一、sqoop安装
安装sqoop的前提是已经具备java和hadoop的环境
1、下载并解压
最新版下载地址http://ftp.wayne.edu/apache/sqoop/1.4.6/
2、修改配置文件
$ cd sqoop/conf（默认将sqoop解压到当前目录下） $ mv sqoop-env-template.sh sqoop-env.sh
打开sqoop-env.sh并编辑下面几行：
export HADOOP_COMMON_HOME=/home/hadoop/apps/hadoop-2.6.4/（可用which （hadoop）命令查看位置） export HADOOP_MAPRED_HOME=/home/hadoop/apps/hadoop-2.6.4/ export HIVE_HOME=/home/hadoop/apps/hive
3、加入mysql的jdbc驱动包
cp ~/apps/hive/lib/mysql-connector-java-5.1.28.jar /sqoop/lib/（在之前的hive安装中已经导入过mysql驱动包）
若无，则可以将驱动包上传至虚拟机，拷入至sqoop/lib中
4、验证启动
$ cd sqoop/bin
$ sqoop-version
预期的输出：
15/12/17 14:52:32 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
Sqoop 1.4.6 git commit id 5b34accaca7de251fc91161733f906af2eddbe83
Compiled by abe on Fri Aug 1 11:19:26 PDT 2015
到这里，整个Sqoop安装工作完成。
5、使用
cd sqoop/
使用bin/sqoop help 可查看其中操作指令
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
举例：将mysql数据库中某表数据导入HDFS中，由于前边的hadoop配置，最终数据会导向HDFS中的/usr/hadoop/文件夹中：
$bin/sqoop import --connect jdbc:mysql://min1:3306/mysql 数据库链接信息 --username root --password 123456 --table db 选择导出哪张表数据 --m 1
系统会运行mapreduce程序，在min1:8088以及min1:50070中可看到运行过程，如果成功执行，那么会得到下面的输出。
9/03/18 16:53:53 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1552575186473_0030 19/03/18 16:53:54 INFO impl.YarnClientImpl: Submitted application application_1552575186473_0030 19/03/18 16:53:54 INFO mapreduce.Job: The url to track the job: http://min1:8088/proxy/application_1552575186473_0030/ 19/03/18 16:53:54 INFO mapreduce.Job: Running job: job_1552575186473_0030 19/03/18 16:54:11 INFO mapreduce.Job: Job job_1552575186473_0030 running in uber mode : false 19/03/18 16:54:11 INFO mapreduce.Job: map 0% reduce 0% 19/03/18 16:54:32 INFO mapreduce.Job: map 100% reduce 0% 19/03/18 16:54:33 INFO mapreduce.Job: Job job_1552575186473_0030 completed successfully 19/03/18 16:54:33 INFO mapreduce.Job: Counters: 30 File System Counters FILE: Number of bytes read=0 FILE: Number of bytes written=124571 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=95 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)=16705 Total time spent by all reduces in occupied slots (ms)=0 Total time spent by all map tasks (ms)=16705 Total vcore-milliseconds taken by all map tasks=16705 Total megabyte-milliseconds taken by all map tasks=17105920 Map-Reduce Framework Map input records=2 Map output records=2 Input split bytes=87 Spilled Records=0 Failed Shuffles=0 Merged Map outputs=0 GC time elapsed (ms)=110 CPU time spent (ms)=2980 Physical memory (bytes) snapshot=103735296 Virtual memory (bytes) snapshot=2064986112 Total committed heap usage (bytes)=30474240 File Input Format Counters Bytes Read=0 File Output Format Counters Bytes Written=95 19/03/18 16:54:33 INFO mapreduce.ImportJobBase: Transferred 95 bytes in 52.9843 seconds (1.793 bytes/sec) 19/03/18 16:54:33 INFO mapreduce.ImportJobBase: Retrieved 2 records.