sqoop基本理论知识,安装搭建及常用命令

Sqoop学习之路

目录

· 一、概述

· 二、工作机制

· 三、安装

· 1、前提概述

· 2、软件下载

· 3、安装步骤

· 四、Sqoop的基本命令

· 基本操作

· 示例

· 五、Sqoop的数据导入

· 1、从RDBMS导入到HDFS中

· 2、把MySQL数据库中的表数据导入到Hive中

· 3、把MySQL数据库中的表数据导入到hbase

一、概述

sqoop 是 apache 旗下一款“Hadoop 和关系数据库服务器之间传送数据”的工具。

核心的功能有两个：

导入、迁入

导出、迁出

导入数据：MySQL，Oracle 导入数据到 Hadoop 的 HDFS、HIVE、HBASE 等数据存储系统

导出数据：从 Hadoop 的文件系统中导出数据到关系数据库 mysql 等 Sqoop 的本质还是一个命令行工具，和 HDFS，Hive 相比，并没有什么高深的理论。

sqoop：

工具：本质就是迁移数据， 迁移的方式：就是把sqoop的迁移命令转换成MR程序

hive

工具，本质就是执行计算，依赖于HDFS存储数据，把SQL转换成MR程序

 

二、工作机制

将导入或导出命令翻译成 MapReduce 程序来实现 在翻译出的 MapReduce 中主要是对 InputFormat 和 OutputFormat 进行定制

1、前提概述

将来sqoop在使用的时候有可能会跟那些系统或者组件打交道？

HDFS， MapReduce， YARN， ZooKeeper， Hive， HBase， MySQL

sqoop就是一个工具， 只需要在一个节点上进行安装即可。

 

补充一点： 如果你的sqoop工具将来要进行hive或者hbase等等的系统和MySQL之间的交互

 

你安装的SQOOP软件的节点一定要包含以上你要使用的集群或者软件系统的安装包

 

补充一点： 将来要使用的azakban这个软件 除了会调度 hadoop的任务或者hbase或者hive的任务之外， 还会调度sqoop的任务

 

azkaban这个软件的安装节点也必须包含以上这些软件系统的客户端/2、

2、软件下载

下载地址http://mirrors.hust.edu.cn/apache/

sqoop版本说明

绝大部分企业所使用的sqoop的版本都是 sqoop1

sqoop-1.4.6 或者 sqoop-1.4.7 它是 sqoop1

sqoop-1.99.4----都是 sqoop2

此处使用sqoop-1.4.6版本sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz

3、安装步骤

（1）上传解压缩安装包到指定目录

因为之前hive只是安装在hadoop3机器上，所以sqoop也同样安装在hadoop3机器上

[hadoop@hadoop3 ~]$ tar -zxvf sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz -C apps/

（2）进入到 conf 文件夹，找到 sqoop-env-template.sh，修改其名称为 sqoop-env.sh cd conf

[hadoop@hadoop3 ~]$ cd apps/

[hadoop@hadoop3 apps]$ ls

apache-hive-2.3.3-bin  hadoop-2.7.5  hbase-1.2.6  sqoop-1.4.6.bin__hadoop-2.0.4-alpha  zookeeper-3.4.10

[hadoop@hadoop3 apps]$ mv sqoop-1.4.6.bin__hadoop-2.0.4-alpha/ sqoop-1.4.6

[hadoop@hadoop3 apps]$ cd sqoop-1.4.6/conf/

[hadoop@hadoop3 conf]$ ls

oraoop-site-template.xml  sqoop-env-template.sh    sqoop-site.xml

sqoop-env-template.cmd    sqoop-site-template.xml

[hadoop@hadoop3 conf]$ mv sqoop-env-template.sh sqoop-env.sh

（3）修改 sqoop-env.sh

[hadoop@hadoop3 conf]$ vi sqoop-env.sh 

export HADOOP_COMMON_HOME=/home/hadoop/apps/hadoop-2.7.5

#Set path to where hadoop-*-core.jar is available 

export HADOOP_MAPRED_HOME=/home/hadoop/apps/hadoop-2.7.5

#set the path to where bin/hbase is available

export HBASE_HOME=/home/hadoop/apps/hbase-1.2.6

#Set the path to where bin/hive is available

export HIVE_HOME=/home/hadoop/apps/apache-hive-2.3.3-bin

#Set the path for where zookeper config dir is

export ZOOCFGDIR=/home/hadoop/apps/zookeeper-3.4.10/conf

为什么在sqoop-env.sh 文件中会要求分别进行 common和mapreduce的配置呢？？？

在apache的hadoop的安装中；四大组件都是安装在同一个hadoop_home中的

但是在CDH, HDP中， 这些组件都是可选的。

在安装hadoop的时候，可以选择性的只安装HDFS或者YARN，

CDH,HDP在安装hadoop的时候，会把HDFS和MapReduce有可能分别安装在不同的地方。

（4）加入 mysql 驱动包到 sqoop1.4.6/lib 目录下

[hadoop@hadoop3 ~]$ cp mysql-connector-java-5.1.40-bin.jar apps/sqoop-1.4.6/lib/

 

（5）配置系统环境变量

[hadoop@hadoop3 ~]$ vi .bashrc 

#Sqoop

export SQOOP_HOME=/home/hadoop/apps/sqoop-1.4.6

export PATH=$PATH:$SQOOP_HOME/bin

 

保存退出使其立即生效

[hadoop@hadoop3 ~]$ source .bashrc 

（6）验证安装是否成功

 sqoop-version 或者 sqoop version

注意,如果报错Error: Could not find or load main class org.apache.sqoop.Sqoop,需要下载sqoop-1.x.x.jar放入

${SQOOP_HOME}/lib目录下(sqoop的lib目录).

下载地址:http://central.maven.org/maven2/org/apache/sqoop/sqoop/1.4.7/

四、Sqoop的基本命令

基本操作

首先，我们可以使用 sqoop help 来查看，sqoop 支持哪些命令

[hadoop@hadoop3 ~]$ sqoop help

Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.

Please set $HCAT_HOME to the root of your HCatalog installation.

Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.

Please set $ACCUMULO_HOME to the root of your Accumulo installation.

18/04/12 13:37:19 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

usage: sqoop COMMAND [ARGS]

Available commands:

  codegen            Generate code to interact with database records

  create-hive-table  Import a table definition into Hive

  eval               Evaluate a SQL statement and display the results

  export             Export an HDFS directory to a database table

  help               List available commands

  import             Import a table from a database to HDFS

  import-all-tables  Import tables from a database to HDFS

  import-mainframe   Import datasets from a mainframe server to HDFS

  job                Work with saved jobs

  list-databases     List available databases on a server

  list-tables        List available tables in a database

  merge              Merge results of incremental imports

  metastore          Run a standalone Sqoop metastore

  version            Display version information

See 'sqoop help COMMAND' for information on a specific command.

[hadoop@hadoop3 ~]$

然后得到这些支持了的命令之后，如果不知道使用方式，可以使用 sqoop command 的方式 来查看某条具体命令的使用方式，比如：

sqoop help import 查看import命令的帮助文档.

 

示例

列出MySQL数据有哪些数据库

[hadoop@hadoop3 ~]$ sqoop list-databases \

> --connect jdbc:mysql://hadoop1:3306/ \

> --username root \

> --password root

Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.

Please set $HCAT_HOME to the root of your HCatalog installation.

Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.

Please set $ACCUMULO_HOME to the root of your Accumulo installation.

18/04/12 13:43:51 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

18/04/12 13:43:51 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.

18/04/12 13:43:51 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.information_schema

hivedb

mysql

performance_schema

test

[hadoop@hadoop3 ~]$

 

列出MySQL中的某个数据库有哪些数据表：

[hadoop@hadoop3 ~]$ sqoop list-tables \
> --connect jdbc:mysql://hadoop1:3306/mysql \
> --username root \
> --password root

 View Code

[hadoop@hadoop3 ~]$ sqoop list-tables \

> --connect jdbc:mysql://hadoop1:3306/mysql \

> --username root \

> --password root

Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.

Please set $HCAT_HOME to the root of your HCatalog installation.

Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.

Please set $ACCUMULO_HOME to the root of your Accumulo installation.

18/04/12 13:46:21 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

18/04/12 13:46:21 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.

18/04/12 13:46:21 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

columns_priv

db

event

func

general_log

help_category

help_keyword

help_relation

help_topic

innodb_index_stats

innodb_table_stats

ndb_binlog_index

plugin

proc

procs_priv

proxies_priv

servers

slave_master_info

slave_relay_log_info

slave_worker_info

slow_log

tables_priv

time_zone

time_zone_leap_second

time_zone_name

time_zone_transition

time_zone_transition_type

user

[hadoop@hadoop3 ~]$

创建一张跟mysql中的help_keyword表一样的hive表hk：

sqoop create-hive-table \

--connect jdbc:mysql://hadoop1:3306/mysql \

--username root \

--password root \

--table help_keyword \

--hive-table hk

 View Code

[hadoop@hadoop3 ~]$ sqoop create-hive-table \

> --connect jdbc:mysql://hadoop1:3306/mysql \

> --username root \

> --password root \

> --table help_keyword \

> --hive-table hk

Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.

Please set $HCAT_HOME to the root of your HCatalog installation.

Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.

Please set $ACCUMULO_HOME to the root of your Accumulo installation.

18/04/12 13:50:20 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

18/04/12 13:50:20 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.

18/04/12 13:50:20 INFO tool.BaseSqoopTool: Using Hive-specific delimiters for output. You can override

18/04/12 13:50:20 INFO tool.BaseSqoopTool: delimiters with --fields-terminated-by, etc.

18/04/12 13:50:20 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

18/04/12 13:50:21 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `help_keyword` AS t LIMIT 1

18/04/12 13:50:21 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `help_keyword` AS t LIMIT 1

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

18/04/12 13:50:23 INFO hive.HiveImport: Loading uploaded data into Hive

18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Class path contains multiple SLF4J bindings.

18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/home/hadoop/apps/apache-hive-2.3.3-bin/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]

18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]

18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]

18/04/12 13:50:36 INFO hive.HiveImport:

18/04/12 13:50:36 INFO hive.HiveImport: Logging initialized using configuration in jar:file:/home/hadoop/apps/apache-hive-2.3.3-bin/lib/hive-common-2.3.3.jar!/hive-log4j2.properties Async: true

18/04/12 13:50:50 INFO hive.HiveImport: OK

18/04/12 13:50:50 INFO hive.HiveImport: Time taken: 11.651 seconds

18/04/12 13:50:51 INFO hive.HiveImport: Hive import complete.

五、Sqoop的数据导入

“导入工具”导入单个表从 RDBMS 到 HDFS。表中的每一行被视为 HDFS 的记录。所有记录 都存储为文本文件的文本数据（或者 Avro、sequence 文件等二进制数据） 

1、从RDBMS导入到HDFS中

语法格式

sqoop import (generic-args) (import-args)

常用参数

--connect <jdbc-uri> jdbc 连接地址

--connection-manager <class-name> 连接管理者

--driver <class-name> 驱动类

--hadoop-mapred-home <dir> $HADOOP_MAPRED_HOME

--help help 信息

-P 从命令行输入密码

--password <password> 密码

--username <username> 账号

--verbose 打印流程信息

--connection-param-file <filename> 可选参数

示例

普通导入：导入mysql库中的help_keyword的数据到HDFS上

导入的默认路径：/user/hadoop/help_keyword

sqoop import   \

--connect jdbc:mysql://hadoop1:3306/mysql   \

--username root  \

--password root   \

--table help_keyword   \

-m 1

 View Code

[hadoop@hadoop3 ~]$ sqoop import   \

> --connect jdbc:mysql://hadoop1:3306/mysql   \

> --username root  \

> --password root   \

> --table help_keyword   \

> -m 1

Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.

Please set $HCAT_HOME to the root of your HCatalog installation.

Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.

Please set $ACCUMULO_HOME to the root of your Accumulo installation.

18/04/12 13:53:48 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

18/04/12 13:53:48 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.

18/04/12 13:53:48 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

18/04/12 13:53:48 INFO tool.CodeGenTool: Beginning code generation

18/04/12 13:53:49 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `help_keyword` AS t LIMIT 1

18/04/12 13:53:49 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `help_keyword` AS t LIMIT 1

18/04/12 13:53:49 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/apps/hadoop-2.7.5

注: /tmp/sqoop-hadoop/compile/979d87b9521d0a09ee6620060a112d60/help_keyword.java使用或覆盖了已过时的 API。

注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。

18/04/12 13:53:51 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/979d87b9521d0a09ee6620060a112d60/help_keyword.jar

18/04/12 13:53:51 WARN manager.MySQLManager: It looks like you are importing from mysql.

18/04/12 13:53:51 WARN manager.MySQLManager: This transfer can be faster! Use the --direct

18/04/12 13:53:51 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.

18/04/12 13:53:51 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)

18/04/12 13:53:51 INFO mapreduce.ImportJobBase: Beginning import of help_keyword

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

18/04/12 13:53:52 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar

18/04/12 13:53:53 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

18/04/12 13:53:58 INFO db.DBInputFormat: Using read commited transaction isolation

18/04/12 13:53:58 INFO mapreduce.JobSubmitter: number of splits:1

18/04/12 13:53:59 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1523510178850_0001

18/04/12 13:54:00 INFO impl.YarnClientImpl: Submitted application application_1523510178850_0001

18/04/12 13:54:00 INFO mapreduce.Job: The url to track the job: http://hadoop3:8088/proxy/application_1523510178850_0001/

18/04/12 13:54:00 INFO mapreduce.Job: Running job: job_1523510178850_0001

18/04/12 13:54:17 INFO mapreduce.Job: Job job_1523510178850_0001 running in uber mode : false

18/04/12 13:54:17 INFO mapreduce.Job:  map 0% reduce 0%

18/04/12 13:54:33 INFO mapreduce.Job:  map 100% reduce 0%

18/04/12 13:54:34 INFO mapreduce.Job: Job job_1523510178850_0001 completed successfully

18/04/12 13:54:35 INFO mapreduce.Job: Counters: 30

    File System Counters

        FILE: Number of bytes read=0

        FILE: Number of bytes written=142965

        FILE: Number of read operations=0

        FILE: Number of large read operations=0

        FILE: Number of write operations=0

        HDFS: Number of bytes read=87

        HDFS: Number of bytes written=8264

        HDFS: Number of read operations=4

        HDFS: Number of large read operations=0

        HDFS: Number of write operations=2

    Job Counters

        Launched map tasks=1

        Other local map tasks=1

        Total time spent by all maps in occupied slots (ms)=12142

        Total time spent by all reduces in occupied slots (ms)=0

        Total time spent by all map tasks (ms)=12142

        Total vcore-milliseconds taken by all map tasks=12142

        Total megabyte-milliseconds taken by all map tasks=12433408

    Map-Reduce Framework

        Map input records=619

        Map output records=619

        Input split bytes=87

        Spilled Records=0

        Failed Shuffles=0

        Merged Map outputs=0

        GC time elapsed (ms)=123

        CPU time spent (ms)=1310

        Physical memory (bytes) snapshot=93212672

        Virtual memory (bytes) snapshot=2068234240

        Total committed heap usage (bytes)=17567744

    File Input Format Counters

        Bytes Read=0

    File Output Format Counters

        Bytes Written=8264

18/04/12 13:54:35 INFO mapreduce.ImportJobBase: Transferred 8.0703 KB in 41.8111 seconds (197.6507 bytes/sec)

18/04/12 13:54:35 INFO mapreduce.ImportJobBase: Retrieved 619 records.

[hadoop@hadoop3 ~]$

 

查看导入的文件

[hadoop@hadoop4 ~]$ hadoop fs -cat /user/hadoop/help_keyword/part-m-00000

 

导入： 指定分隔符和导入路径

sqoop import   \

--connect jdbc:mysql://hadoop1:3306/mysql   \

--username root  \

--password root   \

--table help_keyword   \

--target-dir /user/hadoop11/my_help_keyword1  \

--fields-terminated-by '\t'  \

-m 2

 

导入数据：带where条件

sqoop import   \

--connect jdbc:mysql://hadoop1:3306/mysql   \

--username root  \

--password root   \

--where "name='STRING' " \

--table help_keyword   \

--target-dir /sqoop/hadoop11/myoutport1  \

-m 1

 

查询指定列

sqoop import   \

--connect jdbc:mysql://hadoop1:3306/mysql   \

--username root  \

--password root   \

--columns "name" \

--where "name='STRING' " \

--table help_keyword  \

--target-dir /sqoop/hadoop11/myoutport22  \

-m 1

selct name from help_keyword where name = "string"

 

导入：指定自定义查询SQL

sqoop import   \

--connect jdbc:mysql://hadoop1:3306/  \

--username root  \

--password root   \

--target-dir /user/hadoop/myimport33_1  \

--query 'select help_keyword_id,name from mysql.help_keyword where $CONDITIONS and name = "STRING"' \

--split-by  help_keyword_id \

--fields-terminated-by '\t'  \

-m 4

 

在以上需要按照自定义SQL语句导出数据到HDFS的情况下：
1、引号问题，要么外层使用单引号，内层使用双引号，$CONDITIONS的$符号不用转义， 要么外层使用双引号，那么内层使用单引号，然后$CONDITIONS的$符号需要转义
2、自定义的SQL语句中必须带有WHERE \$CONDITIONS

2、把MySQL数据库中的表数据导入到Hive中

Sqoop 导入关系型数据到 hive 的过程是先导入到 hdfs，然后再 load 进入 hive

普通导入：数据存储在默认的default hive库中，表名就是对应的mysql的表名：

sqoop import   \--connect jdbc:mysql://hadoop1:3306/mysql   \

--username root  \

--password root   \

--table help_keyword   \

--hive-import \-m 1

导入过程

第一步：导入mysql.help_keyword的数据到hdfs的默认路径
第二步：自动仿造mysql.help_keyword去创建一张hive表, 创建在默认的default库中
第三步：把临时目录中的数据导入到hive表中

 

查看数据

[hadoop@hadoop3 ~]$ hadoop fs -cat /user/hive/warehouse/help_keyword/part-m-00000

 

指定行分隔符和列分隔符，指定hive-import，指定覆盖导入，指定自动创建hive表，指定表名，指定删除中间结果数据目录

sqoop import  \--connect jdbc:mysql://hadoop1:3306/mysql  \

--username root  \

--password root  \

--table help_keyword  \

--fields-terminated-by "\t"  \

--lines-terminated-by "\n"  \

--hive-import  \

--hive-overwrite  \

--create-hive-table  \

--delete-target-dir \

--hive-database  mydb_test \

--hive-table new_help_keyword

 报错原因是hive-import 当前这个导入命令。 sqoop会自动给创建hive的表。 但是不会自动创建不存在的库

 

手动创建mydb_test数据块

hive> create database mydb_test;

OK

Time taken: 6.147 seconds

hive> 

之后再执行上面的语句没有报错

 

查询一下

select * from new_help_keyword limit 10;

 

上面的导入语句等价于

sqoop import  \--connect jdbc:mysql://hadoop1:3306/mysql  \

--username root  \

--password root  \

--table help_keyword  \

--fields-terminated-by "\t"  \

--lines-terminated-by "\n"  \

--hive-import  \

--hive-overwrite  \

--create-hive-table  \

--hive-table  mydb_test.new_help_keyword  \

--delete-target-dir

增量导入

执行增量导入之前，先清空hive数据库中的help_keyword表中的数据

truncate table help_keyword;

sqoop import   \--connect jdbc:mysql://hadoop1:3306/mysql   \

--username root  \

--password root   \

--table help_keyword  \

--target-dir /user/hadoop/myimport_add  \

--incremental  append  \

--check-column  help_keyword_id \

--last-value 500  \-m 1

语句执行成功

 View Code

[hadoop@hadoop3 ~]$ sqoop import   \

> --connect jdbc:mysql://hadoop1:3306/mysql   \

> --username root  \

> --password root   \

> --table help_keyword  \

> --target-dir /user/hadoop/myimport_add  \

> --incremental  append  \

> --check-column  help_keyword_id \

> --last-value 500  \

> -m 1

Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.

Please set $HCAT_HOME to the root of your HCatalog installation.

Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.

Please set $ACCUMULO_HOME to the root of your Accumulo installation.

18/04/12 22:01:07 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

18/04/12 22:01:08 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.

18/04/12 22:01:08 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

18/04/12 22:01:08 INFO tool.CodeGenTool: Beginning code generation

18/04/12 22:01:08 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `help_keyword` AS t LIMIT 1

18/04/12 22:01:08 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `help_keyword` AS t LIMIT 1

18/04/12 22:01:08 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/apps/hadoop-2.7.5

注: /tmp/sqoop-hadoop/compile/a51619d1ef8c6e4b112a209326ed9e0f/help_keyword.java使用或覆盖了已过时的 API。

注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。

18/04/12 22:01:11 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/a51619d1ef8c6e4b112a209326ed9e0f/help_keyword.jar

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

18/04/12 22:01:12 INFO tool.ImportTool: Maximal id query for free form incremental import: SELECT MAX(`help_keyword_id`) FROM `help_keyword`

18/04/12 22:01:12 INFO tool.ImportTool: Incremental import based on column `help_keyword_id`

18/04/12 22:01:12 INFO tool.ImportTool: Lower bound value: 500

18/04/12 22:01:12 INFO tool.ImportTool: Upper bound value: 618

18/04/12 22:01:12 WARN manager.MySQLManager: It looks like you are importing from mysql.

18/04/12 22:01:12 WARN manager.MySQLManager: This transfer can be faster! Use the --direct

18/04/12 22:01:12 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.

18/04/12 22:01:12 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)

18/04/12 22:01:12 INFO mapreduce.ImportJobBase: Beginning import of help_keyword

18/04/12 22:01:12 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar

18/04/12 22:01:12 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

18/04/12 22:01:17 INFO db.DBInputFormat: Using read commited transaction isolation

18/04/12 22:01:17 INFO mapreduce.JobSubmitter: number of splits:1

18/04/12 22:01:17 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1523510178850_0010

18/04/12 22:01:19 INFO impl.YarnClientImpl: Submitted application application_1523510178850_0010

18/04/12 22:01:19 INFO mapreduce.Job: The url to track the job: http://hadoop3:8088/proxy/application_1523510178850_0010/

18/04/12 22:01:19 INFO mapreduce.Job: Running job: job_1523510178850_0010

18/04/12 22:01:30 INFO mapreduce.Job: Job job_1523510178850_0010 running in uber mode : false

18/04/12 22:01:30 INFO mapreduce.Job:  map 0% reduce 0%

18/04/12 22:01:40 INFO mapreduce.Job:  map 100% reduce 0%

18/04/12 22:01:40 INFO mapreduce.Job: Job job_1523510178850_0010 completed successfully

18/04/12 22:01:41 INFO mapreduce.Job: Counters: 30

    File System Counters

        FILE: Number of bytes read=0

        FILE: Number of bytes written=143200

        FILE: Number of read operations=0

        FILE: Number of large read operations=0

        FILE: Number of write operations=0

        HDFS: Number of bytes read=87

        HDFS: Number of bytes written=1576

        HDFS: Number of read operations=4

        HDFS: Number of large read operations=0

        HDFS: Number of write operations=2

    Job Counters

        Launched map tasks=1

        Other local map tasks=1

        Total time spent by all maps in occupied slots (ms)=7188

        Total time spent by all reduces in occupied slots (ms)=0

        Total time spent by all map tasks (ms)=7188

        Total vcore-milliseconds taken by all map tasks=7188

        Total megabyte-milliseconds taken by all map tasks=7360512

    Map-Reduce Framework

        Map input records=118

        Map output records=118

        Input split bytes=87

        Spilled Records=0

        Failed Shuffles=0

        Merged Map outputs=0

        GC time elapsed (ms)=86

        CPU time spent (ms)=870

        Physical memory (bytes) snapshot=95576064

        Virtual memory (bytes) snapshot=2068234240

        Total committed heap usage (bytes)=18608128

    File Input Format Counters

        Bytes Read=0

    File Output Format Counters

        Bytes Written=1576

18/04/12 22:01:41 INFO mapreduce.ImportJobBase: Transferred 1.5391 KB in 28.3008 seconds (55.6875 bytes/sec)

18/04/12 22:01:41 INFO mapreduce.ImportJobBase: Retrieved 118 records.

18/04/12 22:01:41 INFO util.AppendUtils: Creating missing output directory - myimport_add

18/04/12 22:01:41 INFO tool.ImportTool: Incremental import complete! To run another incremental import of all data following this import, supply the following arguments:

18/04/12 22:01:41 INFO tool.ImportTool:  --incremental append

18/04/12 22:01:41 INFO tool.ImportTool:   --check-column help_keyword_id

18/04/12 22:01:41 INFO tool.ImportTool:   --last-value 618

18/04/12 22:01:41 INFO tool.ImportTool: (Consider saving this with 'sqoop job --create')

[hadoop@hadoop3 ~]$

 查看结果

 

3、把MySQL数据库中的表数据导入到hbase

 普通导

sqoop import \--connect jdbc:mysql://hadoop1:3306/mysql \

--username root \

--password root \

--table help_keyword \

--hbase-table new_help_keyword \

--column-family person \

--hbase-row-key help_keyword_id

 

此时会报错，因为需要先创建Hbase里面的表，再执行导入的语句

hbase(main):001:0> create 'new_help_keyword', 'base_info'

0 row(s) in 3.6280 seconds

=> Hbase::Table - new_help_keyword

hbase(main):002:0>