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Sqoop的安装与使用

[日期:2014-10-23] 来源:Linux社区  作者:sunflower_cao [字体: ]

5.3 添加需要的jar包到lib下面

这里的jar包指的是连接关系型数据库的jar 比如mysql Oracle  这些jar包是需要自己添加到lib目录下面去的

cp  ~/hive/lib/mysql-connector-java-5.1.30.jar  ~/sqoop/lib/

5.4 添加环境变量

vi ~/.profile

添加如下内容

export SQOOP_HOME=/home/Hadoop/sqoop

export SBT_HOME=/home/hadoop/sbt


export PATH=$PATH:$SBT_HOME/bin:$SQOOP_HOME/bin
export CLASSPATH=$CLASSPATH:$SQOOP_HOME/lib

source ~/.profile使配置文件生效

5.5 测试mysql数据库的连接使用

①连接mysql数据库,列出所有的数据库

hadoop@linuxidc:~/sqoop/conf$ sqoop list-databases --connect jdbc:mysql://127.0.0.1:3306/ --username root -P
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:15:15 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password:
14/10/21 18:15:19 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
information_schema
XINGXUNTONG
XINGXUNTONG_HIVE
amon
hive
hmon
mahout
mysql
oozie
performance_schema
realworld
rman
scm
smon

-P表示输入密码 可以直接使用--password来制定密码

②mysql数据库的表导入到HDFS

hadoop@linuxidc:~/sqoop/conf$ sqoop import -m 1  --connect jdbc:mysql://127.0.0.1:3306/realworld --username root -P --table weblogs --target-dir /user/sqoop/test1
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:19:18 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password:
14/10/21 18:19:21 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
14/10/21 18:19:21 INFO tool.CodeGenTool: Beginning code generation
14/10/21 18:19:22 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:19:22 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:19:22 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/hadoop
Note: /tmp/sqoop-hadoop/compile/15cb67e2b315154cdf02e3a17cf32bbe/weblogs.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
14/10/21 18:19:23 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/15cb67e2b315154cdf02e3a17cf32bbe/weblogs.jar
14/10/21 18:19:23 WARN manager.MySQLManager: It looks like you are importing from mysql.
14/10/21 18:19:23 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
14/10/21 18:19:23 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
14/10/21 18:19:23 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
14/10/21 18:19:23 INFO mapreduce.ImportJobBase: Beginning import of weblogs
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/hadoop-2.3.0-cdh5.1.2/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/hbase-0.98.1-cdh5.1.2/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]
14/10/21 18:19:24 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/10/21 18:19:24 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/10/21 18:19:25 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/10/21 18:19:25 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/10/21 18:19:40 INFO db.DBInputFormat: Using read commited transaction isolation
14/10/21 18:19:41 INFO mapreduce.JobSubmitter: number of splits:1
14/10/21 18:19:42 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1413879907572_0002
14/10/21 18:19:46 INFO impl.YarnClientImpl: Submitted application application_1413879907572_0002
14/10/21 18:19:46 INFO mapreduce.Job: The url to track the job: N/A
14/10/21 18:19:46 INFO mapreduce.Job: Running job: job_1413879907572_0002
14/10/21 18:20:12 INFO mapreduce.Job: Job job_1413879907572_0002 running in uber mode : false
14/10/21 18:20:12 INFO mapreduce.Job:  map 0% reduce 0%
14/10/21 18:20:41 INFO mapreduce.Job:  map 100% reduce 0%
14/10/21 18:20:45 INFO mapreduce.Job: Job job_1413879907572_0002 completed successfully
14/10/21 18:20:46 INFO mapreduce.Job: Counters: 30
 File System Counters
  FILE: Number of bytes read=0
  FILE: Number of bytes written=107189
  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=251130
  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)=22668
  Total time spent by all reduces in occupied slots (ms)=0
  Total time spent by all map tasks (ms)=22668
  Total vcore-seconds taken by all map tasks=22668
  Total megabyte-seconds taken by all map tasks=23212032
 Map-Reduce Framework
  Map input records=3000
  Map output records=3000
  Input split bytes=87
  Spilled Records=0
  Failed Shuffles=0
  Merged Map outputs=0
  GC time elapsed (ms)=41
  CPU time spent (ms)=1540
  Physical memory (bytes) snapshot=133345280
  Virtual memory (bytes) snapshot=1201442816
  Total committed heap usage (bytes)=76021760
 File Input Format Counters
  Bytes Read=0
 File Output Format Counters
  Bytes Written=251130
14/10/21 18:20:46 INFO mapreduce.ImportJobBase: Transferred 245.2441 KB in 80.7974 seconds (3.0353 KB/sec)
14/10/21 18:20:46 INFO mapreduce.ImportJobBase: Retrieved 3000 records.

-m 表示启动几个map任务来读取数据  如果数据库中的表没有主键这个参数是必须设置的而且只能设定为1  否则会提示

14/10/21 18:18:27 ERROR tool.ImportTool: Error during import: No primary key could be found for table weblogs. Please specify one with --split-by or perform a sequential import with '-m 1'.

而这个参数设置为几会直接决定导入的文件在hdfs上面是分成几块的 比如 设置为1 则会产生一个数据文件

14/10/21 18:23:54 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 2 items
-rw-r--r--  1 hadoop supergroup          0 2014-10-21 18:20 /user/sqoop/test1/_SUCCESS
-rw-r--r--  1 hadoop supergroup    251130 2014-10-21 18:20 /user/sqoop/test1/part-m-00000

这里添加主键:

mysql> desc weblogs;
+--------------+-------------+------+-----+---------+-------+
| Field        | Type        | Null | Key | Default | Extra |
+--------------+-------------+------+-----+---------+-------+
| md5          | varchar(32) | YES  |    | NULL    |      |
| url          | varchar(64) | YES  |    | NULL    |      |
| request_date | date        | YES  |    | NULL    |      |
| request_time | time        | YES  |    | NULL    |      |
| ip          | varchar(15) | YES  |    | NULL    |      |
+--------------+-------------+------+-----+---------+-------+
5 rows in set (0.00 sec)


mysql> alter table weblogs add primary key(md5,ip);
Query OK, 3000 rows affected (1.60 sec)
Records: 3000  Duplicates: 0  Warnings: 0

mysql> desc weblogs;
+--------------+-------------+------+-----+---------+-------+
| Field        | Type        | Null | Key | Default | Extra |
+--------------+-------------+------+-----+---------+-------+
| md5          | varchar(32) | NO  | PRI |        |      |
| url          | varchar(64) | YES  |    | NULL    |      |
| request_date | date        | YES  |    | NULL    |      |
| request_time | time        | YES  |    | NULL    |      |
| ip          | varchar(15) | NO  | PRI |        |      |
+--------------+-------------+------+-----+---------+-------+
5 rows in set (0.02 sec)

然后指定-m

hadoop@linuxidc:~/sqoop/conf$ sqoop import -m 2  --connect jdbc:mysql://127.0.0.1:3306/realworld --username root -P --table weblogs --target-dir /user/sqoop/test2
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:22:40 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password:
14/10/21 18:24:04 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
14/10/21 18:24:04 INFO tool.CodeGenTool: Beginning code generation
14/10/21 18:24:04 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:24:04 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:24:04 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/hadoop
Note: /tmp/sqoop-hadoop/compile/7061f445f29510afa2b89729126a57b9/weblogs.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
14/10/21 18:24:07 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/7061f445f29510afa2b89729126a57b9/weblogs.jar
14/10/21 18:24:07 WARN manager.MySQLManager: It looks like you are importing from mysql.
14/10/21 18:24:07 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
14/10/21 18:24:07 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
14/10/21 18:24:07 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
14/10/21 18:24:07 ERROR tool.ImportTool: Error during import: No primary key could be found for table weblogs. Please specify one with --split-by or perform a sequential import with '-m 1'.
hadoop@linuxidc:~/sqoop/conf$ sqoop import -m 2  --connect jdbc:mysql://127.0.0.1:3306/realworld --username root -P --table weblogs --target-dir /user/sqoop/test2
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:30:04 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password:
14/10/21 18:30:07 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
14/10/21 18:30:07 INFO tool.CodeGenTool: Beginning code generation
14/10/21 18:30:07 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:30:07 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:30:07 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/hadoop
Note: /tmp/sqoop-hadoop/compile/6dbf2401c1a51b81c5b885e6f7d43137/weblogs.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
14/10/21 18:30:09 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/6dbf2401c1a51b81c5b885e6f7d43137/weblogs.jar
14/10/21 18:30:09 WARN manager.MySQLManager: It looks like you are importing from mysql.
14/10/21 18:30:09 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
14/10/21 18:30:09 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
14/10/21 18:30:09 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
14/10/21 18:30:09 WARN manager.CatalogQueryManager: The table weblogs contains a multi-column primary key. Sqoop will default to the column md5 only for this job.
14/10/21 18:30:09 WARN manager.CatalogQueryManager: The table weblogs contains a multi-column primary key. Sqoop will default to the column md5 only for this job.
14/10/21 18:30:09 INFO mapreduce.ImportJobBase: Beginning import of weblogs
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/hadoop-2.3.0-cdh5.1.2/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/hbase-0.98.1-cdh5.1.2/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]
14/10/21 18:30:09 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/10/21 18:30:09 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/10/21 18:30:10 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/10/21 18:30:10 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/10/21 18:30:17 INFO db.DBInputFormat: Using read commited transaction isolation
14/10/21 18:30:17 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`md5`), MAX(`md5`) FROM `weblogs`
14/10/21 18:30:17 WARN db.TextSplitter: Generating splits for a textual index column.
14/10/21 18:30:17 WARN db.TextSplitter: If your database sorts in a case-insensitive order, this may result in a partial import or duplicate records.
14/10/21 18:30:17 WARN db.TextSplitter: You are strongly encouraged to choose an integral split column.
14/10/21 18:30:18 INFO mapreduce.JobSubmitter: number of splits:4
14/10/21 18:30:18 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1413879907572_0003
14/10/21 18:30:19 INFO impl.YarnClientImpl: Submitted application application_1413879907572_0003
14/10/21 18:30:19 INFO mapreduce.Job: The url to track the job: N/A
14/10/21 18:30:19 INFO mapreduce.Job: Running job: job_1413879907572_0003
14/10/21 18:30:32 INFO mapreduce.Job: Job job_1413879907572_0003 running in uber mode : false
14/10/21 18:30:32 INFO mapreduce.Job:  map 0% reduce 0%
14/10/21 18:31:12 INFO mapreduce.Job:  map 50% reduce 0%
14/10/21 18:31:13 INFO mapreduce.Job:  map 75% reduce 0%
14/10/21 18:31:15 INFO mapreduce.Job:  map 100% reduce 0%
14/10/21 18:31:21 INFO mapreduce.Job: Job job_1413879907572_0003 completed successfully
14/10/21 18:31:22 INFO mapreduce.Job: Counters: 30
 File System Counters
  FILE: Number of bytes read=0
  FILE: Number of bytes written=429312
  FILE: Number of read operations=0
  FILE: Number of large read operations=0
  FILE: Number of write operations=0
  HDFS: Number of bytes read=532
  HDFS: Number of bytes written=251209
  HDFS: Number of read operations=16
  HDFS: Number of large read operations=0
  HDFS: Number of write operations=8
 Job Counters
  Launched map tasks=4
  Other local map tasks=4
  Total time spent by all maps in occupied slots (ms)=160326
  Total time spent by all reduces in occupied slots (ms)=0
  Total time spent by all map tasks (ms)=160326
  Total vcore-seconds taken by all map tasks=160326
  Total megabyte-seconds taken by all map tasks=164173824
 Map-Reduce Framework
  Map input records=3001
  Map output records=3001
  Input split bytes=532
  Spilled Records=0
  Failed Shuffles=0
  Merged Map outputs=0
  GC time elapsed (ms)=806
  CPU time spent (ms)=5450
  Physical memory (bytes) snapshot=494583808
  Virtual memory (bytes) snapshot=4805771264
  Total committed heap usage (bytes)=325058560
 File Input Format Counters
  Bytes Read=0
 File Output Format Counters
  Bytes Written=251209
14/10/21 18:31:22 INFO mapreduce.ImportJobBase: Transferred 245.3213 KB in 72.5455 seconds (3.3816 KB/sec)

这里产生的文件跟主键的字段个数以及-m的参数是相关的 大致是-m的值乘以主键字段数,有待考证

hadoop@linuxidc:~/study/cdh5$ hadoop fs -ls /user/sqoop/test2/
14/10/21 18:32:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 5 items
-rw-r--r--  1 hadoop supergroup          0 2014-10-21 18:31 /user/sqoop/test2/_SUCCESS
-rw-r--r--  1 hadoop supergroup          0 2014-10-21 18:31 /user/sqoop/test2/part-m-00000
-rw-r--r--  1 hadoop supergroup    251130 2014-10-21 18:31 /user/sqoop/test2/part-m-00001
-rw-r--r--  1 hadoop supergroup          0 2014-10-21 18:31 /user/sqoop/test2/part-m-00002
-rw-r--r--  1 hadoop supergroup        79 2014-10-21 18:31 /user/sqoop/test2/part-m-00003

这里的主键设计的不合理导致数据分布不均匀~~  有待改进


③数据导出Oracle和HBase

使用export可将hdfs中数据导入到远程数据库中
          export --connect jdbc:oracle:thin:@192.168.**.**:**:**--username **--password=** -m1table VEHICLE--export-dir /user/root/VEHICLE

向Hbase导入数据
          sqoop import --connect jdbc:oracle:thin:@192.168.**.**:**:**--username**--password=**--m 1 --table VEHICLE --hbase-create-table --hbase-table VEHICLE--hbase-row-key ID --column-family VEHICLEINFO --split-by ID

5.6 测试Mysql数据库的使用

前提:导入mysql jdbc的jar包

①测试数据库连接
sqoop list-databases –connect jdbc:mysql://192.168.10.63 –username root–password 123456
②Sqoop的使用
以下所有的命令每行之后都存在一个空格,不要忘记
(以下6中命令都没有进行过成功测试)

<1>mysql–>hdfs
sqoop export –connect
jdbc:mysql://192.168.10.63/ipj
–username root
–password 123456
–table ipj_flow_user
–export-dir hdfs://192.168.10.63:8020/user/flow/part-m-00000
前提:
(1)hdfs中目录/user/flow/part-m-00000必须存在
(2)如果集群设置了压缩方式lzo,那么本机必须得安装且配置成功lzo
(3)hadoop集群中每个节点都要有对mysql的操作权限

<2>hdfs–>mysql
sqoop import –connect
jdbc:mysql://192.168.10.63/ipj
–table ipj_flow_user

<3>mysql–>hbase
sqoop  import  –connect
jdbc:mysql://192.168.10.63/ipj
–table ipj_flow_user
–hbase-table ipj_statics_test
–hbase-create-table
–hbase-row-key id
–column-family imei

<4>hbase–>mysql
关于将Hbase的数据导入到mysql里,Sqoop并不是直接支持的,一般采用如下3种方法:
第一种:将Hbase数据扁平化成HDFS文件,然后再由Sqoop导入.
第二种:将Hbase数据导入Hive表中,然后再导入mysql。
第三种:直接使用Hbase的Java API读取表数据,直接向mysql导入
不需要使用Sqoop。

<5>mysql–>hive
sqoop import –connect
jdbc:mysql://192.168.10.63/ipj
–table hive_table_test
–hive-import
–hive-table hive_test_table 或–create-hive-table hive_test_table

<6>hive–>mysql
sqoop export –connect
jdbc:mysql://192.168.10.63/ipj
–username hive
–password 123456
–table target_table
–export-dir /user/hive/warehouse/uv/dt=mytable
前提:mysql中表必须存在


③Sqoop其他操作
<1>列出mysql中的所有数据库
sqoop list-databases –connect jdbc:mysql://192.168.10.63:3306/ –usernameroot –password 123456
<2>列出mysql中某个库下所有表
sqoop list-tables –connect jdbc:mysql://192.168.10.63:3306/ipj –usernameroot –password 123456

6 Sqoop1的性能

 测试数据:

表名:tb_keywords
行数:11628209
数据文件大小:1.4G
测试结果:

 

HDFS--->DB

HDFS<---DB

Sqoop

428s

166s

HDFS<->FILE<->DB

209s

105s

从结果上来看,以FILE作为中转方式性能是要高于SQOOP的,原因如下:

本质上SQOOP使用的是JDBC,效率不会比MYSQL自带的导入\导出工具效率高以导入数据到DB为例,SQOOP的设计思想是分阶段提交,也就是说假设一个表有1K行,那么它会先读出100行(默认值),然后插入,提交,再读取100行……如此往复
即便如此,SQOOP也是有优势的,比如说使用的便利性,任务执行的容错性等。在一些测试环境中如果需要的话可以考虑把它拿来作为一个工具使用。

Sqoop 的详细介绍请点这里
Sqoop 的下载地址请点这里

本文永久更新链接地址http://www.linuxidc.com/Linux/2014-10/108337.htm

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