

The update interval for master key for authentication tokens in servers in milliseconds. Only used when HBase security is enabled.

Default: 86400000

hbase.auth.token.max.lifetime

The maximum lifetime in milliseconds after which an authentication token expires. Only used when HBase security is enabled.

Default: 604800000

 

zookeeper.session.timeout

ZooKeeper 会话超时.HBase把这个值传递改zk集群，向他推荐一个会话的最大超时时间。详见http://hadoop.apache.org/zookeeper/docs/current/zookeeperProgrammers.html#ch_zkSessions "The client sends a requested timeout, the server responds with the timeout that it can give the client. "。 单位是毫秒

默认: 180000

zookeeper.znode.parent

ZooKeeper中的HBase的根ZNode。所有的HBase的ZooKeeper会用这个目录配置相对路径。默认情况下，所有的HBase的ZooKeeper文件路径是用相对路径，所以他们会都去这个目录下面。

默认: /hbase

zookeeper.znode.rootserver

ZNode 保存的 根region的路径. 这个值是由Master来写，client和regionserver 来读的。如果设为一个相对地址，父目录就是 ${zookeeper.znode.parent}.默认情形下，意味着根region的路径存储在/hbase/root-region-server.

默认: root-region-server

zookeeper.znode.acl.parent

Root ZNode for access control lists.

Default: acl

hbase.coprocessor.region.classes

A comma-separated list of Coprocessors that are loaded by default on all tables. For any override coprocessor method, these classes will be called in order. After implementing your own Coprocessor, just put it in HBase's classpath and add the fully qualified class name here. A coprocessor can also be loaded on demand by setting HTableDescriptor.

Default:

hbase.coprocessor.master.classes

A comma-separated list of org.apache.hadoop.hbase.coprocessor.MasterObserver coprocessors that are loaded by default on the active HMaster process. For any implemented coprocessor methods, the listed classes will be called in order. After implementing your own MasterObserver, just put it in HBase's classpath and add the fully qualified class name here.

Default:

 

hbase.zookeeper.quorum

Zookeeper集群的地址列表，用逗号分割。例如："host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".默认是localhost,是给伪分布式用的。要修改才能在完全分布式的情况下使用。如果在hbase-env.sh设置了HBASE_MANAGES_ZK，这些ZooKeeper节点就会和HBase一起启动。

默认: localhost

hbase.zookeeper.peerport

ZooKeeper节点使用的端口。详细参见：http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper

默认: 2888

hbase.zookeeper.leaderport

ZooKeeper用来选择Leader的端口，详细参见：http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper

默认: 3888

hbase.zookeeper.useMulti

Instructs HBase to make use of ZooKeeper's multi-update functionality. This allows certain ZooKeeper operations to complete more quickly and prevents some issues with rare Replication failure scenarios (see the release note of HBASE-2611 for an example). IMPORTANT: only set this to true if all ZooKeeper servers in the cluster are on version 3.4+ and will not be downgraded. ZooKeeper versions before 3.4 do not support multi-update and will not fail gracefully if multi-update is invoked (see ZOOKEEPER-1495).

Default: false

 

hbase.zookeeper.property.initLimit

ZooKeeper的zoo.conf中的配置。 初始化synchronization阶段的ticks数量限制

默认: 10

hbase.zookeeper.property.syncLimit

ZooKeeper的zoo.conf中的配置。 发送一个请求到获得承认之间的ticks的数量限制

默认: 5

hbase.zookeeper.property.dataDir

ZooKeeper的zoo.conf中的配置。 快照的存储位置

默认: ${hbase.tmp.dir}/zookeeper

hbase.zookeeper.property.clientPort

ZooKeeper的zoo.conf中的配置。 客户端连接的端口

默认: 2181

hbase.zookeeper.property.maxClientCnxns

ZooKeeper的zoo.conf中的配置。 ZooKeeper集群中的单个节点接受的单个Client(以IP区分)的请求的并发数。这个值可以调高一点，防止在单机和伪分布式模式中出问题。

默认: 300

hbase.rest.port

HBase REST server的端口

默认: 8080

hbase.rest.readonly

定义REST server的运行模式。可以设置成如下的值： false: 所有的HTTP请求都是被允许的 - GET/PUT/POST/DELETE. true:只有GET请求是被允许的

默认: false

hbase.defaults.for.version.skip

Set to true to skip the 'hbase.defaults.for.version' check. Setting this to true can be useful in contexts other than the other side of a maven generation; i.e. running in an ide. You'll want to set this boolean to true to avoid seeing the RuntimException complaint: "hbase-default.xml file seems to be for and old version of HBase (\${hbase.version}), this version is X.X.X-SNAPSHOT"

Default: false

hbase.coprocessor.abortonerror

Set to true to cause the hosting server (master or regionserver) to abort if a coprocessor throws a Throwable object that is not IOException or a subclass of IOException. Setting it to true might be useful in development environments where one wants to terminate the server as soon as possible to simplify coprocessor failure analysis.

Default: false

hbase.online.schema.update.enable

Set true to enable online schema changes. This is an experimental feature. There are known issues modifying table schemas at the same time a region split is happening so your table needs to be quiescent or else you have to be running with splits disabled.

Default: false

hbase.table.lock.enable

Set to true to enable locking the table in zookeeper for schema change operations. Table locking from master prevents concurrent schema modifications to corrupt table state.

Default: true

dfs.support.append

Does HDFS allow appends to files? This is an hdfs config. set in here so the hdfs client will do append support. You must ensure that this config. is true serverside too when running hbase (You will have to restart your cluster after setting it).

Default: true

hbase.thrift.minWorkerThreads

The "core size" of the thread pool. New threads are created on every connection until this many threads are created.

Default: 16

hbase.thrift.maxWorkerThreads

The maximum size of the thread pool. When the pending request queue overflows, new threads are created until their number reaches this number. After that, the server starts dropping connections.

Default: 1000

hbase.thrift.maxQueuedRequests

The maximum number of pending Thrift connections waiting in the queue. If there are no idle threads in the pool, the server queues requests. Only when the queue overflows, new threads are added, up to hbase.thrift.maxQueuedRequests threads.

Default: 1000

hbase.offheapcache.percentage

The amount of off heap space to be allocated towards the experimental off heap cache. If you desire the cache to be disabled, simply set this value to 0.

Default: 0

hbase.data.umask.enable

Enable, if true, that file permissions should be assigned to the files written by the regionserver

Default: false

hbase.data.umask

File permissions that should be used to write data files when hbase.data.umask.enable is true

Default: 000

hbase.metrics.showTableName

Whether to include the prefix "tbl.tablename" in per-column family metrics. If true, for each metric M, per-cf metrics will be reported for tbl.T.cf.CF.M, if false, per-cf metrics will be aggregated by column-family across tables, and reported for cf.CF.M. In both cases, the aggregated metric M across tables and cfs will be reported.

Default: true

hbase.metrics.exposeOperationTimes

Whether to report metrics about time taken performing an operation on the region server. Get, Put, Delete, Increment, and Append can all have their times exposed through Hadoop metrics per CF and per region.

Default: true

hbase.master.hfilecleaner.plugins

A comma-separated list of HFileCleanerDelegate invoked by the HFileCleaner service. These HFiles cleaners are called in order, so put the cleaner that prunes the most files in front. To implement your own HFileCleanerDelegate, just put it in HBase's classpath and add the fully qualified class name here. Always add the above default log cleaners in the list as they will be overwritten in hbase-site.xml.

Default: org.apache.hadoop.hbase.master.cleaner.TimeToLiveHFileCleaner

hbase.regionserver.catalog.timeout

Timeout value for the Catalog Janitor from the regionserver to META.

Default: 600000

hbase.master.catalog.timeout

Timeout value for the Catalog Janitor from the master to META.

Default: 600000

hbase.config.read.zookeeper.config

Set to true to allow HBaseConfiguration to read the zoo.cfg file for ZooKeeper properties. Switching this to true is not recommended, since the functionality of reading ZK properties from a zoo.cfg file has been deprecated.

Default: false

hbase.snapshot.enabled

Set to true to allow snapshots to be taken / restored / cloned.

Default: true

hbase.rest.threads.max

The maximum number of threads of the REST server thread pool. Threads in the pool are reused to process REST requests. This controls the maximum number of requests processed concurrently. It may help to control the memory used by the REST server to avoid OOM issues. If the thread pool is full, incoming requests will be queued up and wait for some free threads. The default is 100.

Default: 100

hbase.rest.threads.min

The minimum number of threads of the REST server thread pool. The thread pool always has at least these number of threads so the REST server is ready to serve incoming requests. The default is 2.

Default: 2

 

2.3.2. hbase-env.sh

在这个文件里面设置HBase环境变量。比如可以配置JVM启动的堆大小或者GC的参数。你还可在这里配置HBase的参数，如Log位置，niceness(译者注:优先级)，ssh参数还有pid文件的位置等等。打开文件conf/hbase-env.sh细读其中的内容。每个选项都是有详尽的注释的。你可以在此添加自己的环境变量。

这个文件的改动系统HBase重启才能生效。

2.3.3. log4j.properties

编辑这个文件可以改变HBase的日志的级别，轮滚策略等等。

这个文件的改动系统HBase重启才能生效。 日志级别的更改会影响到HBase UI

2.3.4. 连接HBase集群的客户端配置和依赖

因为HBase的Master有可能转移，所有客户端需要访问ZooKeeper来获得现在的位置。ZooKeeper会保存这些值。因此客户端必须知道Zookeeper集群的地址，否则做不了任何事情。通常这个地址存在 hbase-site.xml 里面，客户端可以从CLASSPATH取出这个文件.

如果你是使用一个IDE来运行HBase客户端，你需要将conf/放入你的 classpath,这样 hbase-site.xml就可以找到了，(或者把hbase-site.xml放到 src/test/resources，这样测试的时候可以使用).

HBase客户端最小化的依赖是 hbase, hadoop, log4j, commons-logging, commons-lang, 和 ZooKeeper ，这些jars 需要能在 CLASSPATH 中找到。

下面是一个基本的客户端 hbase-site.xml 例子：

  
    hbase.zookeeper.quorum
    example1,example2,example3
    The directory shared by region servers.
    
  

          

2.3.4.1. Java客户端配置

Java是如何读到hbase-site.xml 的内容的

Java客户端使用的配置信息是被映射在一个HBaseConfiguration 实例中. HBaseConfiguration有一个工厂方法, HBaseConfiguration.create();,运行这个方法的时候，他会去CLASSPATH,下找hbase-site.xml，读他发现的第一个配置文件的内容。 (这个方法还会去找hbase-default.xml ; hbase.X.X.X.jar里面也会有一个an hbase-default.xml). 不使用任何hbase-site.xml文件直接通过Java代码注入配置信息也是可以的。例如，你可以用编程的方式设置ZooKeeper信息，只要这样做:

Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost");  // Here we are running zookeeper locally

如果有多ZooKeeper实例，你可以使用逗号列表。(就像在hbase-site.xml 文件中做得一样). 这个 Configuration 实例会被传递到 HTable, 之类的实例里面去.

---

2.4. 配置示例

2.4.1. 简单的分布式HBase安装

这里是一个10节点的HBase的简单示例，这里的配置都是基本的，节点名为 example0, example1... 一直到 example9 . HBase Master 和 HDFS namenode 运作在同一个节点 example0上. RegionServers 运行在节点 example1-example9. 一个 3-节点 ZooKeeper 集群运行在example1, example2, 和 example3，端口保持默认. ZooKeeper 的数据保存在目录 /export/zookeeper. 下面我们展示主要的配置文件-- hbase-site.xml, regionservers, 和 hbase-env.sh-- 这些文件可以在 conf目录找到.

2.4.1.1. hbase-site.xml

  
    hbase.zookeeper.quorum
    example1,example2,example3
    The directory shared by RegionServers.
    
  
  
    hbase.zookeeper.property.dataDir
    /export/zookeeper
    Property from ZooKeeper's config zoo.cfg.
    The directory where the snapshot is stored.
    
  
  
    hbase.rootdir
    hdfs://example0:9000/hbase
    The directory shared by RegionServers.
    
  
  
    hbase.cluster.distributed
    true
    The mode the cluster will be in. Possible values are
      false: standalone and pseudo-distributed setups with managed Zookeeper
      true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
    
  


          

2.4.1.2. regionservers

这个文件把RegionServer的节点列了下来。在这个例子里面我们让所有的节点都运行RegionServer,除了第一个节点 example1，它要运行 HBase Master 和 HDFS namenode

    example1
    example3
    example4
    example5
    example6
    example7
    example8
    example9
          

2.4.1.3. hbase-env.sh

下面我们用diff 命令来展示 hbase-env.sh 文件相比默认变化的部分. 我们把HBase的堆内存设置为4G而不是默认的1G.

    
$ git diff hbase-env.sh
diff --git a/conf/hbase-env.sh b/conf/hbase-env.sh
index e70ebc6..96f8c27 100644
--- a/conf/hbase-env.sh
+++ b/conf/hbase-env.sh
@@ -31,7 +31,7 @@ export JAVA_HOME=/usr/lib//jvm/java-6-sun/
 # export HBASE_CLASSPATH=
 
 # The maximum amount of heap to use, in MB. Default is 1000.
-# export HBASE_HEAPSIZE=1000
+export HBASE_HEAPSIZE=4096
 
 # Extra Java runtime options.
 # Below are what we set by default.  May only work with SUN JVM.

          

你可以使用 rsync 来同步 conf 文件夹到你的整个集群.

---

2.5. 重要的配置

下面我们会列举重要 的配置. 这个章节讲述必须的配置和那些值得一看的配置。(译者注:淘宝的博客也有本章节的内容，HBase性能调优，很详尽)。

2.5.1. 必须的配置

参考 Section 2.2, “操作系统” 和 Section 2.3, “Hadoop” 节.

2.5.2. 推荐配置

2.5.2.1. zookeeper.session.timeout

这个默认值是3分钟。这意味着一旦一个server宕掉了，Master至少需要3分钟才能察觉到宕机，开始恢复。你可能希望将这个超时调短，这样Master就能更快的察觉到了。在你调这个值之前，你需要确认你的JVM的GC参数，否则一个长时间的GC操作就可能导致超时。（当一个RegionServer在运行一个长时间的GC的时候，你可能想要重启并恢复它）.

要想改变这个配置，可以编辑 hbase-site.xml, 将配置部署到全部集群，然后重启。

我们之所以把这个值调的很高，是因为我们不想一天到晚在论坛里回答新手的问题。“为什么我在执行一个大规模数据导入的时候Region Server死掉啦”，通常这样的问题是因为长时间的GC操作引起的，他们的JVM没有调优。我们是这样想的，如果一个人对HBase不很熟悉，不能期望他知道所有，打击他的自信心。等到他逐渐熟悉了，他就可以自己调这个参数了。

2.5.2.2.  ZooKeeper 实例个数

参考 Section 2.5, “ZooKeeper”.

2.5.2.3. hbase.regionserver.handler.count

这个设置决定了处理用户请求的线程数量。默认是10，这个值设的比较小，主要是为了预防用户用一个比较大的写缓冲，然后还有很多客户端并发，这样region servers会垮掉。有经验的做法是，当请求内容很大(上MB，如大puts, 使用缓存的scans)的时候，把这个值放低。请求内容较小的时候(gets, 小puts, ICVs, deletes)，把这个值放大。

当客户端的请求内容很小的时候，把这个值设置的和最大客户端数量一样是很安全的。一个典型的例子就是一个给网站服务的集群，put操作一般不会缓冲,绝大多数的操作是get操作。

把这个值放大的危险之处在于，把所有的Put操作缓冲意味着对内存有很大的压力，甚至会导致OutOfMemory.一个运行在内存不足的机器的RegionServer会频繁的触发GC操作，渐渐就能感受到停顿。(因为所有请求内容所占用的内存不管GC执行几遍也是不能回收的)。一段时间后，集群也会受到影响，因为所有的指向这个region的请求都会变慢。这样就会拖累集群，加剧了这个问题。

你可能会对handler太多或太少有感觉，可以通过 Section 12.2.2.1, “启用 RPC级 日志” ，在单个RegionServer启动log并查看log末尾 (请求队列消耗内存)。

2.5.2.4. 大内存机器的配置

HBase有一个合理的保守的配置，这样可以运作在所有的机器上。如果你有台大内存的集群-HBase有8G或者更大的heap,接下来的配置可能会帮助你. TODO.

2.5.2.5. 压缩

应该考虑启用ColumnFamily 压缩。有好几个选项，通过降低存储文件大小以降低IO，降低消耗且大多情况下提高性能。

参考 Appendix C,  HBase压缩  获取更多信息.

2.5.2.6. 较大 Regions

更大的Region可以使你集群上的Region的总数量较少。 一般来言，更少的Region可以使你的集群运行更加流畅。(你可以自己随时手工将大Region切割，这样单个热点Region就会被分布在集群的更多节点上)。

较少的Region较好。一般每个RegionServer在20到小几百之间。 调整Region大小以适合该数字。

 

0.90.x 版本, 默认情况下单个Region是256MB。Region 大小的上界是 4Gb. 0.92.x 版本, 由于 HFile v2 已经将Region大小支持得大很多， (如, 20Gb).

可能需要实验，基于硬件和应用需要进行配置。

可以调整hbase-site.xml中的 hbase.hregion.max.filesize属性. RegionSize 也可以基于每个表设置：  HTableDescriptor.

2.5.2.7. 管理 Splitting

除了让HBase自动切割你的Region,你也可以手动切割。 ^[12] 随着数据量的增大，splite会被持续执行。如果你需要知道你现在有几个region,比如长时间的debug或者做调优，你需要手动切割。通过跟踪日志来了解region级的问题是很难的，因为他在不停的切割和重命名。data offlineing bug和未知量的region会让你没有办法。如果一个 HLog 或者 StoreFile由于一个奇怪的bug，HBase没有执行它。等到一天之后，你才发现这个问题，你可以确保现在的regions和那个时候的一样，这样你就可以restore或者replay这些数据。你还可以调优你的合并算法。如果数据是均匀的，随着数据增长，很容易导致split / compaction疯狂的运行。因为所有的region都是差不多大的。用手的切割，你就可以交错执行定时的合并和切割操作，降低IO负载。

为什么我关闭自动split呢？因为自动的splite是配置文件中的 hbase.hregion.max.filesize决定的. 你把它设置成Long.MAX_VALUE是不推荐的做法，要是你忘记了手工切割怎么办.推荐的做法是设置成100GB，一旦到达这样的值，至少需要一个小时执行 major compactions。

那什么是最佳的在pre-splite regions的数量呢。这个决定于你的应用程序了。你可以先从低的开始，比如每个server10个pre-splite regions.然后花时间观察数据增长。有太少的region至少比出错好，你可以之后再rolling split.一个更复杂的答案是这个值是取决于你的region中的最大的storefile。随着数据的增大，这个也会跟着增大。 你可以当这个文件足够大的时候，用一个定时的操作使用Store的合并选择算法(compact selection algorithm)来仅合并这一个HStore。如果你不这样做，这个算法会启动一个 major compactions，很多region会受到影响，你的集群会疯狂的运行。需要注意的是，这样的疯狂合并操作是数据增长造成的，而不是手动分割操作决定的。

如果你 pre-split 导致 regions 很小,你可以通过配置HConstants.MAJOR_COMPACTION_PERIOD把你的major compaction参数调大

如果你的数据变得太大，可以使用org.apache.hadoop.hbase.util.RegionSplitter 脚本来执行针对全部集群的一个网络IO安全的rolling split操作。

 

2.5.2.8. 管理 Compactions

通常管理技术是手动管理主紧缩(major compactions)， 而不是让HBase 来做。 缺省HConstants.MAJOR_COMPACTION_PERIOD 是一天。主紧缩可能强行进行，在你并不太希望发生的时候——特别是在一个繁忙系统。关闭自动主紧缩，设置该值为0.

重点强调，主紧缩对存储文件(StoreFile)清理是绝对必要的。唯一变量是发生的时间。可以通过HBase shell进行管理，或通过 HBaseAdmin.

更多信息关于紧缩和紧缩文件选择过程，参考 Section 9.7.5.5, “紧缩”

2.5.2.9.  预测执行 (Speculative Execution)

MapReduce任务的预测执行缺省是打开的，HBase集群一般建议在系统级关闭预测执行，除非在某种特殊情况下需要打开，此时可以每任务配置。设置mapred.map.tasks.speculative.execution 和 mapred.reduce.tasks.speculative.execution 为 false.

2.5.3. 其他配置

2.5.3.1. 负载均衡

负载均衡器(LoadBalancer)是在主服务器上运行的定期操作，以重新分布集群区域。通过hbase.balancer.period 设置，缺省值300000 (5 分钟).

参考 Section 9.5.4.1, “负载均衡” 获取关于负载均衡器( LoadBalancer )的更多信息。

2.5.3.2. 禁止块缓存(Blockcache)

不要关闭块缓存 (通过hbase.block.cache.size 为 0 来设置)。当前如果关闭块缓存会很不好，因为区域服务器会花很多时间不停加载hfile指数。如果工作集如此配置块缓存没有好处，最少应保证hfile指数保存在块缓存内的大小(可以通过查询区域服务器的UI，得到大致的数值。可以看到网页的上方有块指数值统计).

2.5.3.3. Nagle算法 或小包问题

如果操作HBase时看到大量40ms左右的偶然延时，尝试Nagles配置。如，参考用户邮件列表线索, Inconsistent scan performance with caching set to 1 ，该议题在其中启用tcpNoDelay (译者注，本英文原文notcpdelay有误)提高了扫描速度。你也可以查看该文档的尾部图表：HBASE-7008 Set scanner caching to a better default  (xie liang)，我们的Lars Hofhansl 尝试了各种不同的数据大小，Nagle打开或关闭的测量结果。

---

 

[1] Be careful editing XML. Make sure you close all elements. Run your file through xmllint or similar to ensure well-formedness of your document after an edit session.

[2] The hadoop-dns-checker tool can be used to verify DNS is working correctly on the cluster. The project README file provides detailed instructions on usage.

[3] 参考 Jack Levin's major hdfs issues note up on the user list.

[4] The requirement that a database requires upping of system limits is not peculiar to HBase. 参考 for example the section Setting Shell Limits for the Oracle User inShort Guide to install Oracle 10 on Linux.

[5] A useful read setting config on you hadoop cluster is Aaron Kimballs' Configuration Parameters: What can you just ignore?

[ 6] 

[6] The Cloudera blog post An update on Apache Hadoop 1.0 by Charles Zedlweski has a nice exposition on how all the Hadoop versions relate. Its worth checking out if you are having trouble making sense of the Hadoop version morass.

[7] Until recently only the branch-0.20-append branch had a working sync but no official release was ever made from this branch. You had to build it yourself. Michael Noll wrote a detailed blog, Building an Hadoop 0.20.x version for HBase 0.90.2, on how to build an Hadoop from branch-0.20-append. Recommended.

[8] Praveen Kumar has written a complimentary article, Building Hadoop and HBase for HBase Maven application development.

[ 9] dfs.support.append

[10] 参考 Hadoop HDFS: Deceived by Xciever for an informative rant on xceivering.

[11] The pseudo-distributed vs fully-distributed nomenclature comes from Hadoop.

[12] 参考 Section 2.4.2.1.2, “Pseudo-distributed Extras” for notes on how to start extra Masters and RegionServers when running pseudo-distributed.

[13] 对 ZooKeeper 全部配置，参考ZooKeeper 的zoo.cfg. HBase 没有包含 zoo.cfg ，所以需要浏览合适的独立ZooKeeper下载版本的 conf 目录找到。

[14] What follows is taken from the javadoc at the head of the org.apache.hadoop.hbase.util.RegionSplitter tool added to HBase post-0.90.0 release.

 

Chapter 3. 升级

不能跳过主要版本升级。如果想从0.20.x 升级到 0.92.x，必须从0.20.x 升级到 0.90.x ，再从0.90.x 升级到 0.92.x.

参见 Section 2, “配置”, 需要特别注意有关Hadoop 版本的信息.

3.1. 从 0.94.x 升级到 0.96.x

The Singularity

You will have to stop your old 0.94 cluster completely to upgrade. If you are replicating between clusters, both clusters will have to go down to upgrade. Make sure it is a clean shutdown so there are no WAL files laying around (TODO: Can 0.96 read 0.94 WAL files?). Make sure zookeeper is cleared of state. All clients must be upgraded to 0.96 too.

The API has changed in a few areas; in particular how you use coprocessors (TODO: MapReduce too?)

3.2. 从 0.92.x 升级到 0.94.x

0.92 和 0.94 接口兼容，可平滑升级。

 

3.3. 从 0.90.x 到 0.92.x 升级

升级指引

You will find that 0.92.0 runs a little differently to 0.90.x releases. Here are a few things to watch out for upgrading from 0.90.x to 0.92.0.

 

If you've not patience, here are the important things to know upgrading.

1. Once you upgrade, you can’t go back.
2. MSLAB is on by default. Watch that heap usage if you have a lot of regions.
3. Distributed splitting is on by defaul. It should make region server failover faster.
4. There’s a separate tarball for security.
5. If -XX:MaxDirectMemorySize is set in your hbase-env.sh, it’s going to enable the experimental off-heap cache (You may not want this).

3.3.1. 不可回退!

To move to 0.92.0, all you need to do is shutdown your cluster, replace your hbase 0.90.x with hbase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and restart (You cannot do a rolling restart from 0.90.x to 0.92.x -- you must restart). On startup, the .META. table content is rewritten removing the table schema from the info:regioninfo column. Also, any flushes done post first startup will write out data in the new 0.92.0 file format, HFile V2. This means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase data directory.

3.3.2. MSLAB 缺省启用

In 0.92.0, the hbase.hregion.memstore.mslab.enabled flag is set to true (参考 Section 11.3.1.1, “Long GC pauses”). In 0.90.x it was false. When it is enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the memstore has zero or just a few small elements. This is fine usually but if you had lots of regions per regionserver in a 0.90.x cluster (and MSLAB was off), you may find yourself OOME'ing on upgrade because the thousands of regions * number of column families * 2MB MSLAB (at a minimum) puts your heap over the top. Set hbase.hregion.memstore.mslab.enabled to false or set the MSLAB size down from 2MB by setting hbase.hregion.memstore.mslab.chunksize to something less.

3.3.3. 分布式分割缺省启用

Previous, WAL logs on crash were split by the Master alone. In 0.92.0, log splitting is done by the cluster (参考 “HBASE-1364 [performance] Distributed splitting of regionserver commit logs”). This should cut down significantly on the amount of time it takes splitting logs and getting regions back online again.

3.3.4. 内存计算改变

In 0.92.0, Appendix E, HFile format version 2 indices and bloom filters take up residence in the same LRU used caching blocks that come from the filesystem. In 0.90.x, the HFile v1 indices lived outside of the LRU so they took up space even if the index was on a ‘cold’ file, one that wasn’t being actively used. With the indices now in the LRU, you may find you have less space for block caching. Adjust your block cache accordingly. 参考 the Section 9.6.4, “Block Cache” for more detail. The block size default size has been changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25.

3.3.5.  可用 Hadoop 版本

Run 0.92.0 on Hadoop 1.0.x (or CDH3u3 when it ships). The performance benefits are worth making the move. Otherwise, our Hadoop prescription is as it has been; you need an Hadoop that supports a working sync. 参考 Section 2.3, “Hadoop”.

If running on Hadoop 1.0.x (or CDH3u3), enable local read. 参考 Practical Caching presentation for ruminations on the performance benefits ‘going local’ (and for how to enable local reads).

3.3.6. HBase 0.92.0 带 ZooKeeper 3.4.2

If you can, upgrade your zookeeper. If you can’t, 3.4.2 clients should work against 3.3.X ensembles (HBase makes use of 3.4.2 API).

3.3.7. 在线切换缺省关闭

In 0.92.0, we’ve added an experimental online schema alter facility (参考 hbase.online.schema.update.enable). Its off by default. Enable it at your own risk. Online alter and splitting tables do not play well together so be sure your cluster quiescent using this feature (for now).

3.3.8. WebUI

The webui has had a few additions made in 0.92.0. It now shows a list of the regions currently transitioning, recent compactions/flushes, and a process list of running processes (usually empty if all is well and requests are being handled promptly). Other additions including requests by region, a debugging servlet dump, etc.

3.3.9. 安全 tarball

我们发布两个tarball： 安全和非安全 HBase. 如何设置安全HBase的文档正在制定中。

3.3.10. 试验离堆(off-heap)缓存

(译者注：on-heap和off-heap是Terracotta 公司提出的概念。on-heap指java对象在GC内存储管理，效率较高，但GC只能管理2G内存，有时成为性能瓶颈。off-heap又叫BigMemory ，是JVM的GC机制的替代，在GC外存储，100倍速于DiskStore，cache量目前(2012年底)达到350GB)

A new cache was contributed to 0.92.0 to act as a solution between using the “on-heap” cache which is the current LRU cache the region servers have and the operating system cache which is out of our control. To enable, set “-XX:MaxDirectMemorySize” in hbase-env.sh to the value for maximum direct memory size and specify hbase.offheapcache.percentage in hbase-site.xml with the percentage that you want to dedicate to off-heap cache. This should only be set for servers and not for clients. Use at your own risk. See this blog post for additional information on this new experimental feature: http://www.cloudera.com/blog/2012/01/caching-in-hbase-slabcache/

3.3.11. HBase 复制的变动

0.92.0 adds two new features: multi-slave and multi-master replication. The way to enable this is the same as adding a new peer, so in order to have multi-master you would just run add_peer for each cluster that acts as a master to the other slave clusters. Collisions are handled at the timestamp level which may or may not be what you want, this needs to be evaluated on a per use case basis. Replication is still experimental in 0.92 and is disabled by default, run it at your own risk.

3.3.12. 对OOME ，RegionServer 现在退出

If an OOME, we now have the JVM kill -9 the regionserver process so it goes down fast. Previous, a RegionServer might stick around after incurring an OOME limping along in some wounded state. To disable this facility, and recommend you leave it in place, you’d need to edit the bin/hbase file. Look for the addition of the -XX:OnOutOfMemoryError="kill -9 %p" arguments (参考 [HBASE-4769] - ‘Abort RegionServer Immediately on OOME’)

3.3.13. HFile V2 和 “更大, 更少” 趋势

0.92.0 stores data in a new format, Appendix E, HFile format version 2. As HBase runs, it will move all your data from HFile v1 to HFile v2 format. This auto-migration will run in the background as flushes and compactions run. HFile V2 allows HBase run with larger regions/files. In fact, we encourage that all HBasers going forward tend toward Facebook axiom #1, run with larger, fewer regions. If you have lots of regions now -- more than 100s per host -- you should look into setting your region size up after you move to 0.92.0 (In 0.92.0, default size is not 1G, up from 256M), and then running online merge tool (参考 “HBASE-1621 merge tool should work on online cluster, but disabled table”).

 

3.4. 从HBase 0.20.x or 0.89.x 升级到 HBase 0.90.x

0.90.x 版本的HBase可以在 HBase 0.20.x 或者 HBase 0.89.x的数据上启动. 不需要转换数据文件， HBase 0.89.x 和 0.90.x 的region目录名是不一样的 -- 老版本用md5 hash 而不是jenkins hash 来命名region-- 这就意味着，一旦启动，再也不能回退到 HBase 0.20.x.

在升级的时候，一定要将hbase-default.xml 从你的 conf目录删掉。 0.20.x 版本的配置对于 0.90.x HBase不是最佳的. hbase-default.xml 现在已经被打包在 HBase jar 里面了. 如果你想看看这个文件内容，你可以在src目录下 src/main/resources/hbase-default.xml 或者在 Section 2.31.1, “HBase 默认配置”看到.

最后，如果从0.20.x升级，需要在shell里检查 .META. schema . 过去，我们推荐用户使用16KB的 MEMSTORE_FLUSHSIZE. 在shell中运行 hbase> scan '-ROOT-'. 会显示当前的.META. schema. 检查 MEMSTORE_FLUSHSIZE 的大小. 看看是不是 16KB (16384)? 如果是的话，你需要修改它(默认的值是 64MB (67108864)) 运行脚本 bin/set_meta_memstore_size.rb. 这个脚本会修改 .META. schema. 如果不运行的话，集群会比较慢^[15] .

---

 

[ 15] 参考  HBASE-3499 Users upgrading to 0.90.0 need to have their .META. table updated with the right MEMSTORE_SIZE

Chapter 4.  HBase Shell

HBase Shell is 在(J)Ruby的IRB的基础上加上了HBase的命令。任何你可以在IRB里做的事情都可在在HBase Shell中做。

你可以这样来运行HBase Shell:

$ ./bin/hbase shell

输入 help 就会返回Shell的命令列表和选项。可以看看在Help文档尾部的关于如何输入变量和选项。尤其要注意的是表名，行，列名必须要加引号。

参见 Section 1.2.3, “Shell 练习”可以看到Shell的基本使用例子。

4.1. 使用脚本

如果要使用脚本，可以看HBase的bin 目录.在里面找到后缀为 *.rb的脚本.要想运行这个脚本，要这样

$ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT

就可以了

4.2. Shell 技巧

4.2.1. irbrc

可以在你自己的Home目录下创建一个.irbrc文件. 在这个文件里加入自定义的命令。有一个有用的命令就是记录命令历史，这样你就可以把你的命令保存起来。

                        $ more .irbrc
                        require 'irb/ext/save-history'
                        IRB.conf[:SAVE_HISTORY] = 100
                        IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history"

可以参见 ruby 关于 .irbrc 的文档来学习更多的关于IRB的配置方法。

4.2.2. LOG 时间转换

可以将日期'08/08/16 20:56:29'从hbase log 转换成一个 timestamp, 操作如下:

                    hbase(main):021:0> import java.text.SimpleDateFormat
                    hbase(main):022:0> import java.text.ParsePosition
                    hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16 20:56:29", ParsePosition.new(0)).getTime() => 1218920189000

也可以逆过来操作。

                    hbase(main):021:0> import java.util.Date
                    hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC 2008"

要想把日期格式和HBase log格式完全相同，可以参见文档 SimpleDateFormat.

4.2.3. 调试

4.2.3.1. Shell 切换成debug 模式

你可以将shell切换成debug模式。这样可以看到更多的信息。 -- 例如可以看到命令异常的stack trace:

hbase> debug 

4.2.3.2. DEBUG log level

想要在shell中看到 DEBUG 级别的 logging ，可以在启动的时候加上 -d 参数.

$ ./bin/hbase shell -d

Chapter 5. 数据模型

简单来说，应用程序是以表的方式在HBase存储数据的。表是由行和列构成的，所有的列是从属于某一个列族的。行和列的交叉点称之为cell,cell是版本化的。cell的内容是不可分割的字节数组。

表的行键也是一段字节数组，所以任何东西都可以保存进去，不论是字符串或者数字。HBase的表是按key排序的，排序方式之针对字节的。所有的表都必须要有主键-key.

5.1. 概念视图

下面是根据BigTable 论文稍加修改的例子。 有一个名为webtable的表，包含两个列族：contents和anchor.在这个例子里面，anchor有两个列 (anchor:cssnsi.com, anchor:my.look.ca)，contents仅有一列(contents:html)

列名

一个列名是由它的列族前缀和修饰符(qualifier)连接而成。例如列contents:html是列族 contents加冒号(:)加 修饰符 html组成的。

Table 5.1. 表 webtable

Row Key	Time Stamp	ColumnFamily contents	ColumnFamily anchor
"com.cnn.www"	t9		anchor:cnnsi.com = "CNN"
"com.cnn.www"	t8		anchor:my.look.ca = "CNN.com"
"com.cnn.www"	t6	contents:html = "..."
"com.cnn.www"	t5	contents:html = "..."
"com.cnn.www"	t3	contents:html = "..."

5.2. 物理视图

尽管在概念视图里，表可以被看成是一个稀疏的行的集合。但在物理上，它的是区分列族 存储的。新的columns可以不经过声明直接加入一个列族.

Table 5.2. ColumnFamily anchor

Row Key	Time Stamp	Column Family anchor
"com.cnn.www"	t9	anchor:cnnsi.com = "CNN"
"com.cnn.www"	t8	anchor:my.look.ca = "CNN.com"

Table 5.3. ColumnFamily contents

Row Key	Time Stamp	ColumnFamily "contents:"
"com.cnn.www"	t6	contents:html = "..."
"com.cnn.www"	t5	contents:html = "..."
"com.cnn.www"	t3	contents:html = "..."

值得注意的是在上面的概念视图中空白cell在物理上是不存储的，因为根本没有必要存储。因此若一个请求为要获取t8时间的contents:html，他的结果就是空。相似的，若请求为获取t9时间的anchor:my.look.ca，结果也是空。但是，如果不指明时间，将会返回最新时间的行，每个最新的都会返回。例如，如果请求为获取行键为"com.cnn.www"，没有指明时间戳的话，活动的结果是t6下的contents:html，t9下的anchor:cnnsi.com和t8下anchor:my.look.ca。

For more information about the internals of how HBase stores data, see Section 9.7, “Regions”.

5.3. 表

表是在schema声明的时候定义的。

5.4. 行

行键是不可分割的字节数组。行是按字典排序由低到高存储在表中的。一个空的数组是用来标识表空间的起始或者结尾。

5.5. 列族

在HBase是列族一些列的集合。一个列族所有列成员是有着相同的前缀。比如，列courses:history 和 courses:math都是 列族 courses的成员.冒号(:)是列族的分隔符，用来区分前缀和列名。column 前缀必须是可打印的字符，剩下的部分(称为qualify),可以又任意字节数组组成。列族必须在表建立的时候声明。column就不需要了，随时可以新建。

在物理上，一个的列族成员在文件系统上都是存储在一起。因为存储优化都是针对列族级别的，这就意味着，一个colimn family的所有成员的是用相同的方式访问的。

5.6. Cells

A {row, column, version} 元组就是一个HBase中的一个 cell。Cell的内容是不可分割的字节数组。

5.7. 数据模型操作

四个主要的数据模型操作是 Get, Put, Scan, 和 Delete. 通过 HTable 实例进行操作.

5.7.1. Get

Get 返回特定行的属性。 Gets 通过 HTable.get 执行。

5.7.2. Put

Put 要么向表增加新行 (如果key是新的) 或更新行 (如果key已经存在)。 Puts 通过 HTable.put (writeBuffer) 或 HTable.batch (non-writeBuffer)执行。

5.7.3. Scans

Scan 允许多行特定属性迭代。

下面是一个在 HTable 表实例上的示例。 假设表有几行键值为 "row1", "row2", "row3", 还有一些行有键值 "abc1", "abc2", 和 "abc3". 下面的示例展示startRow 和 stopRow 可以应用到一个Scan 实例，以返回"row"打头的行。

HTable htable = ...      // instantiate HTable
    
Scan scan = new Scan();
scan.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("attr"));
scan.setStartRow( Bytes.toBytes("row"));                   // start key is inclusive
scan.setStopRow( Bytes.toBytes("row" +  (char)0));  // stop key is exclusive
ResultScanner rs = htable.getScanner(scan);
try {
  for (Result r = rs.next(); r != null; r = rs.next()) {
  // process result...
} finally {
  rs.close();  // always close the ResultScanner!
}

5.7.4. Delete

Delete 从表中删除一行. 删除通过HTable.delete 执行。

HBase 没有修改数据的合适方法。所以通过创建名为墓碑(tombstones)的新标志进行处理。这些墓碑和死去的值，在主紧缩时清除。

参考 Section 5.8.1.5, “Delete” 获取删除列版本的更多信息。参考Section 9.7.5.5, “Compaction” 获取更多有关紧缩的信息。

5.8. 版本

一个 {row, column, version} 元组是HBase中的一个单元(cell).但是有可能会有很多的单元的行和列是相同的，可以使用版本来区分不同的单元.

rows和column key是用字节数组表示的，version则是用一个长整型表示。这个long的值使用 java.util.Date.getTime() 或者 System.currentTimeMillis()产生的。这就意味着他的含义是“当前时间和1970-01-01 UTC的时间差，单位毫秒。”

在HBase中，版本是按倒序排列的，因此当读取这个文件的时候，最先找到的是最近的版本。

有些人不是很理解HBase单元(cell)的意思。一个常见的问题是:

• 如果有多个包含版本写操作同时发起，HBase会保存全部还是会保持最新的一个？^[16]

• 可以发起包含版本的写操作，但是他们的版本顺序和操作顺序相反吗?^[17]

下面我们介绍下在HBase中版本是如何工作的。^[18].

5.8.1. HBase的操作(包含版本操作)

在这一章我们来仔细看看在HBase的各个主要操作中版本起到了什么作用。

5.8.1.1. Get/Scan

Gets实在Scan的基础上实现的。可以详细参见下面的讨论 Get 同样可以用 Scan来描述.

默认情况下，如果你没有指定版本，当你使用Get操作的时候，会返回最近版本的Cell(该Cell可能是最新写入的，但不能保证)。默认的操作可以这样修改:

• 如果想要返回返回两个以上的把版本,参见Get.setMaxVersions()

• 如果想要返回的版本不只是最近的，参见 Get.setTimeRange()

  要向查询的最新版本要小于或等于给定的这个值，这就意味着给定的'最近'的值可以是某一个时间点。可以使用0到你想要的时间来设置，还要把max versions设置为1.

5.8.1.2. 默认 Get 例子

下面的Get操作会只获得最新的一个版本。

        Get get = new Get(Bytes.toBytes("row1"));
        Result r = htable.get(get);
        byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr"));  // returns current version of value          

5.8.1.3. 含有的版本的Get例子

下面的Get操作会获得最近的3个版本。

        Get get = new Get(Bytes.toBytes("row1"));
        get.setMaxVersions(3);  // will return last 3 versions of row
        Result r = htable.get(get);
        byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr"));  // returns current version of value
        List kv = r.getColumn(Bytes.toBytes("cf"), Bytes.toBytes("attr"));  // returns all versions of this column       
                    

5.8.1.4. Put

一个Put操作会给一个cell,创建一个版本，默认使用当前时间戳，当然你也可以自己设置时间戳。这就意味着你可以把时间设置在过去或者未来，或者随意使用一个Long值。

要想覆盖一个现有的值，就意味着你的row,column和版本必须完全相等。

5.8.1.4.1. 不指明版本的例子

下面的Put操作不指明版本，所以HBase会用当前时间作为版本。

          Put put = new Put(Bytes.toBytes(row));
          put.add(Bytes.toBytes("cf"), Bytes.toBytes("attr1"), Bytes.toBytes( data));
          htable.put(put);
                    

5.8.1.4.2. 指明版本的例子

下面的Put操作，指明了版本。

          Put put = new Put( Bytes.toBytes(row ));
          long explicitTimeInMs = 555;  // just an example
          put.add(Bytes.toBytes("cf"), Bytes.toBytes("attr1"), explicitTimeInMs, Bytes.toBytes(data));
          htable.put(put);
                    

5.8.1.5. Delete

有三种不同类型的内部删除标记  [19]:

• Delete: 删除列的指定版本.

• Delete column: 删除列的所有版本.

• Delete family: 删除特定列族所有列

当删除一行，HBase将内部对每个列族创建墓碑(非每个单独列)。

删除操作的实现是创建一个墓碑标记。例如，我们想要删除一个版本，或者默认是currentTimeMillis。就意味着“删除比这个版本更早的所有版本”.HBase不会去改那些数据，数据不会立即从文件中删除。他使用删除标记来屏蔽掉这些值。^[20]若你知道的版本比数据中的版本晚，就意味着这一行中的所有数据都会被删除。

参考 Section 9.7.5.4, “KeyValue” 获取内部 KeyValue 格式更多信息。

5.8.2. 现有的限制

关于版本还有一些bug(或者称之为未实现的功能)，计划在下个版本实现。

5.8.2.1. 删除标记误标新Put 的数据

删除标记操作可能会标记其后put的数据。^[21]记住，当写下一个墓碑标记后，只有下一个主紧缩操作发起之后，墓碑才会清除。假设你删除所有<= 时间T的数据。但之后，你又执行了一个Put操作，时间戳<= T。就算这个Put发生在删除操作之后，他的数据也打上了墓碑标记。这个Put并不会失败，但你做Get操作时，会注意到Put没有产生影响。只有一个主紧缩执行后，一切才会恢复正常。如果你的Put操作一直使用升序的版本，这个问题不会有影响。但是即使你不关心时间，也可能出现该情况。只需删除和插入迅速相互跟随，就有机会在同一毫秒中遇到。

5.8.2.2. 主紧缩改变查询的结果

“设想一下，你一个cell有三个版本t1,t2和t3。你的maximun-version设置是2.当你请求获取全部版本的时候，只会返回两个，t2和t3。如果你将t2和t3删除，就会返回t1。但是如果在删除之前，发生了major compaction操作，那么什么值都不好返回了。^[22]”

5.9. 排序

所有数据模型操作 HBase 返回排序的数据。先是行，再是列族，然后是列修饰(column qualifier), 最后是时间戳(反向排序,所以最新的在前).

5.10. 列的元数据

对列族，没有内部的KeyValue之外的元数据保存。这样，HBase不仅在一行中支持很多列，而且支持行之间不同的列。 由你自己负责跟踪列名。

唯一获取列族的完整列名的方法是处理所有行。HBase内部保存数据更多信息，请参考 Section 9.7.5.4, “KeyValue”.

5.11. 联合查询(Join)

HBase是否支持联合是一个网上常问问题。简单来说 : 不支持。至少不想传统RDBMS那样支持(如 SQL中带 equi-joins 或 outer-joins). 正如本章描述的，读数据模型是 Get 和 Scan.

但并不表示等价联合不能在应用程序中支持，只是必须自己做。 两种方法，要么指示要写到HBase的数据，要么查询表并在应用或MapReduce代码中做联合(如 RDBMS所展示,有几种步骤来实现，依赖于表的大小。如 nested loops vs. hash-joins). 哪个更好？依赖于你准备做什么，所以没有一个单一的回答适合所有方面。

5.12. ACID

参考  ACID Semantics. Lars Hofhansl 也在  ACID in HBase上写了说明.

 

^[16] 目前，只有最新的那个是可以获取到的。.

^[17] 可以

^[18] 参考 HBASE-2406 for discussion of HBase versions. Bending time in HBase makes for a good read on the version, or time, dimension in HBase. It has more detail on versioning than is provided here. As of this writing, the limiitation Overwriting values at existing timestamps mentioned in the article no longer holds in HBase. This section is basically a synopsis of this article by Bruno Dumon.

^{[19] 参考 Lars Hofhansl's blog for discussion of his attempt adding another, Scanning in HBase: Prefix Delete Marker}

^[20] 当HBase执行一次major compaction,标记删除的数据会被实际的删除，删除标记也会被删除。

^[21] HBASE-2256

^[22] 参考垃圾收集： Bending time in HBase

 

Chapter 6. HBase 的 Schema 设计

一份关于各种NSQL数据库的优点和缺点的通用介绍，就是 Ian Varley的博士论文， No Relation: The Mixed Blessings of Non-Relational Databases。 推荐。也可阅读 Section 9.7.5.4, “KeyValue” ，了解HBase如何内部保存数据。

6.1.  模式(Schema) 创建

可以使用Chapter 4, HBase Shell 或Java API的HBaseAdmin来创建和编辑HBase的模式。

表必须禁用以修改列族，如：

Configuration config = HBaseConfiguration.create();  
HBaseAdmin admin = new HBaseAdmin(conf);    
String table = "myTable";

admin.disableTable(table);           

HColumnDescriptor cf1 = ...;
admin.addColumn(table, cf1);      // adding new ColumnFamily
HColumnDescriptor cf2 = ...;
admin.modifyColumn(table, cf2);    // modifying existing ColumnFamily

admin.enableTable(table);                
      

参考  Section 2.3.4, “Client configuration and dependencies connecting to an HBase cluster” ，获取更多配置客户端连接的信息。

注意: 0.92.x 支持在线修改模式, 但 0.90.x 需要禁用表。

6.1.1. 模式更新

当表或列族改变时(如 region size, block size), 当下次存在主紧缩及存储文件重写时起作用。

参考 Section 9.7.5, “Store” 获取存储文件的更多信息。

6.2.  列族的数量

现在HBase并不能很好的处理两个或者三个以上的列族，所以尽量让你的列族数量少一些。目前，flush和compaction操作是针对一个Region。所以当一个列族操作大量数据的时候会引发一个flush。那些不相关的列族也有进行flush操作，尽管他们没有操作多少数据。Compaction操作现在是根据一个列族下的全部文件的数量触发的，而不是根据文件大小触发的。当很多的列族在flush和compaction时,会造成很多没用的I/O负载(要想解决这个问题，需要将flush和compaction操作只针对一个列族) 。 更多紧缩信息, 参考Section 9.7.5.5, “Compaction”.

尽量在你的应用中使用一个列族。只有你的所有查询操作只访问一个列族的时候，可以引入第二个和第三个列族.例如，你有两个列族,但你查询的时候总是访问其中的一个，从来不会两个一起访问。

6.2.1. 列族的基数

一个表存在多列族，注意基数(如, 行数). 如果列族A有100万行，列族B有10亿行，列族A可能被分散到很多很多区(及区服务器)。这导致扫描列族A低效。

 

6.3.  行键(RowKey)设计

6.3.1. 单调递增行键/时序数据

在Tom White的Hadoop: The Definitive Guide一书中，有一个章节描述了一个值得注意的问题：在一个集群中，一个导入数据的进程一动不动，所有的client都在等待一个region(就是一个节点)，过了一会后，变成了下一个region...如果使用了单调递增或者时序的key就会造成这样的问题。详情可以参见IKai画的漫画monotonically increasing values are bad。使用了顺序的key会将本没有顺序的数据变得有顺序，把负载压在一台机器上。所以要尽量避免时间戳或者(e.g. 1, 2, 3)这样的key。

如果你需要导入时间顺序的文件(如log)到HBase中，可以学习OpenTSDB的做法。他有一个页面来描述他的schema.OpenTSDB的Key的格式是[metric_type][event_timestamp]，乍一看，似乎违背了不将timestamp做key的建议，但是他并没有将timestamp作为key的一个关键位置，有成百上千的metric_type就足够将压力分散到各个region了。

6.3.2. 尽量最小化行和列的大小(为何我的存储文件指示很大？)

在HBase中，值是作为一个单元(Cell)保存在系统的中的，要定位一个单元，需要行，列名和时间戳。通常情况下，如果你的行和列的名字要是太大(甚至比value的大小还要大)的话，你可能会遇到一些有趣的情况。例如Marc Limotte 在 HBASE-3551(推荐!)尾部提到的现象。在HBase的存储文件Section 9.7.5.2, “StoreFile (HFile)”中，有一个索引用来方便值的随机访问，但是访问一个单元的坐标要是太大的话，会占用很大的内存，这个索引会被用尽。所以要想解决，可以设置一个更大的块大小，当然也可以使用更小的列名 。压缩也能得到更大指数。参考话题 a question storefileIndexSize 用户邮件列表.

大部分时候，小的低效不会影响很大。不幸的是，这里会是个问题。无论是列族，属性和行键都会在数据中重复上亿次。参考 Section 9.7.5.4, “KeyValue” 获取更多信息，关于HBase 内部保存数据，了解为什么这很重要。

6.3.2.1. 列族

尽量使列族名小，最好一个字符。(如 "d" 表示 data/default).

参考 Section 9.7.5.4, “KeyValue” 获取更多信息，关于HBase 内部保存数据，了解为什么这很重要。

6.3.2.2. 属性

详细属性名 (如, "myVeryImportantAttribute") 易读，最好还是用短属性名 (e.g., "via") 保存到HBase.

参考 Section 9.7.5.4, “KeyValue” 获取更多信息，关于HBase 内部保存数据，了解为什么这很重要。

6.3.2.3. 行键长度

让行键短到可读即可，这样对获取数据有用(e.g., Get vs. Scan)。 短键对访问数据无用，并不比长键对get/scan更好。设计行键需要权衡。

6.3.2.4. 字节模式

long 类型有 8 字节. 8字节内可以保存无符号数字到18,446,744,073,709,551,615. 如果用字符串保存--假设一个字节一个字符--，需要将近3倍的字节数。

不信? 下面是示例代码，可以自己运行一下。

// long
//
long l = 1234567890L;
byte[] lb = Bytes.toBytes(l);
System.out.println("long bytes length: " + lb.length);   // returns 8
		
String s = "" + l;
byte[] sb = Bytes.toBytes(s);
System.out.println("long as string length: " + sb.length);    // returns 10
			
// hash 
//
MessageDigest md = MessageDigest.getInstance("MD5");
byte[] digest = md.digest(Bytes.toBytes(s));
System.out.println("md5 digest bytes length: " + digest.length);    // returns 16
		
String sDigest = new String(digest);
byte[] sbDigest = Bytes.toBytes(sDigest);
System.out.println("md5 digest as string length: " + sbDigest.length);    // returns 26(译者注：实测值为22)		

6.3.3. 倒序时间戳

一个数据库处理的通常问题是找到最近版本的值。采用倒序时间戳作为键的一部分可以对此特定情况有很大帮助。也在Tom White的Hadoop书籍的HBase 章节能找到: The Definitive Guide (O'Reilly), 该技术包含追加(Long.MAX_VALUE - timestamp) 到key的后面，如 [key][reverse_timestamp].

表内[key]的最近的值可以用[key]进行 Scan 找到并获取第一个记录。由于 HBase 行键是排序的，该键排在任何比它老的行键的前面，所以必然是第一个。

该技术可以用于代替Section 6.4, “ 版本的数量 ” ，其目的是保存所有版本到“永远”(或一段很长时间) 。同时，采用同样的Scan技术，可以很快获取其他版本。

6.3.4. 行键和列族

行键在列族范围内。所以同样的行键可以在同一个表的每个列族中存在而不会冲突。

6.3.5. 行键永远不变

行键不能改变。唯一可以“改变”的方式是删除然后再插入。这是一个网上常问问题，所以要注意开始就要让行键正确(且/或在插入很多数据之前)。

6.4.  版本数量

6.4.1. 最大版本数

行的版本的数量是HColumnDescriptor设置的，每个列族可以单独设置，默认是3。这个设置是很重要的，在Chapter 5, 数据模型有描述，因为HBase是不会去覆盖一个值的，他只会在后面在追加写，用时间戳来区分、过早的版本会在执行主紧缩的时候删除。这个版本的值可以根据具体的应用增加减少。

不推荐将版本最大值设到一个很高的水平 (如, 成百或更多)，除非老数据对你很重要。因为这会导致存储文件变得极大。

6.4.2.  最小版本数

和行的最大版本数一样，最小版本数也是通过HColumnDescriptor 在每个列族中设置的。最小版本数缺省值是0，表示该特性禁用。 最小版本数参数和存活时间一起使用，允许配置如“保存最后T秒有价值数据，最多N个版本，但最少约M个版本”(M是最小版本数，M

6.5.  支持数据类型

HBase 通过 Put 和 Result支持 "bytes-in/bytes-out" 接口，所以任何可被转为字节数组的东西可以作为值存入。输入可以是字符串，数字，复杂对象，甚至图像，只要他们能转为字节。

存在值的实际长度限制 (如 保存 10-50MB 对象到 HBase 可能对查询来说太长); 搜索邮件列表获取本话题的对话。 HBase的所有行都遵循 Chapter 5, 数据模型, 包括版本化。 设计时需考虑到这些，以及列族的块大小。

6.5.1. 计数器

一种支持的数据类型，值得一提的是“计数器”(如, 具有原子递增能力的数值)。参考 HTable的 Increment .

同步计数器在区域服务器中完成，不是客户端。

6.6. 联合

如果有多个表，不要在模式设计中忘了 Section 5.11, “Joins” 的潜在因素。

6.7. 存活时间 (TTL)

列族可以设置TTL秒数，HBase 在超时后将自动删除数据。影响 全部 行的全部版本 - 甚至当前版本。HBase里面TTL 时间时区是 UTC.

参考 HColumnDescriptor 获取更多信息。

6.8.  保留删除的单元

列族允许是否保留单元。这就是说  Get 或 Scan 操作仍可以获取删除的单元。由于这些操作指定时间范围，结束在删除单元发生效果之前。这甚至允许在删除进行时进行即时查询。

删除的单元仍然受TTL控制，并永远不会超过“最大版本数”被删除的单元。新 "raw" scan 选项返回所有已删除的行和删除标志。

参考 HColumnDescriptor 获取更多信息

6.9.  第二索引和改变路径查询

本节标题也可以为"如果表的行键像这样 ，但我又想像那样查询该表." A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are are reporting requirements on activity across users for certain time ranges. Thus, selecting by user is easy because it is in the lead position of the key, but time is not.

There is no single answer on the best way to handle this because it depends on...

• Number of users
• Data size and data arrival rate
• Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges)
• Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others)

... and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution. Common techniques are in sub-sections below. This is a comprehensive, but not exhaustive, list of approaches.

It should not be a surprise that secondary indexes require additional cluster space and processing. This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update. RBDMS products are more advanced in this regard to handle alternative index management out of the box. However, HBase scales better at larger data volumes, so this is a feature trade-off.

Pay attention to Chapter 11, Performance Tuning when implementing any of these approaches.

Additionally, see the David Butler response in this dist-list thread HBase, mail # user - Stargate+hbase

6.9.1.  过滤查询

根据具体应用，可能和 Section 9.4, “Client Request Filters” 用法相当。在这种情况下，没有第二索引被创建。然而，不要像这样从应用 (如单线程客户端)中对大表尝试全表扫描。

6.9.2.  定期更新第二索引

第二索引可以在另一个表中创建，并通过MapReduce任务定期更新。任务可以在当天执行，但依赖于加载策略，可能会同主表失去同步。

参考 Section 7.2.2, “HBase MapReduce Read/Write Example” 获取更多信息.

6.9.3.  双写第二索引

另一个策略是在将数据写到集群的同时创建第二索引(如：写到数据表，同时写到索引表)。如果该方法在数据表存在之后采用，则需要利用MapReduce任务来生成已有数据的第二索引。 (参考 Section 6.9.2, “ Periodic-Update Secondary Index ”).

6.9.4.  汇总表(Summary Tables)

对时间跨度长 (e.g., 年报) 和数据量巨大，汇总表是通用路径。可通过MapReduce任务生成到另一个表。

参考 Section 7.2.4, “HBase MapReduce Summary to HBase Example” 获取更多信息。

6.9.5.  协处理第二索引

协处理动作像 RDBMS 触发器。 这在 0.92中添加. 更多参考 Section 9.6.3, “Coprocessors”

6.10. 限制

HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for Constraints is in enforcing business rules for attributes in the table (eg. make sure values are in the range 1-10). Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled. Extensive documentation on using Constraints can be found at: Constraint since version 0.94.

 

6.11. 模式(schema)设计用例

This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack rows into columns for certain time-periods. For a detailed explanation, see:http://opentsdb.net/schema.html, and Lessons Learned from OpenTSDB from HBaseCon2012.

But this is how the general concept works: data is ingested, for example, in this manner…

[hostname][log-event][timestamp1]  [hostname][log-event][timestamp2]  [hostname][log-event][timestamp3]  

… with separate rowkeys for each detailed event, but is re-written like this…

[hostname][log-event][timerange]

… and each of the above events are converted into columns stored with a time-offset relative to the beginning timerange (e.g., every 5 minutes). This is obviously a very advanced processing technique, but HBase makes this possible.

6.11.3. Case Study - Customer/Order

Assume that HBase is used to store customer and order information. There are two core record-types being ingested: a Customer record type, and Order record type.

The Customer record type would include all the things that you’d typically expect:

• Customer number
• Customer name
• Address (e.g., city, state, zip)
• Phone numbers, etc.

The Order record type would include things like:

• Customer number
• Order number
• Sales date
• A series of nested objects for shipping locations and line-items (see Section 6.11.3.2, “Order Object Design” for details)

Assuming that the combination of customer number and sales order uniquely identify an order, these two attributes will compose the rowkey, and specifically a composite key such as:

[customer number][order number]

… for a ORDER table. However, there are more design decisions to make: are the raw values the best choices for rowkeys?

The same design questions in the Log Data use-case confront us here. What is the keyspace of the customer number, and what is the format (e.g., numeric? alphanumeric?) As it is advantageous to use fixed-length keys in HBase, as well as keys that can support a reasonable spread in the keyspace, similar options appear:

Composite Rowkey With Hashes:

• [MD5 of customer number] = 16 bytes
• [MD5 of order number] = 16 bytes

Composite Numeric/Hash Combo Rowkey:

• [substituted long for customer number] = 8 bytes
• [MD5 of order number] = 16 bytes

6.11.3.1. Single Table? Multiple Tables?

A traditional design approach would have separate tables for CUSTOMER and SALES. Another option is to pack multiple record types into a single table (e.g., CUSTOMER++).

Customer Record Type Rowkey:

• [customer-id]
• [type] = type indicating ‘1’ for customer record type

Order Record Type Rowkey:

• [customer-id]
• [type] = type indicating ‘2’ for order record type
• [order]

The advantage of this particular CUSTOMER++ approach is that organizes many different record-types by customer-id (e.g., a single scan could get you everything about that customer). The disadvantage is that it’s not as easy to scan for a particular record-type.

6.11.3.2. Order Object Design

Now we need to address how to model the Order object. Assume that the class structure is as follows:

Order       ShippingLocation     (an Order can have multiple ShippingLocations)            LineItem               (a ShippingLocation can have multiple LineItems)  

... there are multiple options on storing this data.

6.11.3.2.1. Completely Normalized

With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and LINE_ITEM.

The ORDER table's rowkey was described above: Section 6.11.3, “Case Study - Customer/Order”

The SHIPPING_LOCATION's composite rowkey would be something like this:

• [order-rowkey]
• [shipping location number] (e.g., 1st location, 2nd, etc.)

The LINE_ITEM table's composite rowkey would be something like this:

• [order-rowkey]
• [shipping location number] (e.g., 1st location, 2nd, etc.)
• [line item number] (e.g., 1st lineitem, 2nd, etc.)

Such a normalized model is likely to be the approach with an RDBMS, but that's not your only option with HBase. The cons of such an approach is that to retrieve information about any Order, you will need:

• Get on the ORDER table for the Order
• Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances
• Scan on the LINE_ITEM for each ShippingLocation

... granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in HBase you're just more aware of this fact.

6.11.3.2.2. Single Table With Record Types

With this approach, there would exist a single table ORDER that would contain

The Order rowkey was described above: Section 6.11.3, “Case Study - Customer/Order”

• [order-rowkey]
• [ORDER record type]

The ShippingLocation composite rowkey would be something like this:

• [order-rowkey]
• [SHIPPING record type]
• [shipping location number] (e.g., 1st location, 2nd, etc.)

The LineItem composite rowkey would be something like this:

• [order-rowkey]
• [LINE record type]
• [shipping location number] (e.g., 1st location, 2nd, etc.)
• [line item number] (e.g., 1st lineitem, 2nd, etc.)

6.11.3.2.3. Denormalized

A variant of the Single Table With Record Types approach is to denormalize and flatten some of the object hierarchy, such as collapsing the ShippingLocation attributes onto each LineItem instance.

The LineItem composite rowkey would be something like this:

• [order-rowkey]
• [LINE record type]
• [line item number] (e.g., 1st lineitem, 2nd, etc. - care must be taken that there are unique across the entire order)

... and the LineItem columns would be something like this:

• itemNumber
• quantity
• price
• shipToLine1 (denormalized from ShippingLocation)
• shipToLine2 (denormalized from ShippingLocation)
• shipToCity (denormalized from ShippingLocation)
• shipToState (denormalized from ShippingLocation)
• shipToZip (denormalized from ShippingLocation)

The pros of this approach include a less complex object heirarchy, but one of the cons is that updating gets more complicated in case any of this information changes.

6.11.3.2.4. Object BLOB

With this approach, the entire Order object graph is treated, in one way or another, as a BLOB. For example, the ORDER table's rowkey was described above: Section 6.11.3, “Case Study - Customer/Order”, and a single column called "order" would contain an object that could be deserialized that contained a container Order, ShippingLocations, and LineItems.

There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, etc. All of them are variants of the same approach: encode the object graph to a byte-array. Care should be taken with this approach to ensure backward compatibilty in case the object model changes such that older persisted structures can still be read back out of HBase.

Pros are being able to manage complex object graphs with minimal I/O (e.g., a single HBase Get per Order in this example), but the cons include the aforementioned warning about backward compatiblity of serialization, language dependencies of serialization (e.g., Java Serialization only works with Java clients), the fact that you have to deserialize the entire object to get any piece of information inside the BLOB, and the difficulty in getting frameworks like Hive to work with custom objects like this.

6.11.4. Case Study - "Tall/Wide/Middle" Schema Design Smackdown

This section will describe additional schema design questions that appear on the dist-list, specifically about tall and wide tables. These are general guidelines and not laws - each application must consider its own needs.

6.11.4.1. Rows vs. Versions

A common question is whether one should prefer rows or HBase's built-in-versioning. The context is typically where there are "a lot" of versions of a row to be retained (e.g., where it is significantly above the HBase default of 3 max versions). The rows-approach would require storing a timstamp in some portion of the rowkey so that they would not overwite with each successive update.

Preference: Rows (generally speaking).

6.11.4.2. Rows vs. Columns

Another common question is whether one should prefer rows or columns. The context is typically in extreme cases of wide tables, such as having 1 row with 1 million attributes, or 1 million rows with 1 columns apiece.

Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely wide cases, not in the standard use-case where one needs to store a few dozen or hundred columns. But there is also a middle path between these two options, and that is "Rows as Columns."

6.11.4.3. Rows as Columns

The middle path between Rows vs. Columns is packing data that would be a separate row into columns, for certain rows. OpenTSDB is the best example of this case where a single row represents a defined time-range, and then discrete events are treated as columns. This approach is often more complex, and may require the additional complexity of re-writing your data, but has the advantage of being I/O efficient. For an overview of this approach, see ???.

6.11.5. Case Study - List Data

The following is an exchange from the user dist-list regarding a fairly common question: how to handle per-user list data in Apache HBase.

*** QUESTION ***

We're looking at how to store a large amount of (per-user) list data in HBase, and we were trying to figure out what kind of access pattern made the most sense. One option is store the majority of the data in a key, so we could have something like:

:"" (no value)  :"" (no value)  :"" (no value)  			

The other option we had was to do this entirely using:

:...  :...      		

where each row would contain multiple values. So in one case reading the first thirty values would be:

scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}      		

And in the second case it would be

get 'FixedWidthUserName\x00\x00\x00\x00'      		

The general usage pattern would be to read only the first 30 values of these lists, with infrequent access reading deeper into the lists. Some users would have <= 30 total values in these lists, and some users would have millions (i.e. power-law distribution)

The single-value format seems like it would take up more space on HBase, but would offer some improved retrieval / pagination flexibility. Would there be any significant performance advantages to be able to paginate via gets vs paginating with scans?

My initial understanding was that doing a scan should be faster if our paging size is unknown (and caching is set appropriately), but that gets should be faster if we'll always need the same page size. I've ended up hearing different people tell me opposite things about performance. I assume the page sizes would be relatively consistent, so for most use cases we could guarantee that we only wanted one page of data in the fixed-page-length case. I would also assume that we would have infrequent updates, but may have inserts into the middle of these lists (meaning we'd need to update all subsequent rows).

Thanks for help / suggestions / follow-up questions.

*** ANSWER ***

If I understand you correctly, you're ultimately trying to store triples in the form "user, valueid, value", right? E.g., something like:

"user123, firstname, Paul",  "user234, lastname, Smith"  			

(But the usernames are fixed width, and the valueids are fixed width).

And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with valueid Y". Is that right? And these values should be returned sorted by valueid?

The tl;dr version is that you should probably go with one row per user+value, and not build a complicated intra-row pagination scheme on your own unless you're really sure it is needed.

Your two options mirror a common question people have when designing HBase schemas: should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so there are many rows in the table for each user; the row key is user + valueid, and there would be (presumably) a single column qualifier that means "the value". This is great if you want to scan over rows in sorted order by row key (thus my question above, about whether these ids are sorted correctly). You can start a scan at any user+valueid, read the next 30, and be done. What you're giving up is the ability to have transactional guarantees around all the rows for one user, but it doesn't sound like you need that. Doing it this way is generally recommended (see here #schema.smackdown).

Your second option is "wide": you store a bunch of values in one row, using different qualifiers (where the qualifier is the valueid). The simple way to do that would be to just store ALL values for one user in a single row. I'm guessing you jumped to the "paginated" version because you're assuming that storing millions of columns in a single row would be bad for performance, which may or may not be true; as long as you're not trying to do too much in a single request, or do things like scanning over and returning all of the cells in the row, it shouldn't be fundamentally worse. The client has methods that allow you to get specific slices of columns.

Note that neither case fundamentally uses more disk space than the other; you're just "shifting" part of the identifying information for a value either to the left (into the row key, in option one) or to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores the whole row key, and column family name. (If this is a bit confusing, take an hour and watch Lars George's excellent video about understanding HBase schema design: http://www.youtube.com/watch?v=_HLoH_PgrLk).

A manually paginated version has lots more complexities, as you note, like having to keep track of how many things are in each page, re-shuffling if new values are inserted, etc. That seems significantly more complex. It might have some slight speed advantages (or disadvantages!) at extremely high throughput, and the only way to really know that would be to try it out. If you don't have time to build it both ways and compare, my advice would be to start with the simplest option (one row per user+value). Start simple and iterate! :)

 

6.12. 业务和性能配置选项

参考 the Performance section Section 11.6, “Schema Design” for more information operational and performance schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes.

 

 

Chapter 7. HBase 和 MapReduce

关于 HBase 和 MapReduce详见 javadocs. 下面是一些附加的帮助文档. MapReduce的更多信息 (如，通用框架), 参考 Hadoop MapReduce Tutorial.

7.1. Map-Task 分割

7.1.1 默认 HBase MapReduce 分割器(Splitter)

当 MapReduce 任务的HBase 表使用TableInputFormat为数据源格式的时候,他的splitter会给这个table的每个region一个map。因此，如果一个table有100个region，就有100个map-tasks，不论需需要scan多少个列族 。

7.1.2. 自定义分割器

iv>

For those interested in implementing custom splitters, see the method getSplits in TableInputFormatBase. That is where the logic for map-task assignment resides.

 

7.2. HBase MapReduce 例子

7.2.1 HBase MapReduce 读取例子

下面是使用HBase 作为源的MapReduce读取示例。特别是仅有Mapper实例，没有Reducer。Mapper什么也不产生。

如下所示...

Configuration config = HBaseConfiguration.create();
Job job = new Job(config, "ExampleRead");
job.setJarByClass(MyReadJob.class);     // class that contains mapper
	
Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs
...
  
TableMapReduceUtil.initTableMapperJob(
  tableName,        // input HBase table name
  scan,             // Scan instance to control CF and attribute selection
  MyMapper.class,   // mapper
  null,             // mapper output key 
  null,             // mapper output value
  job);
job.setOutputFormatClass(NullOutputFormat.class);   // because we aren't emitting anything from mapper
	    
boolean b = job.waitForCompletion(true);
if (!b) {
  throw new IOException("error with job!");
}

...mapper需要继承于TableMapper...

public class MyMapper extends TableMapper {
public void map(ImmutableBytesWritable row, Result value, Context context) 
throws InterruptedException, IOException {
// process data for the row from the Result instance.

7.2.2. HBase MapReduce 读/写 示例

下面是使用HBase 作为源和目标的MapReduce示例. 本示例简单从一个表复制到另一个表。

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleReadWrite");
job.setJarByClass(MyReadWriteJob.class);    // class that contains mapper
	        	        
Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs
	        
TableMapReduceUtil.initTableMapperJob(
	sourceTable,      // input table
	scan,	          // Scan instance to control CF and attribute selection
	MyMapper.class,   // mapper class
	null,	          // mapper output key
	null,	          // mapper output value
	job);
TableMapReduceUtil.initTableReducerJob(
	targetTable,      // output table
	null,             // reducer class
	job);
job.setNumReduceTasks(0);
	        
boolean b = job.waitForCompletion(true);
if (!b) {
    throw new IOException("error with job!");
}
    

TableMapReduceUtil做了什么需要解释, 特别是对 reducer. TableOutputFormat 作为 outputFormat 类, 几个参数在config中设置(e.g., TableOutputFormat.OUTPUT_TABLE), 同时设置reducer output key 到ImmutableBytesWritable 和 reducer value到 Writable. 这可以编程时设置到job和conf，但TableMapReduceUtil 使其变简单.

下面是 mapper示例, 创建一个 Put，匹配输入的 Result 并提交. Note: 这是 CopyTable 工具做的.

public static class MyMapper extends TableMapper  {

	public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
		// this example is just copying the data from the source table...
   		context.write(row, resultToPut(row,value));
   	}
        
  	private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {
  		Put put = new Put(key.get());
 		for (KeyValue kv : result.raw()) {
			put.add(kv);
		}
		return put;
   	}
}
    

这不是真正的 reducer 步骤, 所以 TableOutputFormat 处理发送 Put 到目标表.

这仅是示例, 开发者可以选择不使用TableOutputFormat并自己连接到目标表。

7.2.3. HBase MapReduce Read/Write 多表输出示例

TODO: MultiTableOutputFormat 示例.

7.2.4. HBase MapReduce 汇总到 HBase 示例

下面是使用HBase 作为源和目标的MapReduce示例，具有汇总步骤。本示例计算一个表中值的个数，并将汇总的计数输出到另一个表。

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleSummary");
job.setJarByClass(MySummaryJob.class);     // class that contains mapper and reducer
	        
Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs
	        
TableMapReduceUtil.initTableMapperJob(
	sourceTable,        // input table
	scan,               // Scan instance to control CF and attribute selection
	MyMapper.class,     // mapper class
	Text.class,         // mapper output key
	IntWritable.class,  // mapper output value
	job);
TableMapReduceUtil.initTableReducerJob(
	targetTable,        // output table
	MyTableReducer.class,    // reducer class
	job);
job.setNumReduceTasks(1);   // at least one, adjust as required
	    
boolean b = job.waitForCompletion(true);
if (!b) {
	throw new IOException("error with job!");
}    
    

本示例mapper，将一个列的一个字符串值作为汇总值。该值作为key在mapper中生成。 IntWritable 代表一个实例计数。

public static class MyMapper extends TableMapper  {

	private final IntWritable ONE = new IntWritable(1);
   	private Text text = new Text();
    	
   	public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
        	String val = new String(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr1")));
          	text.set(val);     // we can only emit Writables...

        	context.write(text, ONE);
   	}
}
    

在 reducer, "ones" 被统计 (和其他 MR 示例一样), 产生一个 Put.

public static class MyTableReducer extends TableReducer  {
        
 	public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
    		int i = 0;
    		for (IntWritable val : values) {
    			i += val.get();
    		}
    		Put put = new Put(Bytes.toBytes(key.toString()));
    		put.add(Bytes.toBytes("cf"), Bytes.toBytes("count"), Bytes.toBytes(i));

    		context.write(null, put);
   	}
}
    

7.2.5. HBase MapReduce 汇总到文件示例

This very similar to the summary example above, with exception that this is using HBase as a MapReduce source but HDFS as the sink. The differences are in the job setup and in the reducer. The mapper remains the same.

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleSummaryToFile");
job.setJarByClass(MySummaryFileJob.class);     // class that contains mapper and reducer
	        
Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs
	        
TableMapReduceUtil.initTableMapperJob(
	sourceTable,        // input table
	scan,               // Scan instance to control CF and attribute selection
	MyMapper.class,     // mapper class
	Text.class,         // mapper output key
	IntWritable.class,  // mapper output value
	job);
job.setReducerClass(MyReducer.class);    // reducer class
job.setNumReduceTasks(1);    // at least one, adjust as required
FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile"));  // adjust directories as required
	    
boolean b = job.waitForCompletion(true);
if (!b) {
	throw new IOException("error with job!");
}    
    

As stated above, the previous Mapper can run unchanged with this example. As for the Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting Puts.

 public static class MyReducer extends Reducer  {
        
	public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
		int i = 0;
		for (IntWritable val : values) {
			i += val.get();
		}	
		context.write(key, new IntWritable(i));
	}
}
    

7.2.6. HBase MapReduce 没有Reducer时汇总到 HBase

It is also possible to perform summaries without a reducer - if you use HBase as the reducer.

An HBase target table would need to exist for the job summary. The HTable method incrementColumnValue would be used to atomically increment values. From a performance perspective, it might make sense to keep a Map of values with their values to be incremeneted for each map-task, and make one update per key at during the cleanup method of the mapper. However, your milage may vary depending on the number of rows to be processed and unique keys.

In the end, the summary results are in HBase.

7.2.7. HBase MapReduce 汇总到 RDBMS

有时更合适产生汇总到 RDBMS.这种情况下,可以将汇总直接通过一个自定义的reducer输出到 RDBMS 。 setup 方法可以连接到 RDBMS (连接信息可以通过context的自定义参数传递)， cleanup 可以关闭连接.

关键需要理解job的多个reducer会影响汇总实现，必须在reducer中进行设计。无论是一个recucer还是多个reducer。不管对错, 依赖于你的用例。认识到多个reducer分配到job，需要创建多个并发的RDBMS连接-可以扩充，但仅在一个点。

 public static class MyRdbmsReducer extends Reducer  {

	private Connection c = null;
	
	public void setup(Context context) {
  		// create DB connection...
  	}
        
	public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
		// do summarization
		// in this example the keys are Text, but this is just an example
	}
	
	public void cleanup(Context context) {
  		// close db connection
  	}
	
}
    

最后，汇总的结果被写入到 RDBMS 表.

7.3. 在一个MapReduce Job中访问其他的HBase Tables

尽管现有的框架允许一个HBase table作为一个MapReduce job的输入，其他的HBase table可以同时作为普通的表被访问。例如在一个MapReduce的job中，可以在Mapper的setup方法中创建HTable实例。

public class MyMapper extends TableMapper {
  private HTable myOtherTable;

  @Override
  public void setup(Context context) {
    myOtherTable = new HTable("myOtherTable");
  }

7.4. 预测执行

通常建议关掉针对HBase的MapReduce job的预测执行(speculative execution)功能。这个功能也可以用每个Job的配置来完成。对于整个集群，使用预测执行意味着双倍的运算量。这可不是你所希望的。

参考 Section 2.8.2.9, “Speculative Execution” 获取更多信息。

Chapter 8.  HBase安全

8.1. 安全客户端访问HBase

新版 HBase (>= 0.92) 支持客户端可选 SASL 认证.

这里描述如何设置HBase 和 HBase 客户端，以安全连接到HBase 资源.

8.1.1. 先决条件

HBase 必须使用 安全Hadoop/HBase的新 maven 配置文件： -P security. Secure Hadoop dependent classes are separated under a pseudo-module in the security/ directory and are only included if built with the secure Hadoop profile.

You need to have a working Kerberos KDC.

A HBase configured for secure client access is expected to be running on top of a secured HDFS cluster. HBase must be able to authenticate to HDFS services. HBase needs Kerberos credentials to interact with the Kerberos-enabled HDFS daemons. Authenticating a service should be done using a keytab file. The procedure for creating keytabs for HBase service is the same as for creating keytabs for Hadoop. Those steps are omitted here. Copy the resulting keytab files to wherever HBase Master and RegionServer processes are deployed and make them readable only to the user account under which the HBase daemons will run.

A Kerberos principal has three parts, with the form username/[email protected]. We recommend using hbase as the username portion.

The following is an example of the configuration properties for Kerberos operation that must be added to the hbase-site.xml file on every server machine in the cluster. Required for even the most basic interactions with a secure Hadoop configuration, independent of HBase security.

      
        hbase.regionserver.kerberos.principal
        hbase/[email protected]
      
      
        hbase.regionserver.keytab.file
        /etc/hbase/conf/keytab.krb5
      
      
        hbase.master.kerberos.principal
        hbase/[email protected]
      
      
        hbase.master.keytab.file
        /etc/hbase/conf/keytab.krb5
      
    

Each HBase client user should also be given a Kerberos principal. This principal should have a password assigned to it (as opposed to a keytab file). The client principal's maxrenewlife should be set so that it can be renewed enough times for the HBase client process to complete. For example, if a user runs a long-running HBase client process that takes at most 3 days, we might create this user's principal within kadmin with: addprinc -maxrenewlife 3days

Long running daemons with indefinite lifetimes that require client access to HBase can instead be configured to log in from a keytab. For each host running such daemons, create a keytab withkadmin or kadmin.local. The procedure for creating keytabs for HBase service is the same as for creating keytabs for Hadoop. Those steps are omitted here. Copy the resulting keytab files to where the client daemon will execute and make them readable only to the user account under which the daemon will run.

8.1.2. 安全操作的服务器端配置

增加下列内容到 hbase-site.xml file on every server machine in the cluster:

      
        hbase.security.authentication
        kerberos 
       
      
        hbase.security.authorization
        true
      
      
        hbase.rpc.engine
        org.apache.hadoop.hbase.ipc.SecureRpcEngine
      
      
      hbase.coprocessor.region.classes
        org.apache.hadoop.hbase.security.token.TokenProvider
      
    

A full shutdown and restart of HBase service is required when deploying these configuration changes.

8.1.3. 客户端安全操作配置

每个客户端增加下列内容到 hbase-site.xml:

      
        hbase.security.authentication
        kerberos
       
      
        hbase.rpc.engine
        org.apache.hadoop.hbase.ipc.SecureRpcEngine
      
    

The client environment must be logged in to Kerberos from KDC or keytab via the kinit command before communication with the HBase cluster will be possible.

Be advised that if the hbase.security.authentication and hbase.rpc.engine properties in the client- and server-side site files do not match, the client will not be able to communicate with the cluster.

Once HBase is configured for secure RPC it is possible to optionally configure encrypted communication. To do so, 增加下列内容到 hbase-site.xml file on every client:

      
        hbase.rpc.protection
        privacy
      
    

This configuration property can also be set on a per connection basis. Set it in the Configuration supplied to HTable:

      Configuration conf = HBaseConfiguration.create();
      conf.set("hbase.rpc.protection", "privacy");
      HTable table = new HTable(conf, tablename);
    

Expect a ~10% performance penalty for encrypted communication.

8.1.4. 客户端安全操作配置 - Thrift 网关

每个Thrift网关增加下列内容到 hbase-site.xml:

    
      hbase.thrift.keytab.file
      /etc/hbase/conf/hbase.keytab
    
    
      hbase.thrift.kerberos.principal
      $USER/[email protected]
    
    

Substitute the appropriate credential and keytab for $USER and $KEYTAB respectively.

The Thrift gateway will authenticate with HBase using the supplied credential. No authentication will be performed by the Thrift gateway itself. All client access via the Thrift gateway will use the Thrift gateway's credential and have its privilege.

8.1.5. 客户端安全操作配置 - REST 网关

每个REST网关增加下列内容到 hbase-site.xml:

    
      hbase.rest.keytab.file
      $KEYTAB
    
    
      hbase.rest.kerberos.principal
      $USER/[email protected]
    
    

Substitute the appropriate credential and keytab for $USER and $KEYTAB respectively.

The REST gateway will authenticate with HBase using the supplied credential. No authentication will be performed by the REST gateway itself. All client access via the REST gateway will use the REST gateway's credential and have its privilege.

It should be possible for clients to authenticate with the HBase cluster through the REST gateway in a pass-through manner via SPEGNO HTTP authentication. This is future work.

8.2. 访问控制

Newer releases of HBase (>= 0.92) support optional access control list (ACL-) based protection of resources on a column family and/or table basis.

This describes how to set up Secure HBase for access control, with an example of granting and revoking user permission on table resources provided.

8.2.1. 先决条件

You must configure HBase for secure operation. Refer to the section "安全客户端访问HBase" and complete all of the steps described there.

You must also configure ZooKeeper for secure operation. Changes to ACLs are synchronized throughout the cluster using ZooKeeper. Secure authentication to ZooKeeper must be enabled or otherwise it will be possible to subvert HBase access control via direct client access to ZooKeeper. Refer to the section on secure ZooKeeper configuration and complete all of the steps described there.

8.2.2. 概述

With Secure RPC and Access Control enabled, client access to HBase is authenticated and user data is private unless access has been explicitly granted. Access to data can be granted at a table or per column family basis.

However, the following items have been left out of the initial implementation for simplicity:

1. Row-level or per value (cell): This would require broader changes for storing the ACLs inline with rows. It is a future goal.

2. Push down of file ownership to HDFS: HBase is not designed for the case where files may have different permissions than the HBase system principal. Pushing file ownership down into HDFS would necessitate changes to core code. Also, while HDFS file ownership would make applying quotas easy, and possibly make bulk imports more straightforward, it is not clear that it would offer a more secure setup.

3. HBase managed "roles" as collections of permissions: We will not model "roles" internally in HBase to begin with. We instead allow group names to be granted permissions, which allows external modeling of roles via group membership. Groups are created and manipulated externally to HBase, via the Hadoop group mapping service.

Access control mechanisms are mature and fairly standardized in the relational database world. The HBase implementation approximates current convention, but HBase has a simpler feature set than relational databases, especially in terms of client operations. We don't distinguish between an insert (new record) and update (of existing record), for example, as both collapse down into a Put. Accordingly, the important operations condense to four permissions: READ, WRITE, CREATE, and ADMIN.

Operation To Permission MappingPermissionOperationReadGetExistsScanWritePutDeleteLock/UnlockRowIncrementColumnValueCheckAndDelete/PutFlushCompactCreateCreateAlterDropAdminEnable/DisableSplitMajor CompactGrantRevokeShutdown

Permissions can be granted in any of the following scopes, though CREATE and ADMIN permissions are effective only at table scope.

• Table

  • Read: User can read from any column family in table

  • Write: User can write to any column family in table

  • Create: User can alter table attributes; add, alter, or drop column families; and drop the table.

  • Admin: User can alter table attributes; add, alter, or drop column families; and enable, disable, or drop the table. User can also trigger region (re)assignments or relocation.

• Column Family

  • Read: User can read from the column family

  • Write: User can write to the column family

There is also an implicit global scope for the superuser.

The superuser is a principal, specified in the HBase site configuration file, that has equivalent access to HBase as the 'root' user would on a UNIX derived system. Normally this is the principal that the HBase processes themselves authenticate as. Although future versions of HBase Access Control may support multiple superusers, the superuser privilege will always include the principal used to run the HMaster process. Only the superuser is allowed to create tables, switch the balancer on or off, or take other actions with global consequence. Furthermore, the superuser has an implicit grant of all permissions to all resources.

Tables have a new metadata attribute: OWNER, the user principal who owns the table. By default this will be set to the user principal who creates the table, though it may be changed at table creation time or during an alter operation by setting or changing the OWNER table attribute. Only a single user principal can own a table at a given time. A table owner will have all permissions over a given table.

8.2.3. Server-side Configuration for Access Control

Enable the AccessController coprocessor in the cluster configuration and restart HBase. The restart can be a rolling one. Complete the restart of all Master and RegionServer processes before setting up ACLs.

To enable the AccessController, modify the hbase-site.xml file on every server machine in the cluster to look like:

      
        hbase.coprocessor.master.classes
        org.apache.hadoop.hbase.security.access.AccessController
      
      
      hbase.coprocessor.region.classes
        org.apache.hadoop.hbase.security.token.TokenProvider,
        org.apache.hadoop.hbase.security.access.AccessController
      
    

8.2.4. Shell Enhancements for Access Control

The HBase shell has been extended to provide simple commands for editing and updating user permissions. The following commands have been added for access control list management:

Grant

    grant    [  [  ] ]
     
         
 
 
         
  is zero or more letters from the set "RWCA": READ('R'), WRITE('W'), CREATE('C'), ADMIN('A').
 
         
 Note: Grants and revocations of individual permissions on a resource are both accomplished using the grant command. A separate revoke command is also provided by the shell, but this is for fast revocation of all of a user's access rights to a given resource only.
 
         
 Revoke
 
         
 
 
         
    revoke   [  [  ] ]
     
         
 
 
         
 Alter
 
         
 The alter command has been extended to allow ownership assignment:
 
         
      alter 'tablename', {OWNER => 'username'}
    
 
         
 
 
         
 User Permission
 
         
 The user_permission command shows all access permissions for the current user for a given table:
 
         
      user_permission 
 
 
          
 
           
 
            
 
             
 8.3. Secure Bulk Load
 
            
 
           
 
          
 
          
 Bulk loading in secure mode is a bit more involved than normal setup, since the client has to transfer the ownership of the files generated from the mapreduce job to HBase. Secure bulk loading is implemented by a coprocessor, named SecureBulkLoadEndpoint. SecureBulkLoadEndpoint uses a staging directory "hbase.bulkload.staging.dir", which defaults to /tmp/hbase-staging/. The algorithm is as follows.
 
          
 
           
 
            
Create an hbase owned staging directory which is world traversable (-rwx--x--x, 711) /tmp/hbase-staging.
 
            
A user writes out data to his secure output directory: /user/foo/data
 
            
A call is made to hbase to create a secret staging directory which is globally readable/writable (-rwxrwxrwx, 777): /tmp/hbase-staging/averylongandrandomdirectoryname
 
            
The user makes the data world readable and writable, then moves it into the random staging directory, then calls bulkLoadHFiles()
 
           
 
          
 
          
 Like delegation tokens the strength of the security lies in the length and randomness of the secret directory.
 
          
 You have to enable the secure bulk load to work properly. You can modify the hbase-site.xml file on every server machine in the cluster and add the SecureBulkLoadEndpoint class to the list of regionserver coprocessors:
 
          
                hbase.bulkload.staging.dir          /tmp/hbase-staging                          hbase.coprocessor.region.classes          org.apache.hadoop.hbase.security.token.TokenProvider,          org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint              
 
         
 
          
 
         
 
        
 
         
 
          
 
         
 
        
 
        
 
         
 
          
 
           
 
          
 
         
 
        
 
       
 
        
 
         
 
          
 
           
 
            
 
             
 
              
 Chapter 9. 架构
 
             
 
            
 
           
 
            
           
 
            
 
             
 
              
 
               
 9.1. 概述
 
              
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 9.1.1. NoSQL?
 
               
 
              
 
             
 
             
 HBase是一种 "NoSQL" 数据库. "NoSQL"是一个通用词表示数据库不是RDBMS ，后者支持 SQL 作为主要访问手段。有许多种 NoSQL 数据库: BerkeleyDB 是本地 NoSQL 数据库例子, 而 HBase 是大型分布式数据库。 技术上来说, HBase 更像是"数据存储(Data Store)" 多于 "数据库(Data Base)"。因为缺少很多RDBMS特性, 如列类型，第二索引，触发器，高级查询语言等.
 
             
 然而, HBase 有许多特征同时支持线性化和模块化扩充。 HBase 集群通过增加RegionServers进行扩充。 它可以放在普通的服务器中。例如，如果集群从10个扩充到20个RegionServer，存储空间和处理容量都同时翻倍。 RDBMS 也能很好扩充， 但仅对一个点 - 特别是对一个单独数据库服务器的大小 - 同时，为了更好的性能，需要特殊的硬件和存储设备。 HBase 特性：
 
             
 
              
 
               
强一致性读写: HBase 不是 "最终一致性(eventually consistent)" 数据存储. 这让它很适合高速计数聚合类任务。
 
               
自动分片(Automatic sharding): HBase 表通过region分布在集群中。数据增长时，region会自动分割并重新分布。
 
               
RegionServer 自动故障转移
 
               
Hadoop/HDFS 集成: HBase 支持本机外HDFS 作为它的分布式文件系统。
 
               
MapReduce: HBase 通过MapReduce支持大并发处理， HBase 可以同时做源和目标.
 
               
Java 客户端 API: HBase 支持易于使用的 Java API 进行编程访问.
 
               
Thrift/REST API: HBase 也支持Thrift 和 REST 作为非Java 前端.
 
               
Block Cache 和 Bloom Filters: 对于大容量查询优化， HBase支持 Block Cache 和 Bloom Filters。
 
               
运维管理: HBase提供内置网页用于运维视角和JMX 度量.
 
              
 
             
 
             
 
 
            
 
            
 
             
 
              
 
               
 
                
 9.1.2. 什么时候用 HBase?
 
               
 
              
 
             
 
             
 HBase不适合所有问题.
 
             
 首先，确信有足够多数据，如果有上亿或上千亿行数据，HBase是很好的备选。 如果只有上千或上百万行，则用传统的RDBMS可能是更好的选择。因为所有数据可以在一两个节点保存，集群其他节点可能闲置。
 
             
 其次，确信可以不依赖所有RDBMS的额外特性 (e.g., 列数据类型, 第二索引, 事物,高级查询语言等.) 一个建立在RDBMS上应用，如不能仅通过改变一个JDBC驱动移植到HBase。相对于移植， 需考虑从RDBMS 到 HBase是一次完全的重新设计。
 
             
 第三， 确信你有足够硬件。甚至 HDFS 在小于5个数据节点时，干不好什么事情 (根据如 HDFS 块复制具有缺省值 3), 还要加上一个 NameNode.
 
             
 HBase 能在单独的笔记本上运行良好。但这应仅当成开发配置。
 
            
 
            
 
             
 
              
 
               
 
                
 9.1.3.  HBase 和 Hadoop/HDFS 的区别?
 
               
 
              
 
             
 
             
 HDFS 是分布式文件系统，适合保存大文件。官方宣称它并非普通用途文件系统，不提供文件的个别记录的快速查询。 另一方面，HBase基于HDFS且提供大表的记录快速查找(和更新)。这有时可能引起概念混乱。 HBase 内部将数据放到索引好的 "存储文件(StoreFiles)" ，以便高速查询。存储文件位于 HDFS中。参考Chapter 5, 数据模型 和该章其他内容获取更多HBase如何归档的信息。
 
            
 
           
 
           
 
            
 
             
 
              
 
               
 9.2. 目录表(Catalog Tables)
 
              
 
             
 
            
 
            
 目录表 -ROOT- 和 .META. 作为 HBase 表存在。他们被HBase shell的 list 命令过滤掉了， 但他们和其他表一样存在。
 
            
 
             
 
              
 
               
 
                
 9.2.1. ROOT
 
               
 
              
 
             
 
             
 -ROOT- 保存 .META. 表存在哪里的踪迹. -ROOT- 表结构如下:
 
             
 Key:
 
             
 
              
 
               
.META. region key (.META.,,1)
 
              
 
             
 
             
 
 
             
 Values:
 
             
 
              
 
               
info:regioninfo (序列化.META.的 HRegionInfo 实例 )
 
               
info:server ( 保存 .META.的RegionServer的server:port)
 
               
info:serverstartcode ( 保存 .META.的RegionServer进程的启动时间)
 
              
 
             
 
             
 
 
            
 
            
 
             
 
              
 
               
 
                
 9.2.2. META
 
               
 
              
 
             
 
             
 .META. 保存系统中所有region列表。 .META.表结构如下:
 
             
 Key:
 
             
 
              
 
               
Region key 格式 ([table],[region start key],[region id])
 
              
 
             
 
             
 
 
             
 Values:
 
             
 
              
 
               
info:regioninfo (序列化.META.的 HRegionInfo 实例 )
 
               
info:server ( 保存 .META.的RegionServer的server:port)
 
               
info:serverstartcode ( 保存 .META.的RegionServer进程的启动时间)
 
              
 
             
 
             
 
 
             
 当表在分割过程中，会创建额外的两列, info:splitA 和 info:splitB 代表两个女儿 region. 这两列的值同样是序列化HRegionInfo 实例. region最终分割完毕后，这行会删除。
 
             
 HRegionInfo的备注: 空 key 用于指示表的开始和结束。具有空开始键值的region是表内的首region。 如果 region 同时有空起始和结束key，说明它是表内的唯一region。
 
             
 在需要编程访问(希望不要)目录元数据时，参考 Writables 工具.
 
            
 
            
 
             
 
              
 
               
 
                
 9.2.3. 启动时序
 
               
 
              
 
             
 
             
 META 地址首先在ROOT 中设置。META 会更新 server 和 startcode 的值.
 
             
 需要 region-RegionServer 分配信息, 参考 Section 9.7.2, “Region-RegionServer 分配”.
 
            
 
           
 
           
 
            
 
             
 
              
 
               
 9.3. 客户端
 
              
 
             
 
            
 
            
 HBase客户端的 HTable类负责寻找相应的RegionServers来处理行。他是先查询 .META. 和 -ROOT 目录表。然后再确定region的位置。定位到所需要的区域后，客户端会直接 去访问相应的region(不经过master)，发起读写请求。这些信息会缓存在客户端，这样就不用每发起一个请求就去查一下。如果一个region已经废弃(原因可能是master load balance或者RegionServer死了)，客户端就会重新进行这个步骤，决定要去访问的新的地址。
 
            
 参考 Section 9.5.2, “Runtime Impact” for more information about the impact of the Master on HBase Client communication.
 
            
 管理集群操作是经由HBaseAdmin发起的
 
            
 
             
 
              
 
               
 
                
 9.3.1. 连接
 
               
 
              
 
             
 
             
 关于连接的配置信息，参见Section 3.7, “连接HBase集群的客户端配置和依赖”.
 
             
 HTable不是线程安全的。建议使用同一个HBaseConfiguration实例来创建HTable实例。这样可以共享ZooKeeper和socket实例。例如，最好这样做：
 
             
HBaseConfiguration conf = HBaseConfiguration.create();
HTable table1 = new HTable(conf, "myTable");
HTable table2 = new HTable(conf, "myTable");
 
             
 而不是这样：
 
             
HBaseConfiguration conf1 = HBaseConfiguration.create();
HTable table1 = new HTable(conf1, "myTable");
HBaseConfiguration conf2 = HBaseConfiguration.create();
HTable table2 = new HTable(conf2, "myTable");
 
             
 如果你想知道的更多的关于HBase客户端connection的知识，可以参照： HConnectionManager.
 
             
 
              
 
               
 
                
 9.3.1.1. 连接池
 
               
 
              
 
             
 
             
 对需要高端多线程访问的应用 (如网页服务器或应用服务器需要在一个JVM服务很多应用线程)，参考 HTablePool.
 
             
  
 
            
 
            
 
             
 
              
 
               
 
                
 9.3.2. 写缓冲和批量操作
 
               
 
              
 
             
 
             
 若关闭了HTable中的 Section 11.10.4, “AutoFlush”，Put操作会在写缓冲填满的时候向RegionServer发起请求。默认情况下，写缓冲是2MB.在Htable被废弃之前，要调用close(), flushCommits()操作，这样写缓冲就不会丢失。
 
             
 要想更好的细粒度控制 Put或Delete的批量操作，可以参考Htable中的batch 方法.
 
             
 
              
 
               
 
                
 
                 
 9.3.3. 外部客户端
 
                
 
               
 
              
 
              
 关于非Java客户端和定制协议信息，在 Chapter 10, 外部 API
 
             
 
             
 
              
 
               
 
                
 
                 
 9.3.4. 行锁
 
                
 
               
 
              
 
              
 行锁 在客户端 API中仍然存在， 但是 不鼓励使用，因为管理不好，会锁定整个RegionServer.
 
              
 这里是优秀ticket HBASE-2332 从终端移除该特性。
 
             
 
             
  
 
            
 
            
 
             
 
              
 
               
 
                
 9.4. 客户端请求过滤器
 
               
 
              
 
             
 
             
 Get 和 Scan 实例可以用 filters 配置，以应用于 RegionServer.
 
             
 过滤器可能会搞混，因为有很多类型的过滤器， 最好通过理解过滤器功能组来了解他们。
 
             
 
              
 
               
 
                
 
                 
 9.4.1. 结构(Structural)过滤器
 
                
 
               
 
              
 
              
 结构过滤器包含其他过滤器
 
              
 
               
 
                
 
                 
 
                  
 9.4.1.1. FilterList
 
                 
 
                
 
               
 
               
 FilterList 代表一个过滤器列表，过滤器间具有 FilterList.Operator.MUST_PASS_ALL 或 FilterList.Operator.MUST_PASS_ONE 关系。下面示例展示两个过滤器的'或'关系(检查同一属性的'my value' 或'my other value' ).
 
               
FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	Bytes.toBytes("my value")
	);
list.add(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	Bytes.toBytes("my other value")
	);
list.add(filter2);
scan.setFilter(list);
 
               
 
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.4.2. 列值
 
                
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.2.1. SingleColumnValueFilter
 
                 
 
                
 
               
 
               
 SingleColumnValueFilter 用于测试列值相等 (CompareOp.EQUAL ), 不等 (CompareOp.NOT_EQUAL),或范围 (e.g., CompareOp.GREATER). 下面示例检查列值和字符串'my value' 相等...
 
               
SingleColumnValueFilter filter = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	Bytes.toBytes("my value")
	);
scan.setFilter(filter);
 
               
 
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.4.3. 列值比较器
 
                
 
               
 
              
 
              
 过滤器包内有好几种比较器类需要特别提及。这些比较器和其他过滤器一起使用, 如 Section 9.4.2.1, “SingleColumnValueFilter”.
 
              
 
               
 
                
 
                 
 
                  
 9.4.3.1. RegexStringComparator
 
                 
 
                
 
               
 
               
 RegexStringComparator 支持值比较的正则表达式 。
 
               
RegexStringComparator comp = new RegexStringComparator("my.");   // any value that starts with 'my'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	comp
	);
scan.setFilter(filter);
 
               
 参考 Oracle JavaDoc 了解 supported RegEx patterns in Java.
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.3.2. SubstringComparator
 
                 
 
                
 
               
 
               
 SubstringComparator 用于检测一个子串是否存在于值中。大小写不敏感。
 
               
SubstringComparator comp = new SubstringComparator("y val");   // looking for 'my value'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	comp
	);
scan.setFilter(filter);
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.3.3. BinaryPrefixComparator
 
                 
 
                
 
               
 
               
 参考 BinaryPrefixComparator.
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.3.4. BinaryComparator
 
                 
 
                
 
               
 
               
 参考 BinaryComparator.
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.4.4. 键值元数据
 
                
 
               
 
              
 
              
 由于HBase 采用键值对保存内部数据，键值元数据过滤器评估一行的键是否存在(如 ColumnFamily:Column qualifiers) , 对应前节所述值的情况。
 
              
 
               
 
                
 
                 
 
                  
 9.4.4.1. FamilyFilter
 
                 
 
                
 
               
 
               
 FamilyFilter 用于过滤列族。 通常，在Scan中选择ColumnFamilie优于在过滤器中做。
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.4.2. QualifierFilter
 
                 
 
                
 
               
 
               
 QualifierFilter 用于基于列名(即 Qualifier)过滤.
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.4.3. ColumnPrefixFilter
 
                 
 
                
 
               
 
               
 ColumnPrefixFilter 可基于列名(即Qualifier)前缀过滤。
 
               
 A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family. It can be used to efficiently get a subset of the columns in very wide rows.
 
               
 Note: The same column qualifier can be used in different column families. This filter returns all matching columns.
 
               
 Example: Find all columns in a row and family that start with "abc"
 
               
HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] prefix = Bytes.toBytes("abc");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnPrefixFilter(prefix);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();
 
               
 
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.4.4. MultipleColumnPrefixFilter
 
                 
 
                
 
               
 
               
 MultipleColumnPrefixFilter 和 ColumnPrefixFilter 行为差不多，但可以指定多个前缀。
 
               
 Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes. It can be used to efficiently get discontinuous sets of columns from very wide rows.
 
               
 Example: Find all columns in a row and family that start with "abc" or "xyz"
 
               
HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new MultipleColumnPrefixFilter(prefixes);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();
 
               
 
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.4.5. ColumnRangeFilter
 
                 
 
                
 
               
 
               
 ColumnRangeFilter 可以进行高效内部扫描。
 
               
 A ColumnRangeFilter can seek ahead to the first matching column for each involved column family. It can be used to efficiently get a 'slice' of the columns of a very wide row. i.e. you have a million columns in a row but you only want to look at columns bbbb-bbdd.
 
               
 Note: The same column qualifier can be used in different column families. This filter returns all matching columns.
 
               
 Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)
 
               
HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] startColumn = Bytes.toBytes("bbbb");
byte[] endColumn = Bytes.toBytes("bbdd");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();
 
               
 
 
               
 Note: HBase 0.92 引入
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.4.5. RowKey
 
                
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.5.1. RowFilter
 
                 
 
                
 
               
 
               
 通常认为行选择时Scan采用 startRow/stopRow 方法比较好。然而 RowFilter 也可以用。
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.4.6. Utility
 
                
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.4.6.1. FirstKeyOnlyFilter
 
                 
 
                
 
               
 
               
 This is primarily used for rowcount jobs. 参考 FirstKeyOnlyFilter.
 
              
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 9.5. 主服务器
 
               
 
              
 
             
 
             
 HMaster is the implementation of the Master Server. The Master server is responsible for monitoring all RegionServer instances in the cluster, and is the interface for all metadata changes. In a distributed cluster, the Master typically runs on the Section 9.9.1, “NameNode”.
 
             
 
              
 
               
 
                
 
                 
 9.5.1. Startup Behavior
 
                
 
               
 
              
 
              
 If run in a multi-Master environment, all Masters compete to run the cluster. If the active Master loses it's lease in ZooKeeper (or the Master shuts down), then then the remaining Masters jostle to take over the Master role.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.5.2. Runtime Impact
 
                
 
               
 
              
 
              
 A common dist-list question is what happens to an HBase cluster when the Master goes down. Because the HBase client talks directly to the RegionServers, the cluster can still function in a "steady state." Additionally, per Section 9.2, “Catalog Tables” ROOT and META exist as HBase tables (i.e., are not resident in the Master). However, the Master controls critical functions such as RegionServer failover and completing region splits. So while the cluster can still run for a time without the Master, the Master should be restarted as soon as possible.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.5.3. Interface
 
                
 
               
 
              
 
              
 The methods exposed by HMasterInterface are primarily metadata-oriented methods:
 
              
 
               
 
                
Table (createTable, modifyTable, removeTable, enable, disable)
 
                
ColumnFamily (addColumn, modifyColumn, removeColumn)
 
                
Region (move, assign, unassign)
 
               
 
              
 
              
 For example, when the HBaseAdmin method disableTable is invoked, it is serviced by the Master server.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.5.4. 进程
 
                
 
               
 
              
 
              
 Master 后台运行几种线程:
 
              
 
               
 
                
 
                 
 
                  
 9.5.4.1. LoadBalancer
 
                 
 
                
 
               
 
               
 Periodically, and when there are not any regions in transition, a load balancer will run and move regions around to balance cluster load. 参考 Section 2.8.3.1, “Balancer” for configuring this property.
 
               
 参考 Section 9.7.2, “Region-RegionServer Assignment” for more information on region assignment.
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.5.4.2. CatalogJanitor
 
                 
 
                
 
               
 
               
 Periodically checks and cleans up the .META. table. 参考 Section 9.2.2, “META” for more information on META.
 
              
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 9.6. RegionServer
 
               
 
              
 
             
 
             
 HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions. In a distributed cluster, a RegionServer runs on a Section 9.9.2, “DataNode”.
 
             
 
              
 
               
 
                
 
                 
 9.6.1. 接口
 
                
 
               
 
              
 
              
 The methods exposed by HRegionRegionInterface contain both data-oriented and region-maintenance methods:
 
              
 
               
 
                
Data (get, put, delete, next, etc.)
 
                
Region (splitRegion, compactRegion, etc.)
 
               
 
              
 
              
 For example, when the HBaseAdmin method majorCompact is invoked on a table, the client is actually iterating through all regions for the specified table and requesting a major compaction directly to each region.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.6.2. 进程
 
                
 
               
 
              
 
              
 RegionServer 后台运行几种线程:
 
              
 
               
 
                
 
                 
 
                  
 9.6.2.1. CompactSplitThread
 
                 
 
                
 
               
 
               
 检查分割并处理最小紧缩。
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.2.2. MajorCompactionChecker
 
                 
 
                
 
               
 
               
 检查主紧缩。
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.2.3. MemStoreFlusher
 
                 
 
                
 
               
 
               
 周期将写到内存存储的内容刷到文件存储。
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.2.4. LogRoller
 
                 
 
                
 
               
 
               
 周期检查RegionServer 的 HLog.
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.6.3. 协处理器
 
                
 
               
 
              
 
              
 协处理器在0.92版添加。 有一个详细帖子 Blog Overview of CoProcessors 供参考。文档最终会放到本参考手册，但该blog是当前能获取的大部分信息。
 
             
 
             
 
              
 
               
 
                
 
                 
 9.6.4. 块缓存
 
                
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.4.1. 设计
 
                 
 
                
 
               
 
               
 The Block Cache is an LRU cache that contains three levels of block priority to allow for scan-resistance and in-memory ColumnFamilies:
 
               
 
                
 
                 
Single access priority: The first time a block is loaded from HDFS it normally has this priority and it will be part of the first group to be considered during evictions. The advantage is that scanned blocks are more likely to get evicted than blocks that are getting more usage.
 
                 
Mutli access priority: If a block in the previous priority group is accessed again, it upgrades to this priority. It is thus part of the second group considered during evictions.
 
                 
In-memory access priority: If the block's family was configured to be "in-memory", it will be part of this priority disregarding the number of times it was accessed. Catalog tables are configured like this. This group is the last one considered during evictions.
 
                
 
               
 
               
 For more information, see the LruBlockCache source
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.4.2. 使用
 
                 
 
                
 
               
 
               
 Block caching is enabled by default for all the user tables which means that any read operation will load the LRU cache. This might be good for a large number of use cases, but further tunings are usually required in order to achieve better performance. An important concept is the working set size, or WSS, which is: "the amount of memory needed to compute the answer to a problem". For a website, this would be the data that's needed to answer the queries over a short amount of time.
 
               
 The way to calculate how much memory is available in HBase for caching is:
 
               
            number of region servers * heap size * hfile.block.cache.size * 0.85
        
 
               
 The default value for the block cache is 0.25 which represents 25% of the available heap. The last value (85%) is the default acceptable loading factor in the LRU cache after which eviction is started. The reason it is included in this equation is that it would be unrealistic to say that it is possible to use 100% of the available memory since this would make the process blocking from the point where it loads new blocks. Here are some examples:
 
               
 
                
 
                 
One region server with the default heap size (1GB) and the default block cache size will have 217MB of block cache available.
 
                 
20 region servers with the heap size set to 8GB and a default block cache size will have 34GB of block cache.
 
                 
100 region servers with the heap size set to 24GB and a block cache size of 0.5 will have about 1TB of block cache.
 
                
 
               
 
               
 Your data isn't the only resident of the block cache, here are others that you may have to take into account:
 
               
 
                
 
                 
Catalog tables: The -ROOT- and .META. tables are forced into the block cache and have the in-memory priority which means that they are harder to evict. The former never uses more than a few hundreds of bytes while the latter can occupy a few MBs (depending on the number of regions).
 
                 
HFiles indexes: HFile is the file format that HBase uses to store data in HDFS and it contains a multi-layered index in order seek to the data without having to read the whole file. The size of those indexes is a factor of the block size (64KB by default), the size of your keys and the amount of data you are storing. For big data sets it's not unusual to see numbers around 1GB per region server, although not all of it will be in cache because the LRU will evict indexes that aren't used.
 
                 
Keys: Taking into account only the values that are being stored is missing half the picture since every value is stored along with its keys (row key, family, qualifier, and timestamp). 参考 Section 6.3.2, “Try to minimize row and column sizes”.
 
                 
Bloom filters: Just like the HFile indexes, those data structures (when enabled) are stored in the LRU.
 
                
 
               
 
               
 Currently the recommended way to measure HFile indexes and bloom filters sizes is to look at the region server web UI and checkout the relevant metrics. For keys, sampling can be done by using the HFile command line tool and look for the average key size metric.
 
               
 It's generally bad to use block caching when the WSS doesn't fit in memory. This is the case when you have for example 40GB available across all your region servers' block caches but you need to process 1TB of data. One of the reasons is that the churn generated by the evictions will trigger more garbage collections unnecessarily. Here are two use cases:
 
               
 
                
 
                 
Fully random reading pattern: This is a case where you almost never access the same row twice within a short amount of time such that the chance of hitting a cached block is close to 0. Setting block caching on such a table is a waste of memory and CPU cycles, more so that it will generate more garbage to pick up by the JVM. For more information on monitoring GC, see Section 12.2.3, “JVM Garbage Collection Logs”.
 
                 
Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read only once so there's no need to put them into the block cache. The Scan object has the option of turning this off via the setCaching method (set it to false). You can still keep block caching turned on on this table if you need fast random read access. An example would be counting the number of rows in a table that serves live traffic, caching every block of that table would create massive churn and would surely evict data that's currently in use.
 
                
 
               
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.6.5. 预写日志 (WAL)
 
                
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.5.1. Purpose
 
                 
 
                
 
               
 
               
 每个RegionServer会将更新(Puts, Deletes) 先记录到预写日志中(WAL)，然后将其更新在Section 9.7.5, “Store”的Section 9.7.5.1, “MemStore”里面。这样就保证了HBase的写的可靠性。如果没有WAL,当RegionServer宕掉的时候，MemStore还没有flush，StoreFile还没有保存，数据就会丢失。HLog 是HBase的一个WAL实现，一个RegionServer有一个HLog实例。
 WAL 保存在HDFS 的  /hbase/.logs/ 里面，每个region一个文件。 
               
 要想知道更多的信息，可以访问维基百科 Write-Ahead Log 的文章.
 
               
  
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.5.2. WAL Flushing
 
                 
 
                
 
               
 
               
 TODO (describe).
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.6.5.3. WAL Splitting
 
                 
 
                
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.6.5.3.1. 当RegionServer宕掉的时候，如何恢复
 
                  
 
                 
 
                
 
                
 When a RegionServer crashes, it will lose its ephemeral lease in ZooKeeper...TODO
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.6.5.3.2. hbase.hlog.split.skip.errors
 
                  
 
                 
 
                
 
                
 默认设置为 true,在split执行中发生的任何错误会被记录，有问题的WAL会被移动到HBase rootdir目录下的.corrupt目录，接着进行处理。如果设置为 false，异常会被抛出，split会记录错误。[23]
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.6.5.3.3. 如何处理一个发生在当RegionServers' WALs 分割时候的EOFExceptions异常
 
                  
 
                 
 
                
 
                
 如果我们在分割日志的时候发生EOF,就是hbase.hlog.split.skip.errors设置为 false，我们也会进行处理。一个EOF会发生在一行一行读取Log，但是Log中最后一行似乎只写了一半就停止了。如果在处理过程中发生了EOF，我们还会继续处理，除非这个文件是要处理的最后一个文件。[24]
 
               
 
              
 
             
 
             
 
              
 
              
 
              
 
               
 [23] 参考 HBASE-2958 When hbase.hlog.split.skip.errors is set to false, we fail the split but thats it. We need to do more than just fail split if this flag is set.
 
              
 
              
 
               
 [24] 要想知道背景知识, 参见HBASE-2643 Figure how to deal with eof splitting logs
 
              
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 9.7. 区域
 
               
 
              
 
             
 
             
 区域是表获取和分布的基本元素，由每个列族的一个库(Store)组成。对象层级图如下：
 
             
Table       (HBase table)      
    Region       (Regions for the table)
         Store          (Store per ColumnFamily for each Region for the table)
              MemStore           (MemStore for each Store for each Region for the table)
              StoreFile          (StoreFiles for each Store for each Region for the table)
                    Block             (Blocks within a StoreFile within a Store for each Region for the table)
 
 
             
 关于HBase文件写到HDFS的描述，参考 Section 12.7.2, “浏览 HDFS的 HBase 对象”.
 
             
 
              
 
               
 
                
 
                 
 9.7.1. Region 大小
 
                
 
               
 
              
 
              
 Region的大小是一个棘手的问题，需要考量如下几个因素。
 
              
 
               
 
                
 
 Regions是可用性和分布式的最基本单位
 
 
                
 
 HBase通过将region切分在许多机器上实现分布式。也就是说，你如果有16GB的数据，只分了2个region， 你却有20台机器，有18台就浪费了。
 
 
                
 
 region数目太多就会造成性能下降，现在比以前好多了。但是对于同样大小的数据，700个region比3000个要好。
 
 
                
 
 region数目太少就会妨碍可扩展性，降低并行能力。有的时候导致压力不够分散。这就是为什么，你向一个10节点的HBase集群导入200MB的数据，大部分的节点是idle的。
 
 
                
 
 RegionServer中1个region和10个region索引需要的内存量没有太多的差别。
 
 
               
 
              
 
              
 最好是使用默认的配置，可以把热的表配小一点(或者受到split热点的region把压力分散到集群中)。如果你的cell的大小比较大(100KB或更大)，就可以把region的大小调到1GB。
 
              
 参考 Section 2.5.2.6, “更大区域” 获取配置更多信息.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.7.2. 区域-区域服务器分配
 
                
 
               
 
              
 
              
 本节描述区域如何分配到区域服务器。
 
              
 
               
 
                
 
                 
 
                  
 9.7.2.1. 启动
 
                 
 
                
 
               
 
               
 当HBase启动时，区域分配如下(短版本):
 
               
 
                
 
                 
启动时主服务器调用AssignmentManager.
 
                 
AssignmentManager 在META 中查找已经存在的区域分配。
 
                 
如果区域分配还有效(如 RegionServer 还在线) ，那么分配继续保持。
 
                 
如果区域分配失效，LoadBalancerFactory 被调用来分配区域。 DefaultLoadBalancer 将随机分配区域到RegionServer.
 
                 
META 随 RegionServer 分配更新(如果需要) ， RegionServer 启动区域开启代码(RegionServer 启动时进程)
 
                
 
               
 
               
 
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.7.2.2. 故障转移
 
                 
 
                
 
               
 
               
 当区域服务器出故障退出时 (短版本):
 
               
 
                
 
                 
区域立即不可获取，因为区域服务器退出。
 
                 
主服务器会检测到区域服务器退出。
 
                 
区域分配会失效并被重新分配，如同启动时序。
 
                
 
               
 
               
 
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.7.2.3. 区域负载均衡
 
                 
 
                
 
               
 
               
 区域可以定期移动，见 Section 9.5.4.1, “LoadBalancer”.
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.7.3. 区域-区域服务器本地化
 
                
 
               
 
              
 
              
 Over time, Region-RegionServer locality is achieved via HDFS block replication. The HDFS client does the following by default when choosing locations to write replicas:
 
              
 
               
 
                
First replica is written to local node
 
                
Second replica is written to another node in same rack
 
                
Third replica is written to a node in another rack (if sufficient nodes)
 
               
 
              
 
              
 Thus, HBase eventually achieves locality for a region after a flush or a compaction. In a RegionServer failover situation a RegionServer may be assigned regions with non-local StoreFiles (because none of the replicas are local), however as new data is written in the region, or the table is compacted and StoreFiles are re-written, they will become "local" to the RegionServer.
 
              
 For more information, see HDFS Design on Replica Placement and also Lars George's blog on HBase and HDFS locality.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.7.4. 区域分割
 
                
 
               
 
              
 
              
 区域服务器的分割操作是不可见的，因为Master不会参与其中。区域服务器切割region的步骤是，先将该region下线，然后切割，将其子region加入到META元信息中，再将他们加入到原本的区域服务器中，最后汇报Master.参见Section 2.8.2.7, “管理 Splitting”来手动管理切割操作(以及为何这么做)。
 
              
 
               
 
                
 
                 
 
                  
 9.7.4.1. 自定义分割策略
 
                 
 
                
 
               
 
               
 缺省分割策略可以被重写，采用自定义RegionSplitPolicy (HBase 0.94+).一般自定义分割策略应该扩展HBase的缺省分割策略: ConstantSizeRegionSplitPolicy.
 
               
 策略可以HBaseConfiguration 全局使用，或基于每张表：
 
               
HTableDescriptor myHtd = ...;
myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());
 
               
 
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.7.5. 存储
 
                
 
               
 
              
 
              
 一个存储包含了一个内存存储(MemStore)和若干个文件存储(StoreFile--HFile).一个存储可以定位到一个列族中的一个区.
 
              
 
               
 
                
 
                 
 
                  
 9.7.5.1. MemStore
 
                 
 
                
 
               
 
               
 MemStores是Store中的内存Store,可以进行修改操作。修改的内容是KeyValues。当flush的是，现有的memstore会生成快照，然后清空。在执行快照的时候，HBase会继续接收修改操作，保存在memstore外面，直到快照完成。
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.7.5.2. StoreFile (HFile)
 
                 
 
                
 
               
 
               
 文件存储是数据存在的地方。
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.2.1. HFile Format
 
                  
 
                 
 
                
 
                
 hfile文件格式是基于BigTable [2006]论文中的SSTable。构建在Hadoop的tfile上面(直接使用了tfile的单元测试和压缩工具)。 Schubert Zhang 的博客HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs详细介绍了HBases的hfile。Matteo Bertozzi也做了详细的介绍HBase I/O: HFile。
 
                
 For more information, see the HFile source code. Also see Appendix E, HFile format version 2 for information about the HFile v2 format that was included in 0.92.
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.2.2. HFile 工具
 
                  
 
                 
 
                
 
                
 要想看到hfile内容的文本化版本，你可以使用org.apache.hadoop.hbase.io.hfile.HFile 工具。可以这样用：
 
                
$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile  
 
                
 例如，你想看文件 hdfs://10.81.47.41:9000/hbase/TEST/1418428042/DSMP/4759508618286845475的内容, 就执行如下的命令:
 
                
 $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile -v -f hdfs://10.81.47.41:9000/hbase/TEST/1418428042/DSMP/4759508618286845475  
 
                
 如果你没有输入-v,就仅仅能看到一个hfile的汇总信息。其他功能的用法可以看HFile的文档。
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.2.3. StoreFile Directory Structure on HDFS
 
                  
 
                 
 
                
 
                
 For more information of what StoreFiles look like on HDFS with respect to the directory structure, see Section 12.7.2, “Browsing HDFS for HBase Objects”.
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.7.5.3. Blocks
 
                 
 
                
 
               
 
               
 StoreFiles are composed of blocks. The blocksize is configured on a per-ColumnFamily basis.
 
               
 Compression happens at the block level within StoreFiles. For more information on compression, see Appendix C, Compression In HBase.
 
               
 For more information on blocks, see the HFileBlock source code.
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.7.5.4. KeyValue
 
                 
 
                
 
               
 
               
 The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes offsets and lengths into passed array at where to start interpreting the content as KeyValue.
 
               
 The KeyValue format inside a byte array is:
 
               
 
                
 
                 
keylength
 
                 
valuelength
 
                 
key
 
                 
value
 
                
 
               
 
               
 
 
               
 The Key is further decomposed as:
 
               
 
                
 
                 
rowlength
 
                 
row (i.e., the rowkey)
 
                 
columnfamilylength
 
                 
columnfamily
 
                 
columnqualifier
 
                 
timestamp
 
                 
keytype (e.g., Put, Delete, DeleteColumn, DeleteFamily)
 
                
 
               
 
               
 
 
               
 KeyValue instances are not split across blocks. For example, if there is an 8 MB KeyValue, even if the block-size is 64kb this KeyValue will be read in as a coherent block. For more information, see the KeyValue source code.
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.4.1. Example
 
                  
 
                 
 
                
 
                
 To emphasize the points above, examine what happens with two Puts for two different columns for the same row:
 
                
 
                 
 
                  
Put #1: rowkey=row1, cf:attr1=value1
 
                  
Put #2: rowkey=row1, cf:attr2=value2
 
                 
 
                
 
                
 Even though these are for the same row, a KeyValue is created for each column:
 
                
 Key portion for Put #1:
 
                
 
                 
 
                  
rowlength ------------> 4
 
                  
row -----------------> row1
 
                  
columnfamilylength ---> 2
 
                  
columnfamily --------> cf
 
                  
columnqualifier ------> attr1
 
                  
timestamp -----------> server time of Put
 
                  
keytype -------------> Put
 
                 
 
                
 
                
 
 
                
 Key portion for Put #2:
 
                
 
                 
 
                  
rowlength ------------> 4
 
                  
row -----------------> row1
 
                  
columnfamilylength ---> 2
 
                  
columnfamily --------> cf
 
                  
columnqualifier ------> attr2
 
                  
timestamp -----------> server time of Put
 
                  
keytype -------------> Put
 
                 
 
                
 
                
 
 
               
 
               
 It is critical to understand that the rowkey, ColumnFamily, and column (aka columnqualifier) are embedded within the KeyValue instance. The longer these identifiers are, the bigger the KeyValue is.
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.7.5.5. 紧缩
 
                 
 
                
 
               
 
               
 有两种类型的紧缩：次紧缩和主紧缩。minor紧缩通常会将数个小的相邻的文件合并成一个大的。Minor不会删除打上删除标记的数据，也不会删除过期的数据，Major紧缩会删除过期的数据。有些时候minor紧缩就会将一个store中的全部文件紧缩，实际上这个时候他本身就是一个major压‹缩。对于一个minor紧缩是如何紧缩的，可以参见ascii diagram in the Store source code.
 
               
 在执行一个major紧缩之后，一个store只会有一个sotrefile,通常情况下这样可以提供性能。注意：major紧缩将会将store中的数据全部重写，在一个负载很大的系统中，这个操作是很伤的。所以在大型系统中，通常会自己Section 2.8.2.8, “管理 Splitting”。
 
               
 紧缩 不会 进行分区合并。参考 Section 14.2.2, “Merge” 获取更多合并的信息。
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.5.1. Compaction File Selection
 
                  
 
                 
 
                
 
                
 To understand the core algorithm for StoreFile selection, there is some ASCII-art in the Store source code that will serve as useful reference. It has been copied below:
 
                
/* normal skew:
 *
 *         older ----> newer
 *     _
 *    | |   _
 *    | |  | |   _
 *  --|-|- |-|- |-|---_-------_-------  minCompactSize
 *    | |  | |  | |  | |  _  | |
 *    | |  | |  | |  | | | | | |
 *    | |  | |  | |  | | | | | |
 */
 
                
 Important knobs:
 
                
 
                 
 
                  
hbase.store.compaction.ratio Ratio used in compaction file selection algorithm (default 1.2f).
 
                  
hbase.hstore.compaction.min (.90 hbase.hstore.compactionThreshold) (files) Minimum number of StoreFiles per Store to be selected for a compaction to occur (default 2).
 
                  
hbase.hstore.compaction.max (files) Maximum number of StoreFiles to compact per minor compaction (default 10).
 
                  
hbase.hstore.compaction.min.size (bytes) Any StoreFile smaller than this setting with automatically be a candidate for compaction. Defaults to hbase.hregion.memstore.flush.size (128 mb).
 
                  
hbase.hstore.compaction.max.size (.92) (bytes) Any StoreFile larger than this setting with automatically be excluded from compaction (default Long.MAX_VALUE).
 
                 
 
                
 
                
 
 
                
 The minor compaction StoreFile selection logic is size based, and selects a file for compaction when the file <= sum(smaller_files) * hbase.hstore.compaction.ratio.
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.5.2. Minor Compaction File Selection - Example #1 (Basic Example)
 
                  
 
                 
 
                
 
                
 This example mirrors an example from the unit test TestCompactSelection.
 
                
 
                 
 
                  
hbase.store.compaction.ratio = 1.0f
 
                  
hbase.hstore.compaction.min = 3 (files)
 
                  
hbase.hstore.compaction.max = 5 (files)
 
                  
hbase.hstore.compaction.min.size = 10 (bytes)
 
                  
hbase.hstore.compaction.max.size = 1000 (bytes)
 
                 
 
                
 
                
 The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.
 
                
 Why?
 
                
 
                 
 
                  
100 --> No, because sum(50, 23, 12, 12) * 1.0 = 97.
 
                  
50 --> No, because sum(23, 12, 12) * 1.0 = 47.
 
                  
23 --> Yes, because sum(12, 12) * 1.0 = 24.
 
                  
12 --> Yes, because the previous file has been included, and because this does not exceed the the max-file limit of 5
 
                  
12 --> Yes, because the previous file had been included, and because this does not exceed the the max-file limit of 5.
 
                 
 
                
 
                
 
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.5.3. Minor Compaction File Selection - Example #2 (Not Enough Files To Compact)
 
                  
 
                 
 
                
 
                
 This example mirrors an example from the unit test TestCompactSelection.
 
                
 
                 
 
                  
hbase.store.compaction.ratio = 1.0f
 
                  
hbase.hstore.compaction.min = 3 (files)
 
                  
hbase.hstore.compaction.max = 5 (files)
 
                  
hbase.hstore.compaction.min.size = 10 (bytes)
 
                  
hbase.hstore.compaction.max.size = 1000 (bytes)
 
                 
 
                
 
                
 
 
                
 The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.
 
                
 Why?
 
                
 
                 
 
                  
100 --> No, because sum(25, 12, 12) * 1.0 = 47
 
                  
25 --> No, because sum(12, 12) * 1.0 = 24
 
                  
12 --> No. Candidate because sum(12) * 1.0 = 12, there are only 2 files to compact and that is less than the threshold of 3
 
                  
12 --> No. Candidate because the previous StoreFile was, but there are not enough files to compact
 
                 
 
                
 
                
 
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.5.4. Minor Compaction File Selection - Example #3 (Limiting Files To Compact)
 
                  
 
                 
 
                
 
                
 This example mirrors an example from the unit test TestCompactSelection.
 
                
 
                 
 
                  
hbase.store.compaction.ratio = 1.0f
 
                  
hbase.hstore.compaction.min = 3 (files)
 
                  
hbase.hstore.compaction.max = 5 (files)
 
                  
hbase.hstore.compaction.min.size = 10 (bytes)
 
                  
hbase.hstore.compaction.max.size = 1000 (bytes)
 
                 
 
                
 
                
 The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 7, 6, 5, 4, 3.
 
                
 Why?
 
                
 
                 
 
                  
7 --> Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than the min-size
 
                  
6 --> Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than the min-size.
 
                  
5 --> Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the min-size.
 
                  
4 --> Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the min-size.
 
                  
3 --> Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the min-size.
 
                  
2 --> No. Candidate because previous file was selected and 2 is less than the min-size, but the max-number of files to compact has been reached.
 
                  
1 --> No. Candidate because previous file was selected and 1 is less than the min-size, but max-number of files to compact has been reached.
 
                 
 
                
 
                
 
 
               
 
               
 
                
 
                 
 
                  
 
                   
 9.7.5.5.5. Impact of Key Configuration Options
 
                  
 
                 
 
                
 
                
 hbase.store.compaction.ratio. A large ratio (e.g., 10) will produce a single giant file. Conversely, a value of .25 will produce behavior similar to the BigTable compaction algorithm - resulting in 4 StoreFiles.
 
                
 hbase.hstore.compaction.min.size. Because this limit represents the "automatic include" limit for all StoreFiles smaller than this value, this value may need to be adjusted downwards in write-heavy environments where many 1 or 2 mb StoreFiles are being flushed, because every file will be targeted for compaction and the resulting files may still be under the min-size and require further compaction, etc.
 
               
 
              
 
             
 
             
 
              
 
               
 
                
 
               
 
              
 
              
 
              
 
             
 
             
 
              
 
              
 
              
 
               
 [25] For description of the development process -- why static blooms rather than dynamic -- and for an overview of the unique properties that pertain to blooms in HBase, as well as possible future directions, see the Development Process section of the document BloomFilters in HBase attached to HBase-1200.
 
              
 
              
 
               
 [26] The bloom filters described here are actually version two of blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the European Commission One-Lab Project 034819. The core of the HBase bloom work was later pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. Version 2 is a rewrite from scratch though again it starts with the one-lab work.
 
              
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 9.8. 批量装载(Bulk Loading)
 
               
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.8.1. 概述
 
                
 
               
 
              
 
              
 HBase 有好几种方法将数据装载到表。最直接的方式即可以通过MapReduce任务，也可以通过普通客户端API。但是这都不是高效方法。
 
              
 批量装载特性采用 MapReduce 任务，将表数据输出为HBase的内部数据格式，然后可以将产生的存储文件直接装载到运行的集群中。批量装载比简单使用 HBase API 消耗更少的CPU和网络资源。
 
             
 
             
 
              
 
               
 
                
 
                 
 9.8.2. 批量装载架构
 
                
 
               
 
              
 
              
 HBase 批量装载过程包含两个主要步骤。
 
              
 
               
 
                
 
                 
 
                  
 9.8.2.1. 通过MapReduce 任务准备数据
 
                 
 
                
 
               
 
               
 批量装载第一步，从MapReduce任务通过HFileOutputFormat产生HBase数据文件(StoreFiles) 。输出数据为HBase的内部数据格式，以便随后装载到集群更高效。
 
               
 为了处理高效， HFileOutputFormat 必须比配置为每个HFile适合在一个分区内。为了做到这一点，输出将被批量装载到HBase的任务，使用Hadoop 的TotalOrderPartitioner 类来分开map输出为分开的键空间区间。对应于表内每个分区(region)的键空间。
 
               
 HFileOutputFormat 包含一个方便的函数, configureIncrementalLoad(), 可以基于表当前分区边界自动设置TotalOrderPartitioner。
 
              
 
              
 
               
 
                
 
                 
 
                  
 9.8.2.2. 完成数据装载
 
                 
 
                
 
               
 
               
 After the data has been prepared using HFileOutputFormat, it is loaded into the cluster using completebulkload. This command line tool iterates through the prepared data files, and for each one determines the region the file belongs to. It then contacts the appropriate Region Server which adopts the HFile, moving it into its storage directory and making the data available to clients.
 
               
 If the region boundaries have changed during the course of bulk load preparation, or between the preparation and completion steps, the completebulkloads utility will automatically split the data files into pieces corresponding to the new boundaries. This process is not optimally efficient, so users should take care to minimize the delay between preparing a bulk load and importing it into the cluster, especially if other clients are simultaneously loading data through other means.
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 9.8.3. 采用completebulkload 工具导入准备的数据
 
                
 
               
 
              
 
              
 After a data import has been prepared, either by using the importtsv tool with the "importtsv.bulk.output" option or by some other MapReduce job using the HFileOutputFormat, the completebulkload tool is used to import the data into the running cluster.
 
              
 The completebulkload tool simply takes the output path where importtsv or your MapReduce job put its results, and the table name to import into. For example:
 $ hadoop jar hbase-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable 
              
 The -c config-file option can be used to specify a file containing the appropriate hbase parameters (e.g., hbase-site.xml) if not supplied already on the CLASSPATH (In addition, the CLASSPATH must contain the directory that has the zookeeper configuration file if zookeeper is NOT managed by HBase).
 
              
 Note: If the target table does not already exist in HBase, this tool will create the table automatically.
 
              
 This tool will run quickly, after which point the new data will be visible in the cluster.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.8.4. 参考
 
                
 
               
 
              
 
              
 For more information about the referenced utilities, see Section 14.1.9, “ImportTsv” and Section 14.1.10, “CompleteBulkLoad”.
 
             
 
             
 
              
 
               
 
                
 
                 
 9.8.5. 高级使用
 
                
 
               
 
              
 
              
 Although the importtsv tool is useful in many cases, advanced users may want to generate data programatically, or import data from other formats. To get started doing so, dig into ImportTsv.java and check the JavaDoc for HFileOutputFormat.
 
              
 The import step of the bulk load can also be done programatically. 参考 the LoadIncrementalHFiles class 获取更多信息。
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 9.9. HDFS
 
               
 
              
 
             
 
             
 由于 HBase 在 HDFS 上运行(每个存储文件也被写为HDFS的文件)，必须理解 HDFS 结构，特别是它如何存储文件，处理故障转移，备份块。
 
             
 参考 Hadoop 文档 HDFS Architecture 获取更多信息。
 
             
 
              
 
               
 
                
 
                 
 9.9.1. NameNode
 
                
 
               
 
              
 
              
 NameNode 负责维护文件系统元数据。参考上述HDFS结构链接获取更多信息。
 
             
 
             
 
              
 
               
 
                
 
                 
 9.9.2. DataNode
 
                
 
               
 
              
 
              
 DataNode 负责存储HDFS 块。 参考上述HDFS结构链接获取更多信息。
 
             
 
            
 
            
 
             
 
              
 
               
 
                
  
 
                
 
                
 
                 
 [23] 参考 HBASE-2958 When hbase.hlog.split.skip.errors is set to false, we fail the split but thats it. We need to do more than just fail split if this flag is set.
 
                
 
                
 
                 
 [24] For background, see HBASE-2643 Figure how to deal with eof splitting logs
 
                
 
                
 
                 
 [25] For description of the development process -- why static blooms rather than dynamic -- and for an overview of the unique properties that pertain to blooms in HBase, as well as possible future directions, see the Development Process section of the documentBloomFilters in HBase attached to HBase-1200.
 
                
 
                
 
                 
 [26] The bloom filters described here are actually version two of blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the European Commission One-Lab Project 034819. The core of the HBase bloom work was later pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. Version 2 is a rewrite from scratch though again it starts with the one-lab work.
 
                
 
                
 Chapter 10. 外部 API
 
               
 
              
 
             
 
              This chapter will cover access to HBase either through non-Java languages, or through custom protocols. 
             
 
              
 
               
 
                
 
                 
 10.1. 非Java 语言和 JVM 通话
 
                
 
               
 
              
 
              
 当前本话题大部分文档在 HBase Wiki. 参考 Thrift API Javadoc.
 
             
 
            
 
            
 
             
 
              
 
               
 
                
 10.2. REST
 
               
 
              
 
             
 
             
 当前 REST大部分文档在 HBase Wiki on REST.
 
            
 
            
 
             
 
              
 
               
 
                
 10.3. Thrift
 
               
 
              
 
             
 
             
 当前 Thrift大部分文档在 HBase Wiki on Thrift.
 
             
 
              
 
               
 
                
 
                 
 10.3.1. 过滤器语言
 
                
 
               
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.1. 用例
 
                 
 
                
 
               
 
               
 注意: 本特性在 HBase 0.92 中加入。
 
               
 This allows the user to perform server-side filtering when accessing HBase over Thrift. The user specifies a filter via a string. The string is parsed on the server to construct the filter
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.2. 通用过滤字符串语法
 
                 
 
                
 
               
 
               
 A simple filter expression is expressed as: “FilterName (argument, argument, ... , argument)”
 
               
 You must specify the name of the filter followed by the argument list in parenthesis. Commas separate the individual arguments
 
               
 If the argument represents a string, it should be enclosed in single quotes.
 
               
 If it represents a boolean, an integer or a comparison operator like <, >, != etc. it should not be enclosed in quotes
 
               
 The filter name must be one word. All ASCII characters are allowed except for whitespace, single quotes and parenthesis.
 
               
 The filter’s arguments can contain any ASCII character. If single quotes are present in the argument, they must be escaped by a preceding single quote
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.3. Compound Filters and Operators
 
                 
 
                
 
               
 
               
 Currently, two binary operators – AND/OR and two unary operators – WHILE/SKIP are supported.
 
               
 Note: the operators are all in uppercase
 
               
 AND – as the name suggests, if this operator is used, the key-value must pass both the filters
 
               
 OR – as the name suggests, if this operator is used, the key-value must pass at least one of the filters
 
               
 SKIP – For a particular row, if any of the key-values don’t pass the filter condition, the entire row is skipped
 
               
 WHILE - For a particular row, it continues to emit key-values until a key-value is reached that fails the filter condition
 
               
 Compound Filters: Using these operators, a hierarchy of filters can be created. For example: “(Filter1 AND Filter2) OR (Filter3 AND Filter4)”
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.4. Order of Evaluation
 
                 
 
                
 
               
 
               
 Parenthesis have the highest precedence. The SKIP and WHILE operators are next and have the same precedence.The AND operator has the next highest precedence followed by the OR operator.
 
               
 For example:
 
               
 A filter string of the form:“Filter1 AND Filter2 OR Filter3” will be evaluated as:“(Filter1 AND Filter2) OR Filter3”
 
               
 A filter string of the form:“Filter1 AND SKIP Filter2 OR Filter3” will be evaluated as:“(Filter1 AND (SKIP Filter2)) OR Filter3”
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.5. 比较运算符
 
                 
 
                
 
               
 
               
 比较运算符可以是下面之一:
 
               
 
                
 
                 
 
 LESS (<)
 
 
                 
 
 LESS_OR_EQUAL (<=)
 
 
                 
 
 EQUAL (=)
 
 
                 
 
 NOT_EQUAL (!=)
 
 
                 
 
 GREATER_OR_EQUAL (>=)
 
 
                 
 
 GREATER (>)
 
 
                 
 
 NO_OP (no operation)
 
 
                
 
               
 
               
 客户端应该使用 (<, <=, =, !=, >, >=) 来表达比较操作.
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.6. 比较器(Comparator)
 
                 
 
                
 
               
 
               
 比较器可以是下面之一:
 
               
 
                
 
                 
 
 BinaryComparator - This lexicographically compares against the specified byte array using Bytes.compareTo(byte[], byte[])
 
 
                 
 
 BinaryPrefixComparator - This lexicographically compares against a specified byte array. It only compares up to the length of this byte array.
 
 
                 
 
 RegexStringComparator - This compares against the specified byte array using the given regular expression. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator
 
 
                 
 
 SubStringComparator - This tests if the given substring appears in a specified byte array. The comparison is case insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator
 
 
                
 
               
 
               
 The general syntax of a comparator is: ComparatorType:ComparatorValue
 
               
 The ComparatorType for the various comparators is as follows:
 
               
 
                
 
                 
 
 BinaryComparator - binary
 
 
                 
 
 BinaryPrefixComparator - binaryprefix
 
 
                 
 
 RegexStringComparator - regexstring
 
 
                 
 
 SubStringComparator - substring
 
 
                
 
               
 
               
 The ComparatorValue can be any value.
 
               
 Example1: >, 'binary:abc' will match everything that is lexicographically greater than "abc"
 
               
 Example2: =, 'binaryprefix:abc' will match everything whose first 3 characters are lexicographically equal to "abc"
 
               
 Example3: !=, 'regexstring:ab*yz' will match everything that doesn't begin with "ab" and ends with "yz"
 
               
 Example4: =, 'substring:abc123' will match everything that begins with the substring "abc123"
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.7.  PHP 客户端编程使用过滤器示例
 
                 
 
                
 
               
 
               
', );
   $hbase->open();
   $client = $hbase->getClient();
   $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
   $to_print = $client->scannerGetList($result,1);
   while ($to_print) {
      print_r($to_print);
      $to_print = $client->scannerGetList($result,1);
    }
   $client->scannerClose($result);
?>
        
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.8. 过滤字符串示例
 
                 
 
                
 
               
 
               
 
 
               
 
                
 
                 
 
 “PrefixFilter (‘Row’) AND PageFilter (1) AND FirstKeyOnlyFilter ()” will return all key-value pairs that match the following conditions:
 
 1) The row containing the key-value should have prefix “Row”
 
 2) The key-value must be located in the first row of the table
 
 3) The key-value pair must be the first key-value in the row
 
 
                
 
               
 
               
 
 
               
 
                
 
 
                
 
                 
 
                  
 
 “(RowFilter (=, ‘binary:Row 1’) AND TimeStampsFilter (74689, 89734)) OR ColumnRangeFilter (‘abc’, true, ‘xyz’, false))” will return all key-value pairs that match both the following conditions:
 
 1) The key-value is in a row having row key “Row 1”
 
 2) The key-value must have a timestamp of either 74689 or 89734.
 
 Or it must match the following condition:
 
 1) The key-value pair must be in a column that is lexicographically >= abc and < xyz 
 
 
                 
 
                
 
                
 
 
                
 
               
 
               
 
 
               
 
                
 
                 
 
 “SKIP ValueFilter (0)” will skip the entire row if any of the values in the row is not 0
 
 
                
 
               
 
               
 
 
              
 
              
 
               
 
                
 
                 
 
                  
 10.3.1.9. 独有过滤器语法
 
                 
 
                
 
               
 
               
 
                
 
                 
 
 KeyOnlyFilter
 
 Description: This filter doesn’t take any arguments. It returns only the key component of each key-value.
 
 Syntax: KeyOnlyFilter ()
 
 Example: "KeyOnlyFilter ()"
 
 
                 
 
 FirstKeyOnlyFilter
 
 Description: This filter doesn’t take any arguments. It returns only the first key-value from each row.
 
 Syntax: FirstKeyOnlyFilter ()
 
 Example: "FirstKeyOnlyFilter ()"
 
 
                 
 
 PrefixFilter
 
 Description: This filter takes one argument – a prefix of a row key. It returns only those key-values present in a row that starts with the specified row prefix
 
 Syntax: PrefixFilter (‘’)
 
 Example: "PrefixFilter (‘Row’)"
 
 
                 
 
 ColumnPrefixFilter
 
 Description: This filter takes one argument – a column prefix. It returns only those key-values present in a column that starts with the specified column prefix. The column prefix must be of the form: “qualifier”
 
 Syntax:ColumnPrefixFilter(‘’)
 
 Example: "ColumnPrefixFilter(‘Col’)"
 
 
                 
 
 MultipleColumnPrefixFilter
 
 Description: This filter takes a list of column prefixes. It returns key-values that are present in a column that starts with any of the specified column prefixes. Each of the column prefixes must be of the form: “qualifier”
 
 Syntax:MultipleColumnPrefixFilter(‘’, ‘’, …, ‘’)
 
 Example: "MultipleColumnPrefixFilter(‘Col1’, ‘Col2’)"
 
 
                 
 
 ColumnCountGetFilter
 
 Description: This filter takes one argument – a limit. It returns the first limit number of columns in the table
 
 Syntax: ColumnCountGetFilter (‘’)
 
 Example: "ColumnCountGetFilter (4)"
 
 
                 
 
 PageFilter
 
 Description: This filter takes one argument – a page size. It returns page size number of rows from the table.
 
 Syntax: PageFilter (‘’)
 
 Example: "PageFilter (2)"
 
 
                 
 
 ColumnPaginationFilter
 
 Description: This filter takes two arguments – a limit and offset. It returns limit number of columns after offset number of columns. It does this for all the rows
 
 Syntax: ColumnPaginationFilter(‘’, ‘’)
 
 Example: "ColumnPaginationFilter (3, 5)"
 
 
                 
 
 InclusiveStopFilter
 
 Description: This filter takes one argument – a row key on which to stop scanning. It returns all key-values present in rows up to and including the specified row
 
 Syntax: InclusiveStopFilter(‘’)
 
 Example: "InclusiveStopFilter ('Row2')"
 
 
                 
 
 TimeStampsFilter
 
 Description: This filter takes a list of timestamps. It returns those key-values whose timestamps matches any of the specified timestamps
 
 Syntax: TimeStampsFilter (, , ... ,)
 
 Example: "TimeStampsFilter (5985489, 48895495, 58489845945)"
 
 
                 
 
 RowFilter
 
 Description: This filter takes a compare operator and a comparator. It compares each row key with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that row
 
 Syntax: RowFilter (, ‘’)
 
 Example: "RowFilter (<=, ‘xyz)"
 
 
                 
 
 Family Filter
 
 Description: This filter takes a compare operator and a comparator. It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column
 
 Syntax: QualifierFilter (, ‘’)
 
 Example: "QualifierFilter (=, ‘Column1’)"
 
 
                 
 
 QualifierFilter
 
 Description: This filter takes a compare operator and a comparator. It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column
 
 Syntax: QualifierFilter (,‘’)
 
 Example: "QualifierFilter (=,‘Column1’)"
 
 
                 
 
 ValueFilter
 
 Description: This filter takes a compare operator and a comparator. It compares each value with the comparator using the compare operator and if the comparison returns true, it returns that key-value
 
 Syntax: ValueFilter (,‘’)
 
 Example: "ValueFilter (!=, ‘Value’)"
 
 
                 
 
 DependentColumnFilter
 
 Description: This filter takes two arguments – a family and a qualifier. It tries to locate this column in each row and returns all key-values in that row that have the same timestamp. If the row doesn’t contain the specified column – none of the key-values in that row will be returned.
 
 The filter can also take an optional boolean argument – dropDependentColumn. If set to true, the column we were depending on doesn’t get returned.
 
 The filter can also take two more additional optional arguments – a compare operator and a value comparator, which are further checks in addition to the family and qualifier. If the dependent column is found, its value should also pass the value check and then only is its timestamp taken into consideration
 
 Syntax: DependentColumnFilter (‘’, ‘’, , , ‘ 
 Syntax: DependentColumnFilter (‘’, ‘’, )
 
 Syntax: DependentColumnFilter (‘’, ‘’)
 
 Example: "DependentColumnFilter (‘conf’, ‘blacklist’, false, >=, ‘zebra’)"
 
 Example: "DependentColumnFilter (‘conf’, 'blacklist', true)"
 
 Example: "DependentColumnFilter (‘conf’, 'blacklist')"
 
 
                 
 
 SingleColumnValueFilter
 
 Description: This filter takes a column family, a qualifier, a compare operator and a comparator. If the specified column is not found – all the columns of that row will be emitted. If the column is found and the comparison with the comparator returns true, all the columns of the row will be emitted. If the condition fails, the row will not be emitted.
 
 This filter also takes two additional optional boolean arguments – filterIfColumnMissing and setLatestVersionOnly
 
 If the filterIfColumnMissing flag is set to true the columns of the row will not be emitted if the specified column to check is not found in the row. The default value is false.
 
 If the setLatestVersionOnly flag is set to false, it will test previous versions (timestamps) too. The default value is true.
 
 These flags are optional and if you must set neither or both
 
 Syntax: SingleColumnValueFilter(, ‘’, ‘’, ‘’,, )
 
 Syntax: SingleColumnValueFilter(, ‘’, ‘’, ‘)
 
 Example: "SingleColumnValueFilter (<=, ‘abc’,‘FamilyA’, ‘Column1’, true, false)"
 
 Example: "SingleColumnValueFilter (<=, ‘abc’,‘FamilyA’, ‘Column1’)"
 
 
                 
 
 SingleColumnValueExcludeFilter
 
 Description: This filter takes the same arguments and behaves same as SingleColumnValueFilter – however, if the column is found and the condition passes, all the columns of the row will be emitted except for the tested column value.
 
 Syntax: SingleColumnValueExcludeFilter(, '', '', '',, )
 
 Syntax: SingleColumnValueExcludeFilter(, '', '', '')
 
 Example: "SingleColumnValueExcludeFilter (‘<=’, ‘abc’,‘FamilyA’, ‘Column1’, ‘false’, ‘true’)"
 
 Example: "SingleColumnValueExcludeFilter (‘<=’, ‘abc’, ‘FamilyA’, ‘Column1’)"
 
 
                 
 
 ColumnRangeFilter
 
 Description: This filter is used for selecting only those keys with columns that are between minColumn and maxColumn. It also takes two boolean variables to indicate whether to include the minColumn and maxColumn or not.
 
 If you don’t want to set the minColumn or the maxColumn – you can pass in an empty argument.
 
 Syntax: ColumnRangeFilter (‘’, , ‘’, )
 
 Example: "ColumnRangeFilter (‘abc’, true, ‘xyz’, false)"
 
 
                
 
                
 
                 
 
                  
 
                   
 10.4. C/C++ Apache HBase Client
 
                  
 
                 
 
                
 
                
 Facebook的 Chip Turner 写了个纯 C/C++ 客户端。 Check it out.
 
               
 
              
 
             
 
            
 
            
 
             
 
              
 
               
 
              
 
             
 
            
 
           
 
           
 
            
 
             
 
              
 
               
 Chapter 11. 性能调优
 
              
 
             
 
            
 
             
            
 
             
 
              
 
               
 
                
 11.1. 操作系统
 
               
 
              
 
             
 
             
 
              
 
               
 
                
 
                 
 11.1.1. 内存
 
                
 
               
 
              
 
              
 RAM, RAM, RAM. 不要饿着 HBase.
 
             
 
             
 
              
 
               
 
                
 
                 
 11.1.2. 64-bit
 
                
 
               
 
              
 
              
 使用 64-bit 平台(和64-bit JVM).
 
             
 
             
 
              
 
               
 
                
 
                 
 11.1.3. 交换区
 
                
 
               
 
              
 
              
 小心交换，将交换区设为0。
 
             
 
            
 
           
 
           
 
            
 
             
 
              
 
               
 11.2. 网络
 
              
 
             
 
            
 
            
 也许，避免网络问题降低Hadoop和HBase性能的最重要因素就是所使用的交换机硬件。在项目范围内，集群大小翻倍或三倍甚至更多时，早期的决定可能导致主要的问题。
 
            
 要考虑的重要事项：
 
            
 
             
 
              
设备交换机容量
 
              
系统连接数量
 
              
上行容量
 
             
 
            
 
            
 
 
            
 
             
 
              
 
               
 
                
 11.2.1. 单交换机
 
               
 
              
 
             
 
             
 单交换机配置最重要的因素，是硬件交换容量，所有系统连接到交换机产生的流量的处理能力。一些低价硬件商品，相对全交换机，具有较低交换能力。
 
            
 
            
 
             
 
              
 
               
 
                
 11.2.2. 多交换机
 
               
 
              
 
             
 
             
 多交换机在系统结构中是潜在陷阱。低价硬件的最常用配置是1Gbps上行连接到另一个交换机。 该常被忽略的窄点很容易成为集群通讯的瓶颈。特别是MapReduce任务通过该上行连接同时读写大量数据时，会导致饱和。
 
             
 缓解该问题很简单，可以通过多种途径完成：
 
             
 
              
 
               
针对要创建的集群容量，采用合适硬件。
 
               
采用更大单交换机配置，如单48 端口相较2x 24 端口为优。
 
               
配置上行端口聚合(port trunking)来利用多网络接口增加交换机带宽。(译者注：port trunk： 
将交换机上的多个端口在物理上连接起来，在逻辑上捆绑在一起，形成一个拥有较大带宽的端口，组成一个干路，以达到平衡负载和提供备份线路，扩充带宽的目的。
)        
 
 
              
 
             
 
             
 
 
            
 
            
 
             
 
              
 
               
 
                
 11.2.3. 多机架
 
               
 
              
 
             
 
             
 多机架配置带来多交换机同样的潜在问题。导致性能降低的原因主要来自两个方面：
 
             
 
              
 
               
较低的交换机容量性能
 
               
到其他机架的上行链路不足
 
              
 
             
 
             
 如果机架上的交换机有合适交换容量，可以处理所有主机全速通信，那么下一个问题就是如何自动导航更多的交错在机架中的集群。最简单的避免横跨多机架问题的办法，是采用端口聚合来创建到其他机架的捆绑的上行的连接。然而该方法下行侧，是潜在被使用的端口开销。举例：从机架A到机架B创建 8Gbps 端口通道，采用24端口中的8个来和其他机架互通，ROI(投资回报率)很低。采用太少端口意味着不能从集群中传出最多的东西。
 
             
 机架间采用10Gbe 链接将极大增加性能，确保交换机都支持10Gbe 上行连接或支持扩展卡，后者相对上行连接，允许你节省机器端口。
 
            
 
            
 
             
 
              
 
               
 
                
 11.2.4. 网络接口
 
               
 
              
 
             
 
             
 所有网络接口功能正常吗？你确定？参考故障诊断用例：Section 13.3.1, “Case Study #1 (Performance Issue On A Single Node)”.
 
            
 
           
 
           
 
            
 
             
 
              
 
                
               
 可以从 wiki Performance Tuning看起。这个文档讲了一些主要的影响性能的方面:RAM, 压缩, JVM 设置, 等等。然后，可以看看下面的补充内容。
 
               
 
                
 打开RPC-level日志
 
                
 在区域服务器打开RPC-level的日志对于深度的优化是有好处的。一旦打开，日志将喷涌而出。所以不建议长时间打开，只能看一小段时间。要想启用RPC-level的职责，可以使用区域服务器 UI点击Log Level。将 org.apache.hadoop.ipc 的日志级别设为DEBUG。然后tail 区域服务器的日志，进行分析。
 
                
 要想关闭，只要把日志级别设为INFO就可以了.
 
               
 
               
 
                
 
                 
 
                  
 
                   
 11.3. Java
 
                  
 
                 
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.3.1. 垃圾收集和Apache HBase
 
                   
 
                  
 
                 
 
                 
 
                  
 
                   
 
                    
 
                     
 11.3.1.1. 长时间GC停顿
 
                    
 
                   
 
                  
 
                  
 在这个PPT Avoiding Full GCs with MemStore-Local Allocation Buffers, Todd Lipcon描述了在HBase中常见的两种“世界停止”式的GC操作，尤其是在加载的时候。一种是CMS失败的模式(译者注:CMS是一种GC的算法)，另一种是老一代的堆碎片导致的。要想定位第一种，只要将CMS执行的时间提前就可以了，加入-XX:CMSInitiatingOccupancyFraction参数，把值调低。可以先从60%和70%开始(这个值调的越低，触发的GC次数就越多，消耗的CPU时间就越长)。要想定位第二种错误，Todd加入了一个实验性的功能，在HBase 0.90.x中这个是要明确指定的(在0.92.x中，这个是默认项)，将你的Configuration中的hbase.hregion.memstore.mslab.enabled设置为true。详细信息，可以看这个PPT. [27]. Be aware that when enabled, each MemStore instance will occupy at least an MSLAB instance of memory. If you have thousands of regions or lots of regions each with many column families, this allocation of MSLAB may be responsible for a good portion of your heap allocation and in an extreme case cause you to OOME. Disable MSLAB in this case, or lower the amount of memory it uses or float less regions per server.
 
                  
 GC日志的更多信息，参考 Section 12.2.3, “JVM 垃圾收集日志”.
 
                 
 
                
 
               
 
               
 
                
 
                 
 
                  
 
                   
 11.4. 配置
 
                  
 
                 
 
                
 
                
 参见Section 2.8.2, “推荐的配置”.
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.1. Regions的数目
 
                   
 
                  
 
                 
 
                 
 HBase中region的数目可以根据Section 3.6.5, “更大的 Regions”调整.也可以参见 Section 12.3.1, “Region大小”
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.2. 管理紧缩
 
                   
 
                  
 
                 
 
                 
 对于大型的系统，你需要考虑管理紧缩和分割
 
                
 
                
 
                 
 
                  
 
                   
 
                  
 
                 
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.3. hbase.regionserver.handler.count
 
                   
 
                  
 
                 
 
                 
 参见hbase.regionserver.handler.count.这个参数的本质是设置一个RegsionServer可以同时处理多少请求。 如果定的太高，吞吐量反而会降低;如果定的太低，请求会被阻塞，得不到响应。你可以打开RPC-level日志读Log，来决定对于你的集群什么值是合适的。(请求队列也是会消耗内存的)
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.4. hfile.block.cache.size
 
                   
 
                  
 
                 
 
                 
 参见 hfile.block.cache.size. 对于区域服务器进程的内存设置。
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.5. hbase.regionserver.global.memstore.upperLimit
 
                   
 
                  
 
                 
 
                 
 参见 hbase.regionserver.global.memstore.upperLimit. 这个内存设置是根据区域服务器的需要来设定。
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.6. hbase.regionserver.global.memstore.lowerLimit
 
                   
 
                  
 
                 
 
                 
 参见 hbase.regionserver.global.memstore.lowerLimit. 这个内存设置是根据区域服务器的需要来设定。
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.7. hbase.hstore.blockingStoreFiles
 
                   
 
                  
 
                 
 
                 
 参见hbase.hstore.blockingStoreFiles. 如果在区域服务器的Log中block,提高这个值是有帮助的。
 
                
 
                
 
                 
 
                  
 
                   
 
                    
 11.4.8. hbase.hregion.memstore.block.multiplier
 
                   
 
                  
 
                 
 
                 
 参见 hbase.hregion.memstore.block.multiplier. 如果有足够的RAM，提高这个值。
 
                
 
               
 
               
 
                
 
                 
 
                  
 
                   
 
                    
 
                     
 
                      
 11.5. ZooKeeper
 
                     
 
                    
 
                   
 
                   
 配置ZooKeeper信息，请参考 Section 2.5, “ZooKeeper”  , 参看关于使用专用磁盘部分。
 
                   
 
                    
 
                     
 
                      
 11.6. 模式设计
 
                     
 
                    
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.1.  列族的数目
 
                      
 
                     
 
                    
 
                    
 参见 Section 6.2, “ On the number of column families ”.
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.2. 键和属性长度
 
                      
 
                     
 
                    
 
                    
 参考 Section 6.3.2, “Try to minimize row and column sizes”. 参考  Section 11.6.7.1, “However...” 获取压缩申请终止( compression caveats)
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.3. 表的区域大小
 
                      
 
                     
 
                    
 
                    
 区域大小可以通过基于每张表设置，当某些表需要与缺省设置的区域大小不同时，通过 HTableDescriptor 的setFileSize 的事件设置。
 
                    
 参考 Section 11.4.1, “Number of Regions” 获取更多信息。
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.4. 布隆过滤(Bloom Filters)
 
                      
 
                     
 
                    
 
                    
 布隆过滤可以每列族单独启用。使用 HColumnDescriptor.setBloomFilterType(NONE | ROW | ROWCOL) 对列族单独启用布隆。 Default = NONE 没有布隆过滤。对 ROW，行键的哈希在每次插入行时将被添加到布隆。对 ROWCOL，行键 + 列族 + 列族修饰的哈希将在每次插入行时添加到布隆。
 
                    
 参考 HColumnDescriptor 和 Section 9.7.6, “布隆过滤(Bloom Filters)” 获取更多信息。
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.5. 列族块大小
 
                      
 
                     
 
                    
 
                    
 The blocksize can be configured for each ColumnFamily in a table, and this defaults to 64k. Larger cell values require larger blocksizes. There is an inverse relationship between blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting indexes should be roughly halved).
 
                    
 参考 HColumnDescriptor and Section 9.7.5, “Store”获取更多信息。
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.6. 内存中的列族
 
                      
 
                     
 
                    
 
                    
 ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, just like any other ColumnFamily. In-memory blocks have the highest priority in the Section 9.6.4, “Block Cache”, but it is not a guarantee that the entire table will be in memory.
 
                    
 参考 HColumnDescriptor 获取更多信息。
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.6.7. 压缩
 
                      
 
                     
 
                    
 
                    
 生产系统应该采用列族压缩定义。 参考 Appendix C, Compression In HBase 获取更多信息。
 
                    
 
                     
 
                      
 
                       
 
                        
 11.6.7.1. 然而...
 
                       
 
                      
 
                     
 
                     
 Compression deflates data on disk. When it's in-memory (e.g., in the MemStore) or on the wire (e.g., transferring between RegionServer and Client) it's inflated. So while using ColumnFamily compression is a best practice, but it's not going to completely eliminate the impact of over-sized Keys, over-sized ColumnFamily names, or over-sized Column names.
 
                     
 参考 Section 6.3.2, “Try to minimize row and column sizes” on for schema design tips, and Section 9.7.5.4, “KeyValue” for more information on HBase stores data internally.
 
                    
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 11.7. HBase 通用模式
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 11.7.1. 常量
 
                       
 
                      
 
                     
 
                     
 人们刚开始使用HBase时，趋向于写如下的代码：
 
                     
Get get = new Get(rowkey);  Result r = htable.get(get);  byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr"));  // returns current version of value  
 
                     
 然而，特别是在循环内(和 MapReduce 工作内), 将列族和列名转为字节数组代价昂贵。最好使用字节数组常量，如下：
 
                     
public static final byte[] CF = "cf".getBytes();  public static final byte[] ATTR = "attr".getBytes();  ...  Get get = new Get(rowkey);  Result r = htable.get(get);  byte[] b = r.getValue(CF, ATTR);  // returns current version of value  
 
                    
 
                   
 
                   
 
                    
 
                     
 
                      
 
                       
 
                       
  
 
                       
 
                        
 
                         
 
                          
 
                           
 11.8. 写到 HBase
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.1. 批量装载
 
                           
 
                          
 
                         
 
                         
 如果可以的话，尽量使用批量导入工具，参见 Section 9.8, “批量装载”.否则就要详细看看下面的内容。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.2. 表创建: 预创建区域(Region)
 
                           
 
                          
 
                         
 
                         
 默认情况下HBase创建表会新建一个区域。执行批量导入，意味着所有的client会写入这个区域，直到这个区域足够大，以至于分裂。一个有效的提高批量导入的性能的方式，是预创建空的区域。最好稍保守一点，因为过多的区域会实实在在的降低性能。下面是一个预创建区域的例子。 (注意：这个例子里需要根据应用的key进行调整。):
 
                         
 
 
                         
public static boolean createTable(HBaseAdmin admin, HTableDescriptor table, byte[][] splits)
throws IOException {
  try {
    admin.createTable( table, splits );
    return true;
  } catch (TableExistsException e) {
    logger.info("table " + table.getNameAsString() + " already exists");
    // the table already exists...
    return false;  
  }
}

public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
  byte[][] splits = new byte[numRegions-1][];
  BigInteger lowestKey = new BigInteger(startKey, 16);
  BigInteger highestKey = new BigInteger(endKey, 16);
  BigInteger range = highestKey.subtract(lowestKey);
  BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
  lowestKey = lowestKey.add(regionIncrement);
  for(int i=0; i < numRegions-1;i++) {
    BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
    byte[] b = String.format("%016x", key).getBytes();
    splits[i] = b;
  }
  return splits;
}
 
                         
 
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.3.  表创建: 延迟log刷写
 
                           
 
                          
 
                         
 
                         
 Puts的缺省行为使用 Write Ahead Log (WAL)，会导致 HLog 编辑立即写盘。如果采用延迟刷写，WAL编辑会保留在内存中，直到刷写周期来临。好处是集中和异步写HLog，潜在问题是如果RegionServer退出，没有刷写的日志将丢失。但这也比Puts时不使用WAL安全多了。
 
                         
 延迟log刷写可以通过 HTableDescriptor 在表上设置，hbase.regionserver.optionallogflushinterval缺省值是1000ms.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.4. HBase 客户端: 自动刷写
 
                            
 
                             
 
                              
 
                              
  
 
                              
 
                             
 
                            
 
                            
 当你进行大量的Put的时候，要确认你的HTable的setAutoFlush是关闭着的。否则的话，每执行一个Put就要想区域服务器发一个请求。通过 htable.add(Put) 和 htable.add(  Put)来将Put添加到写缓冲中。如果 autoFlush = false，要等到写缓冲都填满的时候才会发起请求。要想显式的发起请求，可以调用flushCommits。在HTable实例上进行的close操作也会发起flushCommits
 
                           
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.5. HBase 客户端: 在Puts上关闭WAL
 
                           
 
                          
 
                         
 
                         
 一个经常讨论的在Puts上增加吞吐量的选项是调用 writeToWAL(false)。关闭它意味着 RegionServer 不再将 Put 写到 Write Ahead Log, 仅写到内存。然而后果是如果出现 RegionServer 失败，将导致数据丢失。如果调用writeToWAL(false) ，需保持高度警惕。你会发现实际上基本没有不同，如果你的负载很好的分布到集群中。
 
                         
 通常而言，最好对Puts使用WAL， 而增加负载吞吐量与使用 bulk loading 替代技术有关。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.6. HBase 客户端: RegionServer 成组写入
 
                           
 
                          
 
                         
 
                         
 In addition to using the writeBuffer, grouping Puts by RegionServer can reduce the number of client RPC calls per writeBuffer flush. There is a utility HTableUtil currently on TRUNK that does this, but you can either copy that or implement your own verison for those still on 0.90.x or earlier.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.7. MapReduce: 跳过 Reducer
 
                           
 
                          
 
                         
 
                         
 When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted from the Mapper, skip the Reducer step. When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other Reducers that will most likely be off-node. It's far more efficient to just write directly to HBase.
 
                         
 For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result). This is a different processing problem than from the the above case.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.8.8. Anti-Pattern: One Hot Region
 
                           
 
                          
 
                         
 
                         
 If all your data is being written to one region at a time, then re-read the section on processing timeseries data.
 
                         
 Also, if you are pre-splitting regions and all your data is still winding up in a single region even though your keys aren't monotonically increasing, confirm that your keyspace actually works with the split strategy. There are a variety of reasons that regions may appear "well split" but won't work with your data. As the HBase client communicates directly with the RegionServers, this can be obtained via HTable.getRegionLocation.
 
                         
 参考 Section 11.8.2, “ Table Creation: Pre-Creating Regions ”, as well as Section 11.4, “HBase Configurations”
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 11.9. 从HBase读
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.1. Scan 缓存
 
                           
 
                          
 
                         
 
                         
 如果HBase的输入源是一个MapReduce Job，要确保输入的Scan的setCaching值要比默认值0要大。使用默认值就意味着map-task每一行都会去请求一下region-server。可以把这个值设为500，这样就可以一次传输500行。当然这也是需要权衡的，过大的值会同时消耗客户端和服务端很大的内存，不是越大越好。
 
                         
 
                          
 
                           
 
                            
 
                             
 11.9.1.1. Scan Caching in MapReduce Jobs
 
                            
 
                           
 
                          
 
                          
 Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g., UnknownScannerException) in Map tasks if it takes longer to process a batch of records before the client goes back to the RegionServer for the next set of data. This problem can occur because there is non-trivial processing occuring per row. If you process rows quickly, set caching higher. If you process rows more slowly (e.g., lots of transformations per row, writes), then set caching lower.
 
                          
 Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a Scan), but the processing that is often performed in MapReduce jobs tends to exacerbate this issue.
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.2. Scan 属性选择
 
                           
 
                          
 
                         
 
                         
 当Scan用来处理大量的行的时候(尤其是作为MapReduce的输入)，要注意的是选择了什么字段。如果调用了 scan.addFamily，这个列族的所有属性都会返回。如果只是想过滤其中的一小部分，就指定那几个column，否则就会造成很大浪费，影响性能。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.3. MapReduce - 输入分割
 
                           
 
                          
 
                         
 
                         
 For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks seem to have the same Input Split (i.e., the RegionServer serving the data), see the Troubleshooting Case Study in Section 13.3.1, “Case Study #1 (Performance Issue On A Single Node)”.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.4. 关闭 ResultScanners
 
                           
 
                          
 
                         
 
                         
 这与其说是提高性能，倒不如说是避免发生性能问题。如果你忘记了关闭ResultScanners，会导致RegionServer出现问题。所以一定要把ResultScanner包含在try/catch 块中...
 
                         
Scan scan = new Scan();
// set attrs...
ResultScanner rs = htable.getScanner(scan);
try {
  for (Result r = rs.next(); r != null; r = rs.next()) {
  // process result...
} finally {
  rs.close();  // always close the ResultScanner!
}
htable.close();
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.5. 块缓存
 
                           
 
                          
 
                         
 
                         
 Scan实例可以在RegionServer中使用块缓存，可以由setCacheBlocks方法控制。如果Scan是MapReduce的输入源，要将这个值设置为 false。对于经常读到的行，就建议使用块缓冲。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.6.  行键的负载优化
 
                           
 
                          
 
                         
 
                         
 当scan一个表的时候， 如果仅仅需要行键（不需要no families, qualifiers, values 和 timestamps）,在加入FilterList的时候，要使用Scanner的setFilter方法的时候，要填上MUST_PASS_ALL操作参数(译者注：相当于And操作符)。一个FilterList要包含一个 FirstKeyOnlyFilter 和一个 KeyOnlyFilter.通过这样的filter组合，就算在最坏的情况下，RegionServer只会从磁盘读一个值，同时最小化客户端的网络带宽占用。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 11.9.7. 并发: 监测数据扩散
 
                           
 
                          
 
                         
 
                         
 当优化大量读取时，监测数据扩散到目标表。如果目标表含区域太少，读取时感觉像只有很少节点提供服务一样。
 
                         
 参考 Section 11.9.2, “ Table Creation: Pre-Creating Regions ”, 及 Section 11.4, “HBase Configurations”
 
                         
 
                          
 
                           
 
                            
 11.9.8. 布隆过滤(Bloom Filters)
 
                           
 
                          
 
                         
 
                         
 启用布隆过滤可以节省必须读磁盘过程，可以有助于改进读取延迟。
 
                         
 Bloom filters 在 HBase-1200 Add bloomfilters中开发。[28][29]
 
                         
 参考 Section 11.6.4, “布隆过滤(Bloom Filters)”.
 
                         
 
                          
 
                           
 
                            
 
                             
 11.9.8.1. Bloom StoreFile footprint
 
                            
 
                           
 
                          
 
                          
 Bloom filters add an entry to the StoreFile general FileInfo data structure and then two extra entries to the StoreFilemetadata section.
 
                          
 
                           
 
                            
 
                             
 
                              
 11.9.8.1.1. BloomFilter in the StoreFile FileInfo data structure
 
                             
 
                            
 
                           
 
                           
 FileInfo has a BLOOM_FILTER_TYPE entry which is set to NONE, ROW or ROWCOL.
 
                          
 
                          
 
                           
 
                            
 
                             
 
                              
 11.9.8.1.2. BloomFilter entries in StoreFile metadata
 
                             
 
                            
 
                           
 
                           
 BLOOM_FILTER_META holds Bloom Size, Hash Function used, etc. Its small in size and is cached on StoreFile.Reader load
 
                           
 BLOOM_FILTER_DATA is the actual bloomfilter data. Obtained on-demand. Stored in the LRU cache, if it is enabled (Its enabled by default).
 
                          
 
                         
 
                         
  
 
                         
 
                          
 
                           
 
                            
 
                             
 
                              
 
                              
 
                              
 11.9.8.2. 布隆过滤(Bloom Filter) 配置
 
                              
 
                              
 
                             
 
                             
 
                              
 
                              
 
                              
 
                              
 11.9.8.2.1. io.hfile.bloom.enabled 全局杀死开关
 
                              
 
                              
 
                              
 
                              
 配置文件的io.hfile.bloom.enabled 是一个当出错时的杀死开关。Default = true.
 
                             
 
                             
 
                              
 
                              
 
                              
 
                              
 11.9.8.2.2. io.hfile.bloom.error.rate
 
                              
 
                              
 
                              
 
                              
 io.hfile.bloom.error.rate = 平均误报率( average false positive rate ). 缺省 = 1%. 降低率为 ½ (如 .5%) == +1 位每布隆入口。
 
                             
 
                             
 
                              
 
                              
 
                              
 
                              
 11.9.8.2.3. io.hfile.bloom.max.fold
 
                              
 
                              
 
                              
 
                              
 io.hfile.bloom.max.fold = 保证最小折叠速率(guaranteed minimum fold rate). 大多时候不要管. Default = 7, 或压缩到原来大小的至少 1/128. 想获取更多本选项的意义，参看本文档 开发进程 节 BloomFilters in HBase 
 
                             
 
                            
 
                           
 
                          
 
                         
 
                         
 
                          
 
                           
 
                            
 
                             
 
                              
  
 
                             
 
                            
 
                           
 
                          
 
                         
 
                         
  
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 11.10. 从HBase删除
 
                         
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 11.10.1. 将 HBase 表当 Queues用
 
                          
 
                         
 
                        
 
                        
 HBase tables are sometimes used as queues. In this case, special care must be taken to regularly perform major compactions on tables used in this manner. As is documented in Chapter 5, Data Model, marking rows as deleted creates additional StoreFiles which then need to be processed on reads. Tombstones only get cleaned up with major compactions.
 
                        
 参考  Section 9.7.5.5, “Compaction” 和 HBaseAdmin.majorCompact.
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 11.10.2. 删除的 RPC 行为
 
                          
 
                         
 
                        
 
                        
 Be aware that htable.delete(Delete) doesn't use the writeBuffer. It will execute an RegionServer RPC with each invocation. For a large number of deletes, consider htable.delete(List).
 
                        
 参考 http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HTable.html#delete%28org.apache.hadoop.hbase.client.Delete%29
 
                       
 
                       
 
                        
 
                         
 
                          
 11.11. HDFS
 
                         
 
                        
 
                       
 
                       
 由于 HBase 在 Section 9.9, “HDFS” 上运行，it is important to understand how it works and how it affects HBase.
 
                       
 
                        
 
                         
 
                          
 
                           
 11.11.1. Current Issues With Low-Latency Reads
 
                          
 
                         
 
                        
 
                        
 The original use-case for HDFS was batch processing. As such, there low-latency reads were historically not a priority. With the increased adoption of HBase this is changing, and several improvements are already in development. 参考 the Umbrella Jira Ticket for HDFS Improvements for HBase.
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 
                            
 
                             
 
                              
 
                              
 
 11.11.2. Leveraging local data
 
                              
 
                             
 
                            
 
                            
 Since Hadoop 1.0.0 (also 0.22.1, 0.23.1, CDH3u3 and HDP 1.0) via HDFS-2246, it is possible for the DFSClient to take a "short circuit" and read directly from disk instead of going through the DataNode when the data is local. What this means for HBase is that the RegionServers can read directly off their machine's disks instead of having to open a socket to talk to the DataNode, the former being generally much faster[30]. Also see HBase, mail # dev - read short circuit thread for more discussion around short circuit reads.
 
                            
 To enable "short circuit" reads, you must set two configurations. First, the hdfs-site.xml needs to be amended. Set the property dfs.block.local-path-access.user to be the only user that can use the shortcut. This has to be the user that started HBase. Then in hbase-site.xml, set dfs.client.read.shortcircuit to be true
 
                            
 For optimal performance when short-circuit reads are enabled, it is recommended that HDFS checksums are disabled. To maintain data integrity with HDFS checksums disabled, HBase can be configured to write its own checksums into its datablocks and verify against these. See Section 11.4.9, “hbase.regionserver.checksum.verify”.
 
                            
 The DataNodes need to be restarted in order to pick up the new configuration. Be aware that if a process started under another username than the one configured here also has the shortcircuit enabled, it will get an Exception regarding an unauthorized access but the data will still be read.
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 11.11.3. Performance Comparisons of HBase vs. HDFS
 
                              
 
                             
 
                            
 
                            
 A fairly common question on the dist-list is why HBase isn't as performant as HDFS files in a batch context (e.g., as a MapReduce source or sink). The short answer is that HBase is doing a lot more than HDFS (e.g., reading the KeyValues, returning the most current row or specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this processing context. Not that there isn't room for improvement (and this gap will, over time, be reduced), but HDFS will always be faster in this use-case.
 
                           
 
                           
  
 
                          
 
                         
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 11.12. Amazon EC2
 
                         
 
                        
 
                       
 
                       
 Performance questions are common on Amazon EC2 environments because it is a shared environment. You will not see the same throughput as a dedicated server. In terms of running tests on EC2, run them several times for the same reason (i.e., it's a shared environment and you don't know what else is happening on the server).
 
                       
 If you are running on EC2 and post performance questions on the dist-list, please state this fact up-front that because EC2 issues are practically a separate class of performance issues.
 
                       
 
                        
 
                         
 
                          
 11.13. Case Studies
 
                         
 
                        
 
                       
 
                       
 For Performance and Troubleshooting Case Studies, see Chapter 13, Case Studies.
 
                       
 
                        
 [27] The latest jvms do better regards fragmentation so make sure you are running a recent release. Read down in the message,Identifying concurrent mode failures caused by fragmentation.
 
                       
 
                       
 
                        
 [28] For description of the development process -- why static blooms rather than dynamic -- and for an overview of the unique properties that pertain to blooms in HBase, as well as possible future directions, see the Development Process section of the document BloomFilters in HBase attached to HBase-1200.
 
                       
 
                       
 
                        
 [29] The bloom filters described here are actually version two of blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the European Commission One-Lab Project 034819. The core of the HBase bloom work was later pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. Version 2 is a rewrite from scratch though again it starts with the one-lab work.
 
                       
 
                       
 
                        
 [30] See JD's Performance Talk
 
                       
 
                       
  
 
                       
 
                        
 
                         
 
                          
 Chapter 12. HBase的故障排除和Debug
 
                         
 
                        
 
                       
 
                        
                       
 
                        
 
                         
 
                          
 
                           
 12.1. 一般准则
 
                          
 
                         
 
                        
 
                        
 总是先从主服务器的日志开始(TODO: 哪些行?)。通常情况下，他总是一行一行的重复信息。如果不是这样，说明有问题，可以Google或是用search-hadoop.com来搜索遇到的异常。
 
                        
 错误很少仅仅单独出现在HBase中，通常是某一个地方出了问题，引起各处大量异常和调用栈跟踪信息。遇到这样的错误，最好的办法是往上查日志，找到最初的异常。例如区域服务器会在退出的时候打印一些度量信息。Grep这个转储 应该可以找到最初的异常信息。
 
                        
 区域服务器的自杀是很“正常”的。当一些事情发生错误的，他们就会自杀。如果ulimit和xcievers(最重要的两个设定，详见Section 2.2.5, “ ulimit 和 nproc ”)没有修改，HDFS将无法运转正常，在HBase看来，HDFS死掉了。假想一下，你的MySQL突然无法访问它的文件系统，他会怎么做。同样的事情会发生在HBase和HDFS上。还有一个造成区域服务器切腹自杀的常见的原因是，他们执行了一个长时间的GC操作，这个时间超过了ZooKeeper的会话时长。关于GC停顿的详细信息，参见Todd Lipcon的 3 part blog post by Todd Lipcon 和上面的 Section 11.3.1.1, “长时间GC停顿”.
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 12.2. Logs
 
                          
 
                         
 
                        
 
                        
 重要日志的位置( 是启动服务的用户， 是机器的名字)
 
                        
 NameNode: $HADOOP_HOME/logs/hadoop--namenode-.log
 
                        
 DataNode: $HADOOP_HOME/logs/hadoop--datanode-.log
 
                        
 JobTracker: $HADOOP_HOME/logs/hadoop--jobtracker-.log
 
                        
 TaskTracker: $HADOOP_HOME/logs/hadoop--jobtracker-.log
 
                        
 HMaster: $HBASE_HOME/logs/hbase--master-.log
 
                        
 RegionServer: $HBASE_HOME/logs/hbase--regionserver-.log
 
                        
 ZooKeeper: TODO
 
                        
 
                         
 
                          
 
                           
 
                            
 12.2.1. Log 位置
 
                           
 
                          
 
                         
 
                         
 对于单节点模式，Log都会在一台机器上，但是对于生产环境，都会运行在一个集群上。
 
                         
 
                          
 
                           
 
                            
 
                             
 12.2.1.1. NameNode
 
                            
 
                           
 
                          
 
                          
 NameNode的日志在NameNode server上。HBase Master 通常也运行在NameNode server上，ZooKeeper通常也是这样。
 
                          
 对于小一点的机器，JobTracker也通常运行在NameNode server上面。
 
                         
 
                         
 
                          
 
                           
 
                            
 
                             
 12.2.1.2. DataNode
 
                            
 
                           
 
                          
 
                          
 每一台DataNode server有一个HDFS的日志，Region有一个HBase日志。
 
                          
 每个DataNode server还有一份TaskTracker的日志，来记录MapReduce的Task信息。
 
                         
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 12.2.2. 日志级别
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.2.2.1. 启用 RPC级别日志
 
                           
 
                          
 
                         
 
                         
 Enabling the RPC-level logging on a RegionServer can often given insight on timings at the server. Once enabled, the amount of log spewed is voluminous. It is not recommended that you leave this logging on for more than short bursts of time. To enable RPC-level logging, browse to the RegionServer UI and click on Log Level. Set the log level to DEBUG for the package org.apache.hadoop.ipc(Thats right, for hadoop.ipc, NOT, hbase.ipc). Then tail the RegionServers log. Analyze.
 
                         
 To disable, set the logging level back to INFO level.
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 12.2.3. JVM 垃圾收集日志
 
                          
 
                         
 
                        
 
                        
 HBase is memory intensive, and using the default GC you can see long pauses in all threads including the Juliet Pause aka "GC of Death". To help debug this or confirm this is happening GC logging can be turned on in the Java virtual machine.
 
                        
 To enable, in hbase-env.sh add:
 
                        
 
export HBASE_OPTS="-XX:+UseConcMarkSweepGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/home/hadoop/hbase/logs/gc-hbase.log"
          
 
                        
 Adjust the log directory to wherever you log. Note: The GC log does NOT roll automatically, so you'll have to keep an eye on it so it doesn't fill up the disk.
 
                        
 At this point you should see logs like so:
 
                        
64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 
64898.953: [CMS-concurrent-mark-start]
64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs] 
          
 
                        
 
 
                        
 In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark. This pauses the entire VM, all threads for that period of time.
 
                        
 The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10 milliseconds. It has reduced the "ParNew" from about 5.5m to 576k. Later on in this cycle we see:
 
                        
 
64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs] 
64901.445: [CMS-concurrent-preclean-start]
64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] 
64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs] 
64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs] 
64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] 
64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs] 
64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs] 
64901.578: [CMS-concurrent-abortable-preclean-start]
64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] 
64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs] 
64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs] 
64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs] 
64901.621: [CMS-concurrent-sweep-start]
            
 
                        
 
 
                        
 The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds. But this is a _concurrent_ 2.4 seconds, Java has not been paused at any point in time.
 
                        
 There are a few more minor GCs, then there is a pause at the 2nd last line:
 
                        
  
64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs] 
            
 
                        
 
 
                        
 The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.
 
                        
 At this point the sweep starts, and you can watch the heap size go down:
 
                        
64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] 
...  lines removed ...
64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] 
64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs] 
            
 
                        
 At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB (approximate).
 
                        
 The key points here is to keep all these pauses low. CMS pauses are always low, but if your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at 400ms.
 
                        
 This can be due to the size of the ParNew, which should be relatively small. If your ParNew is very large after running HBase for a while, in one example a ParNew was about 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the collections take but if its too small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.
 
                        
 Add this to HBASE_OPTS:
 
                        
 
export HBASE_OPTS="-XX:NewSize=64m -XX:MaxNewSize=64m  "
            
 
                        
 
 
                        
 For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Section 11.3.1.1, “Long GC pauses” above.
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 
                            
 
                             
 
                              
 12.3. 资源
 
                             
 
                            
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.3.1. search-hadoop.com
 
                              
 
                             
 
                            
 
                            
 search-hadoop.com 索引了全部邮件列表，很适合做历史检索。有问题时先在这里查询，因为别人可能已经遇到过你的问题。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.3.2. 邮件列表
 
                              
 
                             
 
                            
 
                            
 Ask a question on the HBase mailing lists. The 'dev' mailing list is aimed at the community of developers actually building HBase and for features currently under development, and 'user' is generally used for questions on released versions of HBase. Before going to the mailing list, make sure your question has not already been answered by searching the mailing list archives first. Use Section 12.3.1, “search-hadoop.com”. Take some time crafting your question[31]; a quality question that includes all context and exhibits evidence the author has tried to find answers in the manual and out on lists is more likely to get a prompt response.
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.3.3. IRC
 
                              
 
                             
 
                            
 
                            
 #hbase on irc.freenode.net
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.3.4. JIRA
 
                              
 
                             
 
                            
 
                            
 JIRA 在处理 Hadoop/HBase相关问题时也很有帮助。
 
                           
 
                           
 
                            
 
                            
 
                           
 
                           
 12.4. 工具
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 
                             
 
                              
 
                              
 12.4.1. 内置工具
 
                              
 
                             
 
                            
 
                            
 
                             
 
                              
 
                              
 
                              
 12.4.1.1. 主服务器Web接口
 
                              
 
                              
 
                             
 
                             
 主服务器启动了一个缺省端口是 60010的web接口。
 
                             
 The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.). Additionally, the available RegionServers in the cluster are listed along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap). The Master web UI allows navigation to each RegionServer's web UI.
 
                            
 
                            
 
                             
 
                              
 
                              
 
                              
 12.4.1.2. 区域服务器Web接口
 
                              
 
                              
 
                             
 
                             
 区域服务器启动了一个缺省端口是 60030的web接口。
 
                             
 The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).
 
                             
 参考 Section 14.4, “HBase Metrics” 获取更多度量信息。
 
                            
 
                            
 
                             
 
                              
 
                              
 
                              
 12.4.1.3. zkcli
 
                              
 
                              
 
                             
 
                             
 zkcli是一个研究 ZooKeeper相关问题的有用工具。调用：
 
                             
./hbase zkcli -server host:port  
 
                             
 命令 (和参数) ：
 
                             
	connect host:port
	get path [watch]
	ls path [watch]
	set path data [version]
	delquota [-n|-b] path
	quit 
	printwatches on|off
	create [-s] [-e] path data acl
	stat path [watch]
	close 
	ls2 path [watch]
	history 
	listquota path
	setAcl path acl
	getAcl path
	sync path
	redo cmdno
	addauth scheme auth
	delete path [version]
	setquota -n|-b val path
 
                             
 
 
                            
 
                            
 12.4.2. 外部工具
 
                           
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.4.2.1 tail
 
                           
 
                          
 
                         
 
                         
 tail是一个命令行工具，可以用来看日志的尾巴。加入的"-f"参数后，就会在数据更新的时候自己刷新。用它来看日志很方便。例如，一个机器需要花很多时间来启动或关闭，你可以tail他的master log(也可以是region server的log)。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.4.2.2 top
 
                           
 
                          
 
                         
 
                         
 top是一个很重要的工具来看你的机器各个进程的资源占用情况。下面是一个生产环境的例子：
 
                         
top - 14:46:59 up 39 days, 11:55,  1 user,  load average: 3.75, 3.57, 3.84
Tasks: 309 total,   1 running, 308 sleeping,   0 stopped,   0 zombie
Cpu(s):  4.5%us,  1.6%sy,  0.0%ni, 91.7%id,  1.4%wa,  0.1%hi,  0.6%si,  0.0%st
Mem:  24414432k total, 24296956k used,   117476k free,     7196k buffers
Swap: 16008732k total,	14348k used, 15994384k free, 11106908k cached
 
  PID USER  	PR  NI  VIRT  RES  SHR S %CPU %MEM	TIME+  COMMAND                                                                                                                                                                      
15558 hadoop	18  -2 3292m 2.4g 3556 S   79 10.4   6523:52 java                                                                                                                                                                          
13268 hadoop	18  -2 8967m 8.2g 4104 S   21 35.1   5170:30 java                                                                                                                                                                          
 8895 hadoop	18  -2 1581m 497m 3420 S   11  2.1   4002:32 java
…
                     
这里你可以看到系统的load average在最近5分钟是3.75，意思就是说这5分钟里面平均有3.75个线程在CPU时间的等待队列里面。通常来说，最完美的情况是这个值和CPU和核数相等，比这个值低意味着资源闲置，比这个值高就是过载了。这是一个重要的概念，要想理解的更多，可以看这篇文章  http://www.linuxjournal.com/article/9001. 
                         
 处理负载，我们可以看到系统已经几乎使用了他的全部RAM，其中大部分都是用于OS cache(这是一件好事).Swap只使用了一点点KB,这正是我们期望的，如果数值很高的话，就意味着在进行交换，这对Java程序的性能是致命的。另一种检测交换的方法是看Load average是否过高(load average过高还可能是磁盘损坏或者其它什么原因导致的)。
 
                         
 默认情况下进程列表不是很有用，我们可以看到3个Java进程使用了111%的CPU。要想知道哪个进程是什么，可以输入"c"，每一行就会扩展信息。输入“1”可以显示CPU的每个核的具体状况。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.4.2.3  jps
 
                           
 
                          
 
                         
 
                         
 jps是JDK集成的一个工具，可以用来看当前用户的Java进程id。(如果是root,可以看到所有用户的id)，例如:
 
                         
hadoop@sv4borg12:~$ jps
1322 TaskTracker
17789 HRegionServer
27862 Child
1158 DataNode
25115 HQuorumPeer
2950 Jps
19750 ThriftServer
18776 jmx
                     
 
                         
 按顺序看
 
                         
 
                          
 
                           
Hadoop TaskTracker,管理本地的Task
 
                           
HBase RegionServer,提供region的服务
 
                           
Child, 一个 MapReduce task,无法看出详细类型
 
                           
Hadoop DataNode, 管理blocks
 
                           
HQuorumPeer, ZooKeeper集群的成员
 
                           
Jps, 就是这个进程
 
                           
ThriftServer, 当thrif启动后，就会有这个进程
 
                           
jmx, 这个是本地监控平台的进程。你可以不用这个。
 
                          
 
                         
 
                         
 
 
                         
 你可以看到这个进程启动是全部命令行信息。
 
                         
hadoop@sv4borg12:~$ ps aux | grep HRegionServer
hadoop   17789  155 35.2 9067824 8604364 ?     S 
                         
 
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.4.2.4  jstack
 
                           
 
                          
 
                         
 
                         
 jstack 是一个最重要(除了看Log)的java工具，可以看到具体的Java进程的在做什么。可以先用Jps看到进程的Id,然后就可以用jstack。他会按线程的创建顺序显示线程的列表，还有这个线程在做什么。下面是例子：
 
                         
 这个主线程是一个RegionServer正在等master返回什么信息。
 
                         
      "regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70]
   java.lang.Thread.State: TIMED_WAITING (parking)
        	at sun.misc.Unsafe.park(Native Method)
        	- parking to wait for  <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
        	at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
        	at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963)
        	at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
        	at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
        	at java.lang.Thread.run(Thread.java:619)
 
        	The MemStore flusher thread that is currently flushing to a file:
"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
   java.lang.Thread.State: WAITING (on object monitor)
        	at java.lang.Object.wait(Native Method)
        	at java.lang.Object.wait(Object.java:485)
        	at org.apache.hadoop.ipc.Client.call(Client.java:803)
        	- locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
        	at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
        	at $Proxy1.complete(Unknown Source)
        	at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
        	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
        	at java.lang.reflect.Method.invoke(Method.java:597)
        	at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
        	at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
        	at $Proxy1.complete(Unknown Source)
        	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390)
        	- locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
        	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
        	at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61)
        	at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
        	at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
        	at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
        	at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
        	- locked <0x00007f16d00e6f08> (a java.lang.Object)
        	at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
        	at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
        	at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359)
        	at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907)
        	at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834)
        	at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
        	at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250)
        	at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224)
        	at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146)
                     
 
                         
 
 
                         
 一个处理线程是在等一些东西(例如put, delete, scan...):
 
                         
"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
   java.lang.Thread.State: WAITING (parking)
        	at sun.misc.Unsafe.park(Native Method)
        	- parking to wait for  <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
        	at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
        	at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925)
        	at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358)
        	at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013)
                     
 
                         
 
 
                         
 有一个线程正在忙，在递增一个counter(这个阶段是正在创建一个scanner来读最新的值):
 
                         
"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0]
   java.lang.Thread.State: RUNNABLE
        	at org.apache.hadoop.hbase.regionserver.KeyValueHeap.(KeyValueHeap.java:56)
        	at org.apache.hadoop.hbase.regionserver.StoreScanner.(StoreScanner.java:79)
        	at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
        	at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.(HRegion.java:2209)
        	at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063)
        	at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055)
        	at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039)
        	at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875)
        	at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978)
        	at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433)
        	at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
        	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
        	at java.lang.reflect.Method.invoke(Method.java:597)
        	at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
        	at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027)
                     
还有一个线程在从HDFS获取数据。 
                         
        	
"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
   java.lang.Thread.State: RUNNABLE
        	at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
        	at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
        	at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
        	at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
        	- locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
        	- locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
        	- locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
        	at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
        	at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332)
        	at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)
        	at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
        	at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
        	at java.io.FilterInputStream.read(FilterInputStream.java:116)
        	at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java:304)
        	at java.io.BufferedInputStream.fill(BufferedInputStream.java:218)
        	at java.io.BufferedInputStream.read(BufferedInputStream.java:237)
        	- locked <0x00007f1808539178> (a java.io.BufferedInputStream)
        	at java.io.DataInputStream.readInt(DataInputStream.java:370)
        	at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569)
        	at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477)
                     
 
                         
 
 
                         
 这里是一个RegionServer死了，master正在试着恢复。
 
                         
"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70]
--
   java.lang.Thread.State: WAITING (on object monitor)
        	at java.lang.Object.wait(Native Method)
        	at java.lang.Object.wait(Object.java:485)
        	at org.apache.hadoop.ipc.Client.call(Client.java:726)
        	- locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call)
        	at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
        	at $Proxy1.recoverBlock(Unknown Source)
        	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2636)
        	at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.(DFSClient.java:2832)
        	at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529)
        	at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:186)
        	at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530)
        	at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619)
        	at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322)
        	at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210)
        	at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java:648)
        	at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572)
        	at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503)
                     
 
                         
 
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.4.2.5  OpenTSDB
 
                           
 
                          
 
                         
 
                         
 OpenTSDB是一个Ganglia的很好的替代品，因为他使用HBase来存储所有的时序而不需要采样。使用OpenTSDB来监控你的HBase是一个很好的实践
 
                         
 这里有一个例子，集群正在同时进行上百个紧缩，严重影响了IO性能。(TODO: 在这里插入compactionQueueSize的图片)
 
                         
 给集群构建一个图表监控是一个很好的实践。包括集群和每台机器。这样就可以快速定位到问题。例如，在StumbleUpon，每个机器有一个图表监控，包括OS和HBase，涵盖所有的重要的信息。你也可以登录到机器上，获取更多的信息。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.4.2.6  clusterssh+top
 
                           
 
                          
 
                         
 
                         
 clusterssh+top,感觉是一个穷人用的监控系统，但是他确实很有效，当你只有几台机器的是，很好设置。启动clusterssh后，你就会每台机器有个终端，还有一个终端，你在这个终端的操作都会反应到其他的每一个终端上。 这就意味着，你在一天机器执行“top”,集群中的所有机器都会给你全部的top信息。你还可以这样tail全部的log，等等。
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 12.5. 客户端
 
                           
  
 
                          
 
                         
 
                        
 
                        
 HBase 客户端的更多信息， 参考 Section 9.3, “Client”.
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.1. ScannerTimeoutException 或 UnknownScannerException
 
                           
 
                          
 
                         
 
                         
 当从客户端到RegionServer的RPC请求超时。例如如果Scan.setCacheing的值设置为500，RPC请求就要去获取500行的数据，每500次.next()操作获取一次。因为数据是以大块的形式传到客户端的，就可能造成超时。将这个 serCacheing的值调小是一个解决办法，但是这个值要是设的太小就会影响性能。
 
                         
 参考 Section 11.10.1, “Scan Caching”.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.2. 普通操作时，Shell 或客户端应用抛出很多不太重要的异常
 
                           
 
                          
 
                         
 
                         
 Since 0.20.0 the default log level for org.apache.hadoop.hbase.*is DEBUG.
 
                         
 On your clients, edit $HBASE_HOME/conf/log4j.properties and change this: log4j.logger.org.apache.hadoop.hbase=DEBUG to this: log4j.logger.org.apache.hadoop.hbase=INFO, or even log4j.logger.org.apache.hadoop.hbase=WARN.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.3. 压缩时客户端长时暂停
 
                           
 
                          
 
                         
 
                         
 这是一个在HBase区列表中经常被问的问题。场景一般是客户端正在插入大量数据到相对未优化的HBase集群中时发生。压缩正好加重暂停，尽管这不是问题源头。
 
                         
 参考 Section 11.10.2, “ Table Creation: Pre-Creating Regions ” ，关于预先创建区域的模式部分，并确认表没有在单个区域中启动。
 
                         
 参考 Section 11.4, “HBase Configurations” 获取集群配置相关信息, 特别是 hbase.hstore.blockingStoreFiles, hbase.hregion.memstore.block.multiplier, MAX_FILESIZE (region size), 和 MEMSTORE_FLUSHSIZE.
 
                         
 A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes blocked on the MemStores which are blocked by the flusher thread which is blocked because there are too many files to compact because the compactor is given too many small files to compact and has to compact the same data repeatedly. This situation can occur even with minor compactions. Compounding this situation, HBase doesn't compress data in memory. Thus, the 64MB that lives in the MemStore could become a 6MB file after compression - which results in a smaller StoreFile. The upside is that more data is packed into the same region, but performance is achieved by being able to write larger files - which is why HBase waits until the flushize before writing a new StoreFile. And smaller StoreFiles become targets for compaction. Without compression the files are much bigger and don't need as much compaction, however this is at the expense of I/O.
 
                         
 For additional information, see this thread on Long client pauses with compression.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.4. ZooKeeper 客户端连接错误
 
                           
 
                          
 
                         
 
                         
 错误类似于...
 
                         
11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
 unexpected error, closing socket connection and attempting reconnect
 java.net.ConnectException: Connection refused: no further information
        at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
        at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
        at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to
 server localhost/127.0.0.1:2181
 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
 unexpected error, closing socket connection and attempting reconnect
 java.net.ConnectException: Connection refused: no further information
        at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
        at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
        at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to
 server localhost/127.0.0.1:2181
 
                         
 ... 要么是 ZooKeeper 不在了，或网络不可达问题。
 
                         
 工具 Section 12.4.1.3, “zkcli” 可以帮助调查 ZooKeeper 问题。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.5. 客户端内存耗尽，但堆大小看起来不太变化( off-heap/direct heap 在增长)
 
                           
 
                          
 
                         
 
                         
 You are likely running into the issue that is described and worked through in the mail thread HBase, mail # user - Suspected memory leak and continued over in HBase, mail # dev - FeedbackRe: Suspected memory leak. A workaround is passing your client-side JVM a reasonable value for -XX:MaxDirectMemorySize. By default, the MaxDirectMemorySize is equal to your -Xmx max heapsize setting (if -Xmxis set). Try seting it to something smaller (for example, one user had success setting it to 1g when they had a client-side heap of 12g). If you set it too small, it will bring on FullGCs so keep it a bit hefty. You want to make this setting client-side only especially if you are running the new experiemental server-side off-heap cache since this feature depends on being able to use big direct buffers (You may have to keep separate client-side and server-side config dirs).
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.6. 客户端变慢，在调用管理方法(flush, compact, 等)时发生
 
                           
 
                          
 
                         
 
                         
 该客户端问题在 HBASE-5073 版本 0.90.6中修订。 客户端的ZooKeeper 内存泄露，而客户端被管理API的额外调用产生的ZooKeeper事件连续调用。
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.5.7. 安全客户端不能连接 ([由 GSSException 引起: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)])
 
                           
 
                          
 
                         
 
                         
 There can be several causes that produce this symptom.
 
                         
 First, check that you have a valid Kerberos ticket. One is required in order to set up communication with a secure Apache HBase cluster. Examine the ticket currently in the credential cache, if any, by running the klist command line utility. If no ticket is listed, you must obtain a ticket by running the kinit command with either a keytab specified, or by interactively entering a password for the desired principal.
 
                         
 Then, consult the Java Security Guide troubleshooting section. The most common problem addressed there is resolved by setting javax.security.auth.useSubjectCredsOnly system property value to false.
 
                         
 Because of a change in the format in which MIT Kerberos writes its credentials cache, there is a bug in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to read the Kerberos credentials cache created by versions of MIT Kerberos 1.8.1 or higher. If you have this problematic combination of components in your environment, to work around this problem, first log in with kinit and then immediately refresh the credential cache with kinit -R. The refresh will rewrite the credential cache without the problematic formatting.
 
                         
 Finally, depending on your Kerberos configuration, you may need to install the Java Cryptography Extension, or JCE. Insure the JCE jars are on the classpath on both server and client systems.
 
                         
 You may also need to download the unlimited strength JCE policy files. Uncompress and extract the downloaded file, and install the policy jars into /lib/security.
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 12.6. MapReduce
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.6.1. 你认为自己在用集群, 实际上你在用本地(Local)
 
                           
 
                          
 
                         
 
                         
 如下的调用栈在使用 ImportTsv时发生，但同样的事可以在错误配置的任何任务中发生。
 
                         
    WARN mapred.LocalJobRunner: job_local_0001
java.lang.IllegalArgumentException: Can't read partitions file
       at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOrderPartitioner.java:111)
       at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
       at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
       at org.apache.hadoop.mapred.MapTask$NewOutputCollector.(MapTask.java:560)
       at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
       at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323)
       at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
Caused by: java.io.FileNotFoundException: File _partition.lst does not exist.
       at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383)
       at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251)
       at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776)
       at org.apache.hadoop.io.SequenceFile$Reader.(SequenceFile.java:1424)
       at org.apache.hadoop.io.SequenceFile$Reader.(SequenceFile.java:1419)
       at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(TotalOrderPartitioner.java:296)
 
                         
 .. 看到调用栈的关键部分了吗？就是...
 
                         
       at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
 
                         
 LocalJobRunner 意思就是任务跑在本地，不在集群。
 
                         
 参考 http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/package-summary.html#classpath for more information on HBase MapReduce jobs and classpaths.
 
                        
 
                       
 
                       
 
                        
 
                         
 
                          
 
                           
 12.7. NameNode
 
                          
 
                         
 
                        
 
                        
 NameNode 更多信息, 参考 Section 9.9, “HDFS”.
 
                        
 
                         
 
                          
 
                           
 
                            
 12.7.1. 表和区域的HDFS 工具
 
                           
 
                          
 
                         
 
                         
 要确定HBase 用了HDFS多大空间，可在NameNode使用 hadoop shell命令，例如...
 
                         
hadoop fs -dus /hbase/
 
                         
 ...返回全部HBase对象磁盘占用的情况。
 
                         
hadoop fs -dus /hbase/myTable
 
                         
 ...返回HBase表'myTable'磁盘占用的情况。
 
                         
hadoop fs -du /hbase/myTable
 
                         
 ...返回HBase的'myTable'表的各区域列表的磁盘占用情况。
 
                         
 更多关于 HDFS shell 命令的信息，参考 HDFS 文件系统 Shell 文档.
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.7.2. 浏览 HDFS ，查看 HBase 对象
 
                           
 
                          
 
                         
 
                         
 有时需要浏览HDFS上的 HBase对象 。对象包括WALs (Write Ahead Logs), 表，区域，存储文件等。最简易的方法是在NameNode web应用中查看，端口 50070。NameNode web 应用提供到集群中所有 DataNode 的链接，可以无缝浏览。
 
                         
 存储在HDFS集群中的HBase表的目录结构是...
 
                         
/hbase
     /
             (Tables in the cluster)
          /           (Regions for the table)
               /      (ColumnFamilies for the Region for the table)
                    /        (StoreFiles for the ColumnFamily for the Regions for the table)
            
 
 HDFS 中的HBase WAL目录结构是..
/hbase
     /.logs     
          /    (RegionServers)
               /           (WAL HLog files for the RegionServer)
            
 
 参考HDFS User Guide 获取其他非Shell诊断工具如fsck.
 
                          
 
                           
 
                            
 
                             
 12.7.2.1. 用例
 
                            
 
                           
 
                          
 
                          
 查询HDFS，获取HBase对象的两个通常用例是研究未紧缩表的程度。如果每个列族有大量存储文件(StoreFile)，这表示需要主紧缩。另外，在主紧缩之后，如果存储文件的比较小，意味着表的列族要减少。
 
                         
 
                        
 
                         
 
                          
 
                           
 
                            
 
                             
 
                              
 12.8. 网络
 
                             
 
                            
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.8.1. 网络峰值(Network Spikes)
 
                              
 
                             
 
                            
 
                            
 如果看到周期性网络峰值，你可能需要检查compactionQueues，是不是主紧缩正在进行。
 
                            
 参考 Section 2.8.2.8, “Managed Compactions” ，获取更多管理紧缩的信息。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.8.2. 回环IP(Loopback IP)
 
                              
 
                             
 
                            
 
                            
 HBase 希望回环 IP 地址是 127.0.0.1. 参考开始章节 Section 2.2.3, “Loopback IP”.
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.8.3. 网络接口
 
                              
 
                             
 
                            
 
                            
 所有网络接口是否正常？你确定吗？参考故障诊断用例研究 Section 12.14, “Case Studies”.
 
                           
 
                          
 
                         
 
                        
 
                       
 
                        
 
                         
 
                          
 
                           
 12.9. 区域服务器
 
                           
 RegionServer 的更多信息，参考 Section 9.6, “RegionServer”.
 
                          
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                          
 
                         
 
                         
 
                          
 
                          
 
                           
 
                            
 
                             
 
                              
 12.9.1. 启动错误
 
                             
 
                            
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.1.1. 主服务器启动了，但区域服务器没有启动
 
                              
 
                             
 
                            
 
                            
 主服务器相信区域服务器有IP地址127.0.0.1 - 这是 localhost 并被解析到主服务器自己的localhost.
 
                            
 区域服务器错误的通知主服务器他们的IP地址是127.0.0.1.
 
                            
 修改区域服务器的 /etc/hosts 从...
 
                            
# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1               fully.qualified.regionservername regionservername  localhost.localdomain localhost
::1             localhost6.localdomain6 localhost6
            
 
                            
 ... 改到 (将主名称从localhost中移掉)...
 
                            
# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1               localhost.localdomain localhost
::1             localhost6.localdomain6 localhost6
            
 
                            
 
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.1.2. Compression Link Errors
 
                              
 
                             
 
                            
 
                            
 因为LZO压缩算法需要在集群中的每台机器都要安装，这是一个启动失败的常见错误。如果你获得了如下信息
 
                            
11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library
java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
        at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734)
        at java.lang.Runtime.loadLibrary0(Runtime.java:823)
        at java.lang.System.loadLibrary(System.java:1028)
                     
 
                            
 就意味着你的压缩库出现了问题。参见配置章节的 LZO compression configuration.
 
                           
 
                          
 
                          
 
                           
 
                            
 
                             
 
                              
 12.9.2. 运行时错误
 
                             
 
                            
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.1. RegionServer Hanging
 
                              
 
                             
 
                            
 
                            
 Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump, does it look like threads are BLOCKED but no one holds the lock all are blocked on? 参考 HBASE 3622 Deadlock in HBaseServer (JVM bug?). Adding -XX:+UseMembar to the HBase HBASE_OPTS in conf/hbase-env.sh may fix it.
 
                            
 Also, are you using Section 9.3.4, “RowLocks”? These are discouraged because they can lock up the RegionServers if not managed properly.
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.2. java.io.IOException...打开太多文件(Too many open files)
 
                              
 
                             
 
                            
 
                            
 如果看到如下消息...
 
                            
2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: 
Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException: Too many open files
        at java.io.UnixFileSystem.createFileExclusively(Native Method)
        at java.io.File.createNewFile(File.java:883)
 
                            
 ... 参见快速入门的章节 ulimit and nproc configuration.
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.3. xceiverCount 258 exceeds the limit of concurrent xcievers 256
 
                              
 
                             
 
                            
 
                            
 这个时常会出现在DataNode的日志中。
 
                            
 参见快速入门章节的 xceivers configuration.
 
                            
  
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.4. 系统不稳定,DataNode或者其他系统进程有 "java.lang.OutOfMemoryError: unable to create new native thread in exceptions"的错误
 
                              
 
                             
 
                            
 
                            
 参见快速入门章节的 ulimit and nproc configuration.. 最新的Linux发行版缺省值是 1024 - 这对HBase实在太小了。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.5. DFS不稳定或者RegionServer租期超时
 
                              
 
                             
 
                            
 
                            
 如果你收到了如下的消息:
 
                            
2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 10000
2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 15000
2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer: unable to report to master for xxx milliseconds - retrying      
           
 
                            
 ... 或者看到了全GC压缩操作，你可能正在执行一个全GC。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.6. "No live nodes contain current block" and/or YouAreDeadException
 
                              
 
                             
 
                            
 
                            
 这个错误有可能是OS的文件句柄溢出，也可能是网络故障导致节点无法访问。
 
                            
 参见快速入门章节 ulimit and nproc configuration，检查你的网络。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.7. ZooKeeper 会话超时事件
 
                              
 
                             
 
                            
 
                            
 Master or RegionServers shutting down with messages like those in the logs:
 
                            
WARN org.apache.zookeeper.ClientCnxn: Exception
closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec
java.io.IOException: TIMED OUT
       at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906)
WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000
INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT
INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT]
INFO org.apache.zookeeper.ClientCnxn: Server connection successful
WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e
java.io.IOException: Session Expired
       at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.java:589)
       at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709)
       at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945)
ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired           
           
 
                            
 The JVM is doing a long running garbage collecting which is pausing every threads (aka "stop the world"). Since the RegionServer's local ZooKeeper client cannot send heartbeats, the session times out. By design, we shut down any node that isn't able to contact the ZooKeeper ensemble after getting a timeout so that it stops serving data that may already be assigned elsewhere.
 
                            
 
 
                            
 
                             
 
                              
Make sure you give plenty of RAM (in hbase-env.sh), the default of 1GB won't be able to sustain long running imports.
 
                              
Make sure you don't swap, the JVM never behaves well under swapping.
 
                              
Make sure you are not CPU starving the RegionServer thread. For example, if you are running a MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you are probably starving the RegionServer enough to create longer garbage collection pauses.
 
                              
Increase the ZooKeeper session timeout
 
                             
 
                            
 
                            
 If you wish to increase the session timeout, add the following to your hbase-site.xml to increase the timeout from the default of 60 seconds to 120 seconds.
 
                            

    zookeeper.session.timeout
    1200000


    hbase.zookeeper.property.tickTime
    6000

            
 
                            
 
 
                            
 Be aware that setting a higher timeout means that the regions served by a failed RegionServer will take at least that amount of time to be transfered to another RegionServer. For a production system serving live requests, we would instead recommend setting it lower than 1 minute and over-provision your cluster in order the lower the memory load on each machines (hence having less garbage to collect per machine).
 
                            
 If this is happening during an upload which only happens once (like initially loading all your data into HBase), consider bulk loading.
 参考  Section 12.11.2, “ZooKeeper, The Cluster Canary” for other general information about ZooKeeper troubleshooting. 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.8. 无服务区域异常(NotServingRegionException)
 
                              
 
                             
 
                            
 
                            
 This exception is "normal" when found in the RegionServer logs at DEBUG level. This exception is returned back to the client and then the client goes back to .META. to find the new location of the moved region.
 
                            
 However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and something probably wrong.
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.9. 区域列示先是域名，然后IP
 
                              
 
                             
 
                            
 
                            
 修复 DNS. HBase 0.92.x以前的版本，反向 DNS 需要和正向查询相同答案。 参考 HBASE 3431 RegionServer is not using the name given it by the master; double entry in master listing of servers 获取详细细节。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.10. 大量类似 '2011-01-10 12:40:48,407 INFO org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor' 日志消息
 
                              
 
                             
 
                            
 
                            
 没有采用本地压缩库版本。 参考 HBASE-1900 Put back native support when hadoop 0.21 is released。从hadoop的HBase库目录复制本地库或建立链接到正确位置，该消息将消失。
 
                           
 
                           
 
                            
 
                             
 
                              
 
                              
 12.9.2.11. Server handler X on 60020 caught: java.nio.channels.ClosedChannelException
 
                              
 
                             
 
                            
 
                            
 If you see this type of message it means that the region server was trying to read/send data from/to a client but it already went away. Typical causes for this are if the client was killed (you see a storm of messages like this when a MapReduce job is killed or fails) or if the client receives a SocketTimeoutException. It's harmless, but you should consider digging in a bit more if you aren't doing something to trigger them.
 
                           
 
                          
 
                          
 
                           
 
                            
 
                             
 
                            
 
                           
 
                          
 
                         
 
                        
 
                       
 
                     
 
                      
 
                       
 
                        
 12.9.3. 终止错误
 
                       
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 12.10. Master
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 
                         
 
                          
 
                         
 
                        
 
                        
 Master 更多信息, 参考 Section 9.5, “Master”.
 
                        
 
                         
 
                          
 
                           
 
                            
 12.10.1. 启动错误
 
                           
 
                          
 
                         
 
                         
 
                          
 
                           
 
                            
 
                             
 12.10.1.1. Master says that you need to run the hbase migrations script
 
                            
 
                           
 
                          
 
                          
 Upon running that, the hbase migrations script says no files in root directory.
 
                          
 HBase expects the root directory to either not exist, or to have already been initialized by hbase running a previous time. If you create a new directory for HBase using Hadoop DFS, this error will occur. Make sure the HBase root directory does not currently exist or has been initialized by a previous run of HBase. Sure fire solution is to just use Hadoop dfs to delete the HBase root and let HBase create and initialize the directory itself.
 
                         
 
                        
 
                        
 
                         
 
                          
 
                           
 
                            
 12.10.2. 终止错误
 
                           
 
                          
 
                         
 
                        
 
                       
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                     
 
                    
 
                   
 
                    
 
                     
 
                      
 12.11. ZooKeeper
 
                     
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 12.11.1. 启动错误
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 12.11.1.1. 找不到地址: xyz in list of ZooKeeper quorum servers
 
                       
 
                      
 
                     
 
                     
 A ZooKeeper server wasn't able to start, throws that error. xyz is the name of your server.
 
                     
 This is a name lookup problem. HBase tries to start a ZooKeeper server on some machine but that machine isn't able to find itself in the hbase.zookeeper.quorumconfiguration.
 
                     
 Use the hostname presented in the error message instead of the value you used. If you have a DNS server, you can set hbase.zookeeper.dns.interface andhbase.zookeeper.dns.nameserver in hbase-site.xml to make sure it resolves to the correct FQDN.
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 12.11.2. ZooKeeper, The Cluster Canary
 
                      
 
                     
 
                    
 
                    
 ZooKeeper is the cluster's "canary in the mineshaft". It'll be the first to notice issues if any so making sure its happy is the short-cut to a humming cluster.
 
                    
 参考  ZooKeeper Operating Environment Troubleshooting 页。 It has suggestions and tools for checking disk and networking performance; i.e. the operating environment your ZooKeeper and HBase are running in.
 
                    
 Additionally, the utility Section 12.4.1.3, “zkcli” may help investigate ZooKeeper issues.
 
                   
  
 
                    
 
                     
 
                      
 
                       
 12.12. Amazon EC2
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 12.12.1. ZooKeeper 在 Amazon EC2上看起来不工作？
 
                       
 
                      
 
                     
 
                     
 HBase does not start when deployed as Amazon EC2 instances. Exceptions like the below appear in the Master and/or RegionServer logs:
 
                     
  2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting
  connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181
  2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception
  closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861
  java.net.ConnectException: Connection refused
             
 
                     
 Security group policy is blocking the ZooKeeper port on a public address. Use the internal EC2 host names when configuring the ZooKeeper quorum peer list.
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 12.12.2.  Amazon EC2 上不稳定？
 
                       
 
                      
 
                     
 
                     
 关于 HBase 和Amazon EC2 的问题，经常在HBase 讨论列表上被问起。搜索旧线索，使用 Search Hadoop
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 12.12.3. 远程Java连接到EC2集群不工作
 
                       
 
                      
 
                     
 
                     
 参考 Andrew回复，更新在用户列表：Remote Java client connection into EC2 instance.
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 12.13. HBase 和 Hadoop 版本问题
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 12.13.1. 当想在adoop-0.20.205.x (或 hadoop-1.0.x)运行 0.90.x 时报NoClassDefFoundError
 
                       
 
                      
 
                     
 
                     
 HBase 0.90.x does not ship with hadoop-0.20.205.x, etc. To make it run, you need to replace the hadoop jars that HBase shipped with in its lib directory with those of the Hadoop you want to run HBase on. If even after replacing Hadoop jars you get the below exception:
 
                     
sv4r6s38: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/configuration/Configuration
sv4r6s38:       at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.(DefaultMetricsSystem.java:37)
sv4r6s38:       at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.(DefaultMetricsSystem.java:34)
sv4r6s38:       at org.apache.hadoop.security.UgiInstrumentation.create(UgiInstrumentation.java:51)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:209)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.isSecurityEnabled(UserGroupInformation.java:229)
sv4r6s38:       at org.apache.hadoop.security.KerberosName.(KerberosName.java:83)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:202)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
 
                     
 you need to copy under hbase/lib, the commons-configuration-X.jar you find in your Hadoop's lib directory. That should fix the above complaint.
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 12.14. 用例研究
 
                      
 
                     
 
                    
 
                    
 性能和故障诊断用例，参考 Chapter 13, Case Studies.
 
                    
 
                    
 
                     
 [31]参考获取答案
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 Chapter 13. 用例研究
 
                      
 
                     
 
                    
 
                     
                    
 
                     
 
                      
 
                       
 
                        
 13.1. 概述
 
                       
 
                      
 
                     
 
                     
 This chapter will describe a variety of performance and troubleshooting case studies that can provide a useful blueprint on diagnosing cluster issues.
 
                     
 For more information on Performance and Troubleshooting, see Chapter 11, Performance Tuning and Chapter 12, Troubleshooting and Debugging HBase.
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 13.2. 模式设计
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 13.2.1. 数据列表
 
                       
 
                      
 
                     
 
                     
 The following is an exchange from the user dist-list regarding a fairly common question: how to handle per-user list data in HBase.
 
                     
 *** QUESTION ***
 
                     
 We're looking at how to store a large amount of (per-user) list data in HBase, and we were trying to figure out what kind of access pattern made the most sense. One option is store the majority of the data in a key, so we could have something like:
 
                     
:"" (no value)
:"" (no value)
:"" (no value)
			
The other option we had was to do this entirely using: 
                     
:...
:...
    		
 
                     
 where each row would contain multiple values. So in one case reading the first thirty values would be:
 
                     
scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}
    		
And in the second case it would be 
                     
get 'FixedWidthUserName\x00\x00\x00\x00'
    		
 
                     
 The general usage pattern would be to read only the first 30 values of these lists, with infrequent access reading deeper into the lists. Some users would have <= 30 total values in these lists, and some users would have millions (i.e. power-law distribution)
 
                     
 The single-value format seems like it would take up more space on HBase, but would offer some improved retrieval / pagination flexibility. Would there be any significant performance advantages to be able to paginate via gets vs paginating with scans?
 
                     
 My initial understanding was that doing a scan should be faster if our paging size is unknown (and caching is set appropriately), but that gets should be faster if we'll always need the same page size. I've ended up hearing different people tell me opposite things about performance. I assume the page sizes would be relatively consistent, so for most use cases we could guarantee that we only wanted one page of data in the fixed-page-length case. I would also assume that we would have infrequent updates, but may have inserts into the middle of these lists (meaning we'd need to update all subsequent rows).
 
                     
 Thanks for help / suggestions / follow-up questions.
 
                     
 *** ANSWER ***
 
                     
 If I understand you correctly, you're ultimately trying to store triples in the form "user, valueid, value", right? E.g., something like:
 
                     
"user123, firstname, Paul",
"user234, lastname, Smith"
			
 
                     
 (But the usernames are fixed width, and the valueids are fixed width).
 
                     
 And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with valueid Y". Is that right? And these values should be returned sorted by valueid?
 
                     
 The tl;dr version is that you should probably go with one row per user+value, and not build a complicated intra-row pagination scheme on your own unless you're really sure it is needed.
 
                     
 Your two options mirror a common question people have when designing HBase schemas: should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so there are many rows in the table for each user; the row key is user + valueid, and there would be (presumably) a single column qualifier that means "the value". This is great if you want to scan over rows in sorted order by row key (thus my question above, about whether these ids are sorted correctly). You can start a scan at any user+valueid, read the next 30, and be done. What you're giving up is the ability to have transactional guarantees around all the rows for one user, but it doesn't sound like you need that. Doing it this way is generally recommended (see here#schema.smackdown).
 
                     
 Your second option is "wide": you store a bunch of values in one row, using different qualifiers (where the qualifier is the valueid). The simple way to do that would be to just store ALL values for one user in a single row. I'm guessing you jumped to the "paginated" version because you're assuming that storing millions of columns in a single row would be bad for performance, which may or may not be true; as long as you're not trying to do too much in a single request, or do things like scanning over and returning all of the cells in the row, it shouldn't be fundamentally worse. The client has methods that allow you to get specific slices of columns.
 
                     
 Note that neither case fundamentally uses more disk space than the other; you're just "shifting" part of the identifying information for a value either to the left (into the row key, in option one) or to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores the whole row key, and column family name. (If this is a bit confusing, take an hour and watch Lars George's excellent video about understanding HBase schema design: http://www.youtube.com/watch?v=_HLoH_PgrLk).
 
                     
 A manually paginated version has lots more complexities, as you note, like having to keep track of how many things are in each page, re-shuffling if new values are inserted, etc. That seems significantly more complex. It might have some slight speed advantages (or disadvantages!) at extremely high throughput, and the only way to really know that would be to try it out. If you don't have time to build it both ways and compare, my advice would be to start with the simplest option (one row per user+value). Start simple and iterate! :)
 
                    
 
                   
 
                    
 
                     
 
                      
 
                       
 13.3. 性能/故障诊断
 
                      
 
                     
 
                    
 
                    
 
                     
 
                      
 
                       
 
                        
 13.3.1. 用例 #1 (单节点性能问题)
 
                       
 
                      
 
                     
 
                     
 
                      
 
                       
 
                        
 
                         
 13.3.1.1.场景
 
                        
 
                       
 
                      
 
                      
 Following a scheduled reboot, one data node began exhibiting unusual behavior. Routine MapReduce jobs run against HBase tables which regularly completed in five or six minutes began taking 30 or 40 minutes to finish. These jobs were consistently found to be waiting on map and reduce tasks assigned to the troubled data node (e.g., the slow map tasks all had the same Input Split). The situation came to a head during a distributed copy, when the copy was severely prolonged by the lagging node.
 
                     
 
                     
 
                      
 
                       
 
                        
 
                         
 13.3.1.2. 硬件
 
                        
 
                       
 
                      
 
                      
 Datanodes:
 
                      
 
                       
 
                        
Two 12-core processors
 
                        
Six Enerprise SATA disks
 
                        
24GB of RAM
 
                        
Two bonded gigabit NICs
 
                       
 
                      
 
                      
 
 
                      
 Network:
 
                      
 
                       
 
                        
10 Gigabit top-of-rack switches
 
                        
20 Gigabit bonded interconnects between racks.
 
                       
 
                      
 
                      
 
 
                     
 
                     
 
                      
 
                       
 
                        
 
                         
 13.3.1.3. 假设
 
                        
 
                       
 
                      
 
                      
 
                       
 
                        
 
                         
 
                          
 13.3.1.3.1. HBase "热点" 区域
 
                         
 
                        
 
                       
 
                       
 We hypothesized that we were experiencing a familiar point of pain: a "hot spot" region in an HBase table, where uneven key-space distribution can funnel a huge number of requests to a single HBase region, bombarding the RegionServer process and cause slow response time. Examination of the HBase Master status page showed that the number of HBase requests to the troubled node was almost zero. Further, examination of the HBase logs showed that there were no region splits, compactions, or other region transitions in progress. This effectively ruled out a "hot spot" as the root cause of the observed slowness.
 
                      
 
                      
 
                       
 
                        
 
                         
 
                          
 13.3.1.3.2. HBase 分区具有非本地数据
 
                         
 
                        
 
                       
 
                       
 Our next hypothesis was that one of the MapReduce tasks was requesting data from HBase that was not local to the datanode, thus forcing HDFS to request data blocks from other servers over the network. Examination of the datanode logs showed that there were very few blocks being requested over the network, indicating that the HBase region was correctly assigned, and that the majority of the necessary data was located on the node. This ruled out the possibility of non-local data causing a slowdown.
 
                      
 
                      
 
                       
 
                        
 
                         
 
                          
 13.3.1.3.3. Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk
 
                         
 
                        
 
                       
 
                       
 After concluding that the Hadoop and HBase were not likely to be the culprits, we moved on to troubleshooting the datanode's hardware. Java, by design, will periodically scan its entire memory space to do garbage collection. If system memory is heavily overcommitted, the Linux kernel may enter a vicious cycle, using up all of its resources swapping Java heap back and forth from disk to RAM as Java tries to run garbage collection. Further, a failing hard disk will often retry reads and/or writes many times before giving up and returning an error. This can manifest as high iowait, as running processes wait for reads and writes to complete. Finally, a disk nearing the upper edge of its performance envelope will begin to cause iowait as it informs the kernel that it cannot accept any more data, and the kernel queues incoming data into the dirty write pool in memory. However, using vmstat(1) and free(1), we could see that no swap was being used, and the amount of disk IO was only a few kilobytes per second.
 
                      
 
                      
 
                       
 
                        
 
                         
 
                          
 13.3.1.3.4. Slowness Due To High Processor Usage
 
                         
 
                        
 
                       
 
                       
 Next, we checked to see whether the system was performing slowly simply due to very high computational load. top(1) showed that the system load was higher than normal, but vmstat(1) and mpstat(1)showed that the amount of processor being used for actual computation was low.
 
                      
 
                      
 
                       
 
                        
 
                         
 
                          
 13.3.1.3.5. Network Saturation (The Winner)
 
                         
 
                        
 
                       
 
                      
 
                     
 
                    
 
                   

                    

                        
                        

                            
 
                        
                        
                        

                            

                        
                        
                        

                            

                        
                    
                

        
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web前端期末大作业javascripthtmlcss旅游风景
                        
⛵源码获取文末联系✈Web前端开发技术描述网页设计题材，DIV+CSS布局制作,HTML+CSS网页设计期末课程大作业|游景点介绍|旅游风景区|家乡介绍|等网站的设计与制作|HTML期末大学生网页设计作业，Web大学生网页HTML：结构CSS：样式在操作方面上运用了html5和css3，采用了div+css结构、表单、超链接、浮动、绝对定位、相对定位、字体样式、引用视频等基础知识JavaScrip
                    
                    
HTML网页设计制作大作业（div+css） 云南我的家乡旅游景点 带文字滚动
                        二挡起步
web前端期末大作业web设计网页规划与设计htmlcssjavascriptdreamweaver前端
                        
Web前端开发技术描述网页设计题材，DIV+CSS布局制作,HTML+CSS网页设计期末课程大作业游景点介绍|旅游风景区|家乡介绍|等网站的设计与制作HTML期末大学生网页设计作业HTML：结构CSS：样式在操作方面上运用了html5和css3，采用了div+css结构、表单、超链接、浮动、绝对定位、相对定位、字体样式、引用视频等基础知识JavaScript：做与用户的交互行为文章目录前端学习路线
                    
                    
git - Webhook让部署自动化
                        大猪大猪

                        
我们现在有一个需求，将项目打包上传到gitlab或者github后，程序能自动部署，不用手动地去服务器中进行项目更新并运行，如何做到？这里我们可以使用gitlab与github的挂钩，挂钩的原理就是，每当我们有请求到gitlab与github服务器时，这时他俩会根据我们配置的挂钩地扯进行访问，webhook挂钩程序会一直监听着某个端口请求，一但收到他们发过来的请求，这时就知道用户有请求提交了，这时
                    
                    
MYSQL面试系列-04
                        king01299
面试mysql面试
                        
MYSQL面试系列-0417.关于redolog和binlog的刷盘机制、redolog、undolog作用、GTID是做什么的？innodb_flush_log_at_trx_commit及sync_binlog参数意义双117.1innodb_flush_log_at_trx_commit该变量定义了InnoDB在每次事务提交时，如何处理未刷入（flush）的重做日志信息（redolog）。它
                    
                    
webpack图片等资源的处理
                        dmengmeng

                        
需要的loaderfile-loader（让我们可以引入这些资源文件）url-loader（其实是file-loader的二次封装）img-loader（处理图片所需要的）在没有使用任何处理图片的loader之前，比如说css中用到了背景图片，那么最后打包会报错的，因为他没办法处理图片。其实你只想能够使用图片的话。只加一个file-loader就可以，打开网页能准确看到图片。{test:/\.(p
                    
                    
nosql数据库技术与应用知识点
                        皆过客，揽星河
NoSQLnosql数据库大数据数据分析数据结构非关系型数据库
                        
Nosql知识回顾大数据处理流程数据采集(flume、爬虫、传感器)数据存储(本门课程NoSQL所处的阶段)Hdfs、MongoDB、HBase等数据清洗(入仓)Hive等数据处理、分析(Spark、Flink等)数据可视化数据挖掘、机器学习应用(Python、SparkMLlib等)大数据时代存储的挑战(三高)高并发(同一时间很多人访问)高扩展(要求随时根据需求扩展存储)高效率(要求读写速度快)
                    
                    
SpringBlade dict-biz/list 接口 SQL 注入漏洞
                        文章永久免费只为良心
oracle数据库
                        
SpringBladedict-biz/list接口SQL注入漏洞POC:构造请求包查看返回包你的网址/api/blade-system/dict-biz/list?updatexml(1,concat(0x7e,md5(1),0x7e),1)=1漏洞概述在SpringBlade框架中，如果dict-biz/list接口的后台处理逻辑没有正确地对用户输入进行过滤或参数化查询（PreparedSta
                    
                    
关于Mysql 中 Row size too large (＞ 8126) 错误的解决和理解
                        秋刀prince
mysqlmysql数据库
                        
提示：啰嗦一嘴，数据库的任何操作和验证前，一定要记得先备份！！！不会有错；文章目录问题发现一、问题导致的可能原因1、页大小2、行格式2.1compact格式2.2Redundant格式2.3Dynamic格式2.4Compressed格式3、BLOB和TEXT列二、解决办法1、修改页大小（不推荐）2、修改行格式3、修改数据类型为BLOB和TEXT列4、其他优化方式（可以参考使用）4.1合理设置数据
                    
                    
「豆包Marscode体验官」 | 云端 IDE 启动 & Rust 体验
                        张风捷特烈
iderust开发语言后端
                        
theme:cyanosis我正在参加「豆包MarsCode初体验」征文活动MarsCode可以看作一个运行在服务端的远程VSCode开发环境。对于我这种想要学习体验某些语言，但不想在电脑里装环境的人来说非常友好。本文就来介绍一下在MarsCode里，我的体验rust开发体验。一、MarsCode是什么它的本质是:提供代码助手和云端IDE服务的web网站，可通过下面的链接访问https://www
                    
                    
Python神器！WEB自动化测试集成工具 DrissionPage
                        亚丁号
python开发语言
                        
一、前言用requests做数据采集面对要登录的网站时，要分析数据包、JS源码，构造复杂的请求，往往还要应付验证码、JS混淆、签名参数等反爬手段，门槛较高。若数据是由JS计算生成的，还须重现计算过程，体验不好，开发效率不高。使用浏览器，可以很大程度上绕过这些坑，但浏览器运行效率不高。因此，这个库设计初衷，是将它们合而为一，能够在不同须要时切换相应模式，并提供一种人性化的使用方法，提高开发和运行效率
                    
                    
Java爬虫框架（一）--架构设计
                        狼图腾-狼之传说
java框架java任务html解析器存储电子商务
                        
一、架构图那里搜网络爬虫框架主要针对电子商务网站进行数据爬取，分析，存储，索引。爬虫：爬虫负责爬取，解析，处理电子商务网站的网页的内容数据库：存储商品信息索引：商品的全文搜索索引Task队列：需要爬取的网页列表Visited表：已经爬取过的网页列表爬虫监控平台：web平台可以启动，停止爬虫，管理爬虫，task队列，visited表。二、爬虫1.流程1)Scheduler启动爬虫器，TaskMast
                    
                    
Java：爬虫框架
                        dingcho
Javajava爬虫
                        
一、ApacheNutch2【参考地址】Nutch是一个开源Java实现的搜索引擎。它提供了我们运行自己的搜索引擎所需的全部工具。包括全文搜索和Web爬虫。Nutch致力于让每个人能很容易,同时花费很少就可以配置世界一流的Web搜索引擎.为了完成这一宏伟的目标,Nutch必须能够做到:每个月取几十亿网页为这些网页维护一个索引对索引文件进行每秒上千次的搜索提供高质量的搜索结果简单来说Nutch支持分
                    
                    
MongoDB知识概括
                        GeorgeLin98
持久层mongodb
                        
MongoDB知识概括MongoDB相关概念单机部署基本常用命令索引-IndexSpirngDataMongoDB集成副本集分片集群安全认证MongoDB相关概念业务应用场景：传统的关系型数据库（如MySQL），在数据操作的“三高”需求以及应对Web2.0的网站需求面前，显得力不从心。解释：“三高”需求：①Highperformance-对数据库高并发读写的需求。②HugeStorage-对海量数
                    
                    
JAVA·一个简单的登录窗口
                        MortalTom
java开发语言学习
                        
文章目录概要整体架构流程技术名词解释技术细节资源概要JavaSwing是Java基础类库的一部分，主要用于开发图形用户界面（GUI）程序整体架构流程新建项目，导入sql.jar包（链接放在了文末），编译项目并运行技术名词解释一、特点丰富的组件提供了多种可视化组件，如按钮（JButton）、文本框（JTextField）、标签（JLabel）、下拉列表（JComboBox）等，可以满足不同的界面设计
                    
                    
Python实现下载当前年份的谷歌影像
                        sand&wich
python开发语言
                        
在GIS项目和地图应用中，获取最新的地理影像数据是非常重要的。本文将介绍如何使用Python代码从Google地图自动下载当前年份的影像数据，并将其保存为高分辨率的TIFF格式文件。这个过程涉及地理坐标转换、多线程下载和图像处理。关键功能该脚本的核心功能包括：坐标转换：支持WGS-84与WebMercator投影之间转换，以及处理中国GCJ-02偏移。自动化下载：多线程下载地图瓦片，提高效率。图像
                    
                    
Spring MVC 全面指南：从入门到精通的详细解析
                        一杯梅子酱
技术栈学习springmvcjava
                        
引言：SpringMVC，作为Spring框架的一个重要模块，为构建Web应用提供了强大的功能和灵活性。无论是初学者还是有一定经验的开发者，掌握SpringMVC都将显著提升你的Web开发技能。本文旨在为初学者提供一个全面且易于理解的学习路径，通过详细的知识点分析和实际案例，帮助你快速上手SpringMVC，让学习过程既深刻又高效。一、SpringMVC简介1.1什么是SpringMVC？Spri
                    
                    
入门MySQL——查询语法练习
                        K_un

                        
前言：前面几篇文章为大家介绍了DML以及DDL语句的使用方法，本篇文章将主要讲述常用的查询语法。其实MySQL官网给出了多个示例数据库供大家实用查询，下面我们以最常用的员工示例数据库为准，详细介绍各自常用的查询语法。1.员工示例数据库导入官方文档员工示例数据库介绍及下载链接：https://dev.mysql.com/doc/employee/en/employees-installation.h
                    
                    
Spring Boot中实现跨域请求
                        BABA8891
springboot后端java
                        
在SpringBoot中实现跨域请求（CORS，Cross-OriginResourceSharing）可以通过多种方式，以下是几种常见的方法：1.使用@CrossOrigin注解在SpringBoot中，你可以在控制器或者具体的请求处理方法上使用@CrossOrigin注解来允许跨域请求。在控制器上应用：importorg.springframework.web.bind.annotation.
                    
                    
WebMagic：强大的Java爬虫框架解析与实战
                        Aaron_945
Javajava爬虫开发语言
                        
文章目录引言官网链接WebMagic原理概述基础使用1.添加依赖2.编写PageProcessor高级使用1.自定义Pipeline2.分布式抓取优点结论引言在大数据时代，网络爬虫作为数据收集的重要工具，扮演着不可或缺的角色。Java作为一门广泛使用的编程语言，在爬虫开发领域也有其独特的优势。WebMagic是一个开源的Java爬虫框架，它提供了简单灵活的API，支持多线程、分布式抓取，以及丰富的
                    
                    
博客网站制作教程
                        2401_85194651
javamaven
                        
首先就是技术框架：后端：Java+SpringBoot数据库：MySQL前端：Vue.js数据库连接：JPA(JavaPersistenceAPI)1.项目结构blog-app/├──backend/│├──src/main/java/com/example/blogapp/││├──BlogApplication.java││├──config/│││└──DatabaseConfig.java
                    
                    
00. 这里整理了最全的爬虫框架（Java + Python）
                        有一只柴犬
爬虫系列爬虫javapython
                        
目录1、前言2、什么是网络爬虫3、常见的爬虫框架3.1、java框架3.1.1、WebMagic3.1.2、Jsoup3.1.3、HttpClient3.1.4、Crawler4j3.1.5、HtmlUnit3.1.6、Selenium3.2、Python框架3.2.1、Scrapy3.2.2、BeautifulSoup+Requests3.2.3、Selenium3.2.4、PyQuery3.2
                    
                    
ubuntu安装wordpress
                        lissettecarlr

                        
1安装nginx网上安装方式很多，这就就直接用apt-get了apt-getinstallnginx不用启动啥，然后直接在浏览器里面输入IP:80就能看到nginx的主页了。如果修改了一些配置可以使用下列命令重启一下systemctlrestartnginx.service2安装mysql输入安装前也可以更新一下软件源，在安装过程中将会让你输入数据库的密码。sudoapt-getinstallmy
                    
                    
计算机毕业设计PHP仓储综合管理系统（源码+程序+VUE+lw+部署）
                        java毕设程序源码王哥
php课程设计vue.js
                        
该项目含有源码、文档、程序、数据库、配套开发软件、软件安装教程。欢迎交流项目运行环境配置：phpStudy+Vscode+Mysql5.7+HBuilderX+Navicat11+Vue+Express。项目技术：原生PHP++Vue等等组成，B/S模式+Vscode管理+前后端分离等等。环境需要1.运行环境：最好是小皮phpstudy最新版，我们在这个版本上开发的。其他版本理论上也可以。2.开发
                    
                    
最简单将静态网页挂载到服务器上(不用nginx)
                        全能全知者
服务器nginx运维前端html笔记
                        
最简单将静态网页挂载到服务器上(不用nginx)如果随便弄个静态网页挂在服务器都要用nignx就太麻烦了，所以直接使用Apache来搭建一些简单前端静态网页会相对方便很多检查Web服务器服务状态：sudosystemctlstatushttpd#ApacheWeb服务器如果发现没有安装web服务器：安装Apache：sudoyuminstallhttpd启动Apache：sudosystemctl
                    
                                
继之前的线程循环加到窗口中运行
                                    3213213333332132
javathreadJFrameJPanel
                                    
之前写了有关java线程的循环执行和结束，因为想制作成exe文件，想把执行的效果加到窗口上，所以就结合了JFrame和JPanel写了这个程序，这里直接贴出代码，在窗口上运行的效果下面有附图。 
 

package thread;

import java.awt.Graphics;
import java.text.SimpleDateFormat;
import java.util
                                
                                
linux 常用命令
                                    BlueSkator
linux命令
                                    
1.grep 
相信这个命令可以说是大家最常用的命令之一了。尤其是查询生产环境的日志，这个命令绝对是必不可少的。 
但之前总是习惯于使用 （grep -n 关键字 文件名 ）查出关键字以及该关键字所在的行数，然后再用 （sed -n  '100,200p' 文件名），去查出该关键字之后的日志内容。 
但其实还有更简便的办法，就是用（grep  -B n、-A n、-C n 关键
                                
                                
php heredoc原文档和nowdoc语法
                                    dcj3sjt126com
PHPheredocnowdoc
                                    
<!doctype html>
<html lang="en">
<head>
    <meta charset="utf-8">
    <title>Current To-Do List</title>
</head>
<body>
<?
                                
                                
overflow的属性
                                    周华华
JavaScript
                                    
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
<html xmlns="http://www.w3.org/1999/xhtml&q
                                
                                
《我所了解的Java》——总体目录
                                    g21121
java
                                    
        准备用一年左右时间写一个系列的文章《我所了解的Java》，目录及内容会不断完善及调整。 
        在编写相关内容时难免出现笔误、代码无法执行、名词理解错误等，请大家及时指出，我会第一时间更正。 
   &n
                                
                                
[简单]docx4j常用方法小结
                                    53873039oycg
docx
                                    
        本代码基于docx4j-3.2.0，在office word 2007上测试通过。代码如下: 
         
import java.io.File;
import java.io.FileInputStream;
import ja
                                
                                
Spring配置学习
                                    云端月影
spring配置
                                    
 
首先来看一个标准的Spring配置文件 applicationContext.xml 
 
<?xml version="1.0" encoding="UTF-8"?> 
<beans xmlns="http://www.springframework.org/schema/beans" 
 xmlns:xsi=&q
                                
                                
Java新手入门的30个基本概念三
                                    aijuans
java新手java 入门
                                    
17.Java中的每一个类都是从Object类扩展而来的。　　18.object类中的equal和toString方法。　　equal用于测试一个对象是否同另一个对象相等。　　toString返回一个代表该对象的字符串,几乎每一个类都会重载该方法,以便返回当前状态的正确表示.(toString 方法是一个很重要的方法)　　 19.通用编程:任何类类型的所有值都可以同object类性的变量来代替。　
                                
                                
《2008 IBM Rational 软件开发高峰论坛会议》小记
                                    antonyup_2006
软件测试敏捷开发项目管理IBM活动
                                    
我一直想写些总结,用于交流和备忘,然都没提笔,今以一篇参加活动的感受小记开个头,呵呵! 
 
其实参加《2008 IBM Rational 软件开发高峰论坛会议》是9月4号,那天刚好调休.但接着项目颇为忙,所以今天在中秋佳节的假期里整理了下. 
 
参加这次活动是一个朋友给的一个邀请书,才知道有这样的一个活动,虽然现在项目暂时没用到IBM的解决方案,但觉的参与这样一个活动可以拓宽下视野和相关知识.
                                
                                
PL/SQL的过程编程,异常,声明变量,PL/SQL块
                                    百合不是茶
PL/SQL的过程编程异常PL/SQL块声明变量
                                    
PL/SQL; 
   
   过程;

    符号;

     变量;

     PL/SQL块;

     输出;

     异常;
 
  
  
PL/SQL 是过程语言(Procedural Language)与结构化查询语言(SQL)结合而成的编程语言PL/SQL 是对 SQL 的扩展,sql的执行时每次都要写操作
                                
                                
Mockito(三)--完整功能介绍
                                    bijian1013
持续集成mockito单元测试
                                    
        mockito官网：http://code.google.com/p/mockito/，打开documentation可以看到官方最新的文档资料。 
一.使用mockito验证行为 
//首先要import Mockito
import static org.mockito.Mockito.*;

//mo
                                
                                
精通Oracle10编程SQL(8)使用复合数据类型
                                    bijian1013
oracle数据库plsql
                                    
/*
 *使用复合数据类型
 */

--PL/SQL记录
--定义PL/SQL记录
--自定义PL/SQL记录
DECLARE
  TYPE emp_record_type IS RECORD(
     name emp.ename%TYPE,
     salary emp.sal%TYPE,
     dno emp.deptno%TYPE
  );
  emp_
                                
                                
【Linux常用命令一】grep命令
                                    bit1129
Linux常用命令
                                    
grep命令格式 
  
grep [option] pattern [file-list] 
  
  
grep命令用于在指定的文件(一个或者多个,file-list)中查找包含模式串(pattern)的行,[option]用于控制grep命令的查找方式。 
  
pattern可以是普通字符串，也可以是正则表达式，当查找的字符串包含正则表达式字符或者特
                                
                                
mybatis3入门学习笔记
                                    白糖_
sqlibatisqqjdbc配置管理
                                    
MyBatis 的前身就是iBatis，是一个数据持久层(ORM)框架。  MyBatis 是支持普通 SQL 查询，存储过程和高级映射的优秀持久层框架。MyBatis对JDBC进行了一次很浅的封装。 
  
以前也学过iBatis，因为MyBatis是iBatis的升级版本，最初以为改动应该不大，实际结果是MyBatis对配置文件进行了一些大的改动，使整个框架更加方便人性化。
                                
                                
Linux 命令神器：lsof 入门
                                    ronin47
lsof
                                    
       
lsof是系统管理/安全的尤伯工具。我大多数时候用它来从系统获得与网络连接相关的信息，但那只是这个强大而又鲜为人知的应用的第一步。将这个工具称之为lsof真实名副其实，因为它是指“列出打开文件（lists openfiles）”。而有一点要切记，在Unix中一切（包括网络套接口）都是文件。 
有趣的是，lsof也是有着最多
                                
                                
java实现两个大数相加，可能存在溢出。
                                    bylijinnan
java实现
                                    

import java.math.BigInteger;
import java.util.regex.Matcher;
import java.util.regex.Pattern;


public class BigIntegerAddition {

	/**
	 * 题目：java实现两个大数相加，可能存在溢出。
	 * 如123456789 + 987654321
                                
                                
Kettle学习资料分享，附大神用Kettle的一套流程完成对整个数据库迁移方法
                                    Kai_Ge
Kettle
                                    
Kettle学习资料分享 
  
Kettle 3.2 使用说明书 
目录 
概述..........................................................................................................................................7 
1.Kettle 资源库管
                                
                                
[货币与金融]钢之炼金术士
                                    comsci
金融
                                    
 
 
       自古以来,都有一些人在从事炼金术的工作.........但是很少有成功的 
 
       那么随着人类在理论物理和工程物理上面取得的一些突破性进展...... 
 
       炼金术这个古老
                                
                                
Toast原来也可以多样化
                                    dai_lm
androidtoast
                                    
Style 1： 默认 
 

Toast def = Toast.makeText(this, "default", Toast.LENGTH_SHORT);
def.show();
 
Style 2： 顶部显示 
 

Toast top = Toast.makeText(this, "top", Toast.LENGTH_SHORT);
t
                                
                                
java数据计算的几种解决方法3
                                    datamachine
javahadoopibatisr-languer
                                    
4、iBatis 
    简单敏捷因此强大的数据计算层。和Hibernate不同，它鼓励写SQL，所以学习成本最低。同时它用最小的代价实现了计算脚本和JAVA代码的解耦，只用20%的代价就实现了hibernate 80%的功能,没实现的20%是计算脚本和数据库的解耦。 
    复杂计算环境是它的弱项，比如：分布式计算、复杂计算、非数据
                                
                                
向网页中插入透明Flash的方法和技巧
                                    dcj3sjt126com
htmlWebFlash
                                    
将 
Flash 作品插入网页的时候，我们有时候会需要将它设为透明，有时候我们需要在Flash的背面插入一些漂亮的图片，搭配出漂亮的效果……下面我们介绍一些将Flash插入网页中的一些透明的设置技巧。  
　　一、Swf透明、无坐标控制　　首先教大家最简单的插入Flash的代码，透明，无坐标控制： 　　注意wmode="transparent"是控制Flash是否透明
                                
                                
ios UICollectionView的使用
                                    dcj3sjt126com

                                    
UICollectionView的使用有两种方法，一种是继承UICollectionViewController，这个Controller会自带一个UICollectionView；另外一种是作为一个视图放在普通的UIViewController里面。 
个人更喜欢第二种。下面采用第二种方式简单介绍一下UICollectionView的使用。 
1.UIViewController实现委托，代码如
                                
                                
Eos平台java公共逻辑
                                    蕃薯耀
Eos平台java公共逻辑Eos平台java公共逻辑
                                    
 Eos平台java公共逻辑 
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 
蕃薯耀 2015年6月1日 17:20:4
                                
                                
SpringMVC4零配置--Web上下文配置【MvcConfig】
                                    hanqunfeng
springmvc4
                                    
与SpringSecurity的配置类似，spring同样为我们提供了一个实现类WebMvcConfigurationSupport和一个注解@EnableWebMvc以帮助我们减少bean的声明。 
  
applicationContext-MvcConfig.xml 
<!-- 启用注解，并定义组件查找规则 ，mvc层只负责扫描@Controller -->
	<
                                
                                
解决ie和其他浏览器poi下载excel文件名乱码
                                    jackyrong
Excel
                                    
   使用poi,做传统的excel导出，然后想在浏览器中，让用户选择另存为，保存用户下载的xls文件，这个时候，可能的是在ie下出现乱码（ie,9,10,11),但在firefox,chrome下没乱码， 
 
因此必须综合判断，编写一个工具类： 
 
 
     

/**
     * 
     * @Title: pro
                                
                                
挥洒泪水的青春
                                    lampcy
编程生活程序员
                                    
2015年2月28日，我辞职了，离开了相处一年的触控，转过身--挥洒掉泪水，毅然来到了兄弟连，背负着许多的不解、质疑——”你一个零基础、脑子又不聪明的人，还敢跨行业，选择Unity3D？“，”真是不自量力••••••“，”真是初生牛犊不怕虎•••••“，••••••我只是淡淡一笑，拎着行李----坐上了通向挥洒泪水的青春之地——兄弟连！ 
这就是我青春的分割线，不后悔，只会去用泪水浇灌——已经来到
                                
                                
稳增长之中国股市两点意见-----严控做空，建立涨跌停版停牌重组机制
                                    nannan408

                                    
   对于股市，我们国家的监管还是有点拼的，但始终拼不过飞流直下的恐慌，为什么呢？ 
   笔者首先支持股市的监管。对于股市越管越荡的现象，笔者认为首先是做空力量超过了股市自身的升力，并且对于跌停停牌重组的快速反应还没建立好，上市公司对于股价下跌没有很好的利好支撑。 
   我们来看美国和香港是怎么应对股灾的。美国是靠禁止重要股票做空，在
                                
                                
动态设置iframe高度(iframe高度自适应)
                                    Rainbow702
JavaScriptiframecontentDocument高度自适应局部刷新
                                    
如果需要对画面中的部分区域作局部刷新，大家可能都会想到使用ajax。 
但有些情况下，须使用在页面中嵌入一个iframe来作局部刷新。 
对于使用iframe的情况，发现有一个问题，就是iframe中的页面的高度可能会很高，但是外面页面并不会被iframe内部页面给撑开，如下面的结构： 
<div id="content">
    <div id=&quo
                                
                                
用Rapael做图表
                                    tntxia
rap
                                    
function drawReport(paper,attr,data){ 
     
    var width = attr.width; 
    var height = attr.height; 
     
    var max = 0; 
  &nbs
                                
                                
HTML5 bootstrap2网页兼容（支持IE10以下）
                                    xiaoluode
html5bootstrap
                                    
<!DOCTYPE html>
<html>
<head lang="zh-CN">
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
                                
                
            
        
    

    

        

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