day06_hive和hbase

一. hive-2.3.2

下载：http://mirrors.hust.edu.cn/apache/hive/hive-2.3.2/apache-hive-2.3.2-bin.tar.gz

需要配置mysql 的root用户其他IP的访问权限

conf/hive-site.xml

<configuration>
        
        <property>
                <name>javax.jdo.option.ConnectionURLname>
                <value>jdbc:mysql://hadoop1:3306/hive?createDatabaseIfNotExist=true&useSSL=falsevalue>
        property>
        <property>
                <name>javax.jdo.option.ConnectionDriverNamename>
                <value>com.mysql.jdbc.Drivervalue>
        property>
        
        <property>
                <name>javax.jdo.option.ConnectionUserNamename>
                <value>rootvalue>
        property>
        
        <property>
                <name>javax.jdo.option.ConnectionPasswordname>
                <value>123456value>
        property>
        <property>
                <name>datanucleus.autoCreateSchemaname>
                <value>truevalue>
        property>
        <property>
                <name>datanucleus.fixedDatastorename>
                <value>truevalue>
        property>
        <property>
                <name>datanucleus.autoCreateTablesname>
                <value>Truevalue>
        property>
        <property>
                <name>hive.metastore.schema.verificationname>
                <value>falsevalue>
        property>
configuration>

启动HIVE时 也要把hadoop启动起来

基本使用

创建表

hive> create table t_order(id int, name string, rongliang string, price double)
    > row format delimited
    > fields terminated by '\t'
    > ;

文本文件 xxx.data

0000101 iphone6plus     64G     6288
0000102 xiaominote      64G     3288
0000103 iphone5s        64G     2288
0000104 mi3     64G     6611
0000105 xiaomi  128G    628     
0000106 huawei  64G     4244    
0000107 zhongxing       64G     6288

数据既可以从本地导入， 也可以从hdfs中导入, 下面是本地倒入

hive>load data local inpath '/home/hadoop/hivetestdata/xxx.data' into table t_order;

查询数据

hive> select * from t_order;
OK
101 iphone6plus 64G 6288.0
102 xiaominote  64G 3288.0
103 iphone5s    64G 2288.0
104 mi3 64G 6611.0
105 xiaomi  128G    628.0
106 huawei  64G 4244.0
107 zhongxing   64G 6288.0
Time taken: 2.169 seconds, Fetched: 7 row(s)
hive> select count(1) from t_order;

hive> select count(1) from t_order;
WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
Query ID = hadoop_20180401142812_93965d7d-c675-495e-8f68-1e5820dc70dc
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Job = job_1522238071822_0001, Tracking URL = http://hadoop1:8088/proxy/application_1522238071822_0001/
Kill Command = /data/hadoop-2.7.5/bin/hadoop job  -kill job_1522238071822_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2018-04-01 14:28:36,974 Stage-1 map = 0%,  reduce = 0%
2018-04-01 14:28:55,665 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.19 sec
2018-04-01 14:29:13,731 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 8.57 sec
MapReduce Total cumulative CPU time: 8 seconds 570 msec
Ended Job = job_1522238071822_0001
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 8.57 sec   HDFS Read: 8041 HDFS Write: 101 SUCCESS
Total MapReduce CPU Time Spent: 8 seconds 570 msec
OK
7
Time taken: 64.647 seconds, Fetched: 1 row(s)

二. hbase-1.2.6

hbase是一个类似Nosql数据库，是一个hdfs文件系统上的数据库，比文件系统更适合随机读写
因为是使用hdfs作为基础 所以 hbase和hadoop的版本需要要很好的支持

根据官方给出的测试， 我是使用的Hadoop-2.7.5， 所以可以使用hbase1.2.6

hbase-env.sh

export HBASE_MANAGES_ZK=false

hbase-site.xml

<configuration>
    <property>
        <name>hbase.rootdirname>
        <value>hdfs://ns1/hbasevalue>
    property>
    <property>
        <name>hbase.cluster.distributedname>
        <value>truevalue>
    property>
    <property>
        <name>hbase.zookeeper.quorumname>
        <value>hadoop1:2181,hadoop2:2181,hadoop3:2181value>
    property>
configuration>

core-site.xml hdfs-site.xml

把hadoop下的 core-site.xml 和 hdfs-site.xml  拷贝过来

regionservers

hadoop1
hadoop2
hadoop3

部署

scp -r hbase-1.2.6/ hadoop2:/data/
scp -r hbase-1.2.6/ hadoop3:/data/

机器及角色分配如下:

hadoop1	hadoop2	hadoop3
HMaster	HMaster
HRegionServer	HRegionServer	HRegionServer

hadoop1

# ./start-hbase.sh  #启动master和 HRegionServer

hadoop2

# ./hbase-daemon.sh  start master   #单独启动master