欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

Sqoop安装过程详解

程序员文章站 2022-05-25 14:29:43
...

Sqoop是一个用来将Hadoop和关系型数据库中的数据相互转移的工具,可以将一个关系型数据库(例如 : MySQL ,Oracle ,Postgres等)中的数据导进到Hadoop的HDFS中,也可以将HDFS的数据导进到关系型数据库中。

 

Sqoop官方版本:http://apache.dataguru.cn/sqoop/1.4.2/
Sqoop CDH版本:http://archive.cloudera.com/cdh/3/sqoop-1.2.0-CDH3B4.tar.gz
Hadoop CDH版本:http://archive.cloudera.com/cdh/3/hadoop-0.20.2-CDH3B4.tar.gz

 

之前已经安装Hadoop-0.20.2,因sqoop官方版本不支持此版本,但可使用CDH3版本,如上面的下载链接。为了测试方便,可以通过拷贝相应的包到sqoop-1.2.0-CDH3B4/lib下,依然可以使用Hadoop-0.20.2版本。

 

sqoop版本: sqoop-1.2.0-CDH3B4

 

Hadoop版本:0.20.2

 

mysql版本:  5.6.11 

 

 

 

1)解压缩sqoop安装文件

 

 
[hadoop@node01 ~]$ tar -xzvf sqoop-1.2.0-CDH3B4.tar.gz
 
2)sqoop-1.2.0-CDH3B4依赖hadoop-core-0.20.2-CDH3B4.jar,所以你需要下载hadoop- 0.20.2-CDH3B4.tar.gz,解压缩后将hadoop-0.20.2-CDH3B4/hadoop-core-0.20.2- CDH3B4.jar复制到sqoop-1.2.0-CDH3B4/lib中。

 

 

 

[hadoop@node01 ~]$ cp hadoop-core-0.20.2-CDH3B4.jar sqoop-1.2.0-CDH3B4/lib
[hadoop@node01 ~]$ ls -l sqoop-1.2.0-CDH3B4/lib/hadoop-core-0.20.2-CDH3B4.jar
-rw-r--r--. 1 hadoop root 3452461 May  9 05:40 sqoop-1.2.0-CDH3B4/lib/hadoop-core-0.20.2-CDH3B4.jar
 
3)另外,sqoop导入mysql数据运行过程中依赖mysql-connector-java-*.jar,所以你需要下载mysql-connector-java-*.jar并复制到sqoop-1.2.0-CDH3B4/lib中
 
[hadoop@node01 ~]$ cp mysql-connector-java-5.1.24-bin.jar sqoop-1.2.0-CDH3B4/lib
[hadoop@node01 ~]$ ls -l sqoop-1.2.0-CDH3B4/lib/mysql-connector-java-5.1.24-bin.jar
-rw-r--r--. 1 hadoop root 846263 May  9 05:43 sqoop-1.2.0-CDH3B4/lib/mysql-connector-java-5.1.24-bin.jar
 
4)修改SQOOP的文件configure-sqoop,注释掉hbase和zookeeper检查(除非你准备使用HABASE等HADOOP上的组件),否则在进行hbase和zookeeper检查时,可能会卡在这里。
 
[hadoop@node01 bin]$ pwd
/home/hadoop/sqoop-1.2.0-CDH3B4/bin
[hadoop@node01 bin]$ vi configure-sqoop
 
#if [ -z "${HBASE_HOME}" ]; then
#  HBASE_HOME=/usr/lib/hbase
#fi
#if [ -z "${ZOOKEEPER_HOME}" ]; then
#  ZOOKEEPER_HOME=/usr/lib/zookeeper
#fi
 
#if [ ! -d "${HBASE_HOME}" ]; then
#  echo "Error: $HBASE_HOME does not exist!"
#  echo 'Please set $HBASE_HOME to the root of your HBase installation.'
#  exit 1
#fi
#if [ ! -d "${ZOOKEEPER_HOME}" ]; then
#  echo "Error: $ZOOKEEPER_HOME does not exist!"
#  echo 'Please set $ZOOKEEPER_HOME to the root of your ZooKeeper installation.'
#  exit 1
#fi
 
5)启动Hadoop
[hadoop@node01 bin]$ start-all.sh
[hadoop@node01 bin]$ jps
2732 Jps
2478 NameNode
2665 JobTracker
2600 SecondaryNameNode
 
6)从MySQL导入数据到HDFS
 
(1)在MySQL里创建测试数据库sqooptest
[hadoop@node01 ~]$ mysql -u root -p
mysql> create database sqooptest;
Query OK, 1 row affected (0.01 sec)
 
(2)创建sqoop专有用户
mysql> create user 'sqoop' identified by 'sqoop';
Query OK, 0 rows affected (0.00 sec)
 
mysql> grant all privileges on *.* to 'sqoop' with grant option;
Query OK, 0 rows affected (0.00 sec)
 
mysql> flush privileges;
Query OK, 0 rows affected (0.00 sec)
 
(3)生成测试数据
mysql> use sqooptest;
Database changed
mysql> create table tb1 as select table_schema,table_name,table_type from information_schema.TABLES;
Query OK, 154 rows affected (0.28 sec)
Records: 154  Duplicates: 0  Warnings: 0
 
(4)测试sqoop与mysql的连接
[hadoop@node01 ~]$ sqoop list-databases --connect jdbc:mysql://node01:3306/ --username sqoop --password sqoop
13/05/09 06:15:01 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
13/05/09 06:15:01 INFO manager.MySQLManager: Executing SQL statement: SHOW DATABASES
information_schema
hive
mysql
performance_schema
sqooptest
test
 
(5)从MySQL导入数据到HDFS
[hadoop@node01 ~]$ sqoop import --connect jdbc:mysql://node01:3306/sqooptest --username sqoop --password sqoop --table tb1 -m 1
13/05/09 06:16:39 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
13/05/09 06:16:39 INFO tool.CodeGenTool: Beginning code generation
13/05/09 06:16:39 INFO manager.MySQLManager: Executing SQL statement: SELECT t.* FROM `tb1` AS t LIMIT 1
13/05/09 06:16:39 INFO manager.MySQLManager: Executing SQL statement: SELECT t.* FROM `tb1` AS t LIMIT 1
13/05/09 06:16:39 INFO orm.CompilationManager: HADOOP_HOME is /home/hadoop/hadoop-0.20.2/bin/..
13/05/09 06:16:39 INFO orm.CompilationManager: Found hadoop core jar at: /home/hadoop/hadoop-0.20.2/bin/../hadoop-0.20.2-core.jar
13/05/09 06:16:42 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/4175ce59fd53eb3de75875cfd3bd450b/tb1.jar
13/05/09 06:16:42 WARN manager.MySQLManager: It looks like you are importing from mysql.
13/05/09 06:16:42 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
13/05/09 06:16:42 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
13/05/09 06:16:42 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
13/05/09 06:16:42 INFO mapreduce.ImportJobBase: Beginning import of tb1
13/05/09 06:16:43 INFO manager.MySQLManager: Executing SQL statement: SELECT t.* FROM `tb1` AS t LIMIT 1
13/05/09 06:16:45 INFO mapred.JobClient: Running job: job_201305090600_0001
13/05/09 06:16:46 INFO mapred.JobClient:  map 0% reduce 0%
13/05/09 06:17:01 INFO mapred.JobClient:  map 100% reduce 0%
13/05/09 06:17:03 INFO mapred.JobClient: Job complete: job_201305090600_0001
13/05/09 06:17:03 INFO mapred.JobClient: Counters: 5
13/05/09 06:17:03 INFO mapred.JobClient:   Job Counters
13/05/09 06:17:03 INFO mapred.JobClient:     Launched map tasks=1
13/05/09 06:17:03 INFO mapred.JobClient:   FileSystemCounters
13/05/09 06:17:03 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=7072
13/05/09 06:17:03 INFO mapred.JobClient:   Map-Reduce Framework
13/05/09 06:17:03 INFO mapred.JobClient:     Map input records=154
13/05/09 06:17:03 INFO mapred.JobClient:     Spilled Records=0
13/05/09 06:17:03 INFO mapred.JobClient:     Map output records=154
13/05/09 06:17:03 INFO mapreduce.ImportJobBase: Transferred 6.9062 KB in 19.9871 seconds (353.8277 bytes/sec)
13/05/09 06:17:03 INFO mapreduce.ImportJobBase: Retrieved 154 records.
 
(6)在HDFS上查看刚刚导入的数据
[hadoop@node01 ~]$ hadoop dfs -ls tb1
Found 2 items
drwxr-xr-x   - hadoop supergroup          0 2013-05-09 06:16 /user/hadoop/tb1/_logs
-rw-r--r--   2 hadoop supergroup       7072 2013-05-09 06:16 /user/hadoop/tb1/part-m-00000



原文链接:http://blog.csdn.net/u010415792/article/details/8907650

相关标签: sqoop big data