首页 > 代码库 > [hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 数据在mysq和hdfs之间的相互转换

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 数据在mysq和hdfs之间的相互转换

2024-09-02 06:43:10   220人阅读

 

P573 从mysql导入数据到hdfs

 

第一步：在mysql中创建待导入的数据

 

1、创建数据库并允许所有用户访问该数据库

  mysql -h 192.168.200.250 -u root -p

CREATE DATABASE sqoop;

GRANT ALL PRIVILEGES ON *.* TO ‘root‘@‘%‘;
或 GRANT SELECT, INSERT, DELETE,UPDATE ON *.* TO ‘root‘@‘%‘;
FLUSH PRIVILEGES;
查看权限：select user,host,select_priv,insert_priv,update_priv,delete_priv from mysql.user;
 
2、创建表widgets
CREATE TABLE widgets(id INT NOT NULL PRIMARY KEY AUTO_INCREMENT,widget_name VARCHAR(64) NOT NULL,price DECIMAL(10,2),design_date DATE,version INT,design_comment VARCHAR(100));
 
3、导入测试数据
INSERT INTO widgets VALUES(NULL,‘sprocket‘,0.25,‘2010-01-10‘,1,‘connect two gizmos‘);INSERT INTO widgets VALUES(NULL,‘gizmo‘,4.00,‘2009-01-30‘,4,NULL);INSERT INTO widgets VALUES(NULL,‘gadget‘,99.99,‘1983-08-13‘,13,‘our flagship product‘);
 
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第二步：执行sqoop导入命令
sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --table widgets -m 1
 缺少mysql连接器
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先导入mysql的连接器包
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再来执行
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发现怎么也连接不上远程mysql数据库，需要授权如下：
GRANT ALL ON *.* TO ‘‘@‘192.168.200.123‘;
grant all privileges on *.* to ""@"192.168.200.123" identified by "密码";
FLUSH PRIVILEGES;
select user,host,select_priv,insert_priv,update_priv,delete_priv from mysql.user;
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再来执行一下
还是不行的话，就只能是在sqoop命令中通过--username 和--password来显式的指定用户名和密码连接了
sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --table widgets -m 1 -username root -password mysql密码
 
在yarn管理台查看到这个任务正在运行（RUNNING）http://hadoop-allinone-200-123.wdcloud.locl:8088/cluster
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但是最终还是执行失败
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失败原因：物理内存使用了156.8远小于分配的1GB，但是虚拟内存使用2.7超过了默认配置的2.1GB，解决方法：
在etc/hadoop/yarn-site.xml文件中，修改检查虚拟内存的属性为false，如下：
<property>      <name>yarn.nodemanager.vmem-check-enabled</name>      <value>false</value>  </property>  
 运行继续报错：
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解决方法：这个目录没有权限
http://www.oschina.net/question/2288283_2134188?sort=time
保证使用hadoop用户启动集群（因为hadoop的集群的用户是hadoop），并为这个文件夹授权755
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再来执行，姐们儿就不信了 。。。哒哒哒。。。终于成功了
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后台日志：
[hadoop@hadoop-allinone-200-123 sqoop-1.4.6]$ sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --tabgets -m 1 -username root -password weidongWarning: /wdcloud/app/sqoop-1.4.6/../hbase does not exist! HBase imports will fail.Please set $HBASE_HOME to the root of your HBase installation.Warning: /wdcloud/app/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.Please set $HCAT_HOME to the root of your HCatalog installation.Warning: /wdcloud/app/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.Please set $ACCUMULO_HOME to the root of your Accumulo installation.Warning: /wdcloud/app/sqoop-1.4.6/../zookeeper does not exist! Accumulo imports will fail.Please set $ZOOKEEPER_HOME to the root of your Zookeeper installation.17/01/23 23:59:17 INFO sqoop.Sqoop: Running Sqoop version: 1.4.617/01/23 23:59:17 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider us instead.17/01/23 23:59:18 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.17/01/23 23:59:18 INFO tool.CodeGenTool: Beginning code generation17/01/23 23:59:18 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets` AS t LIMIT 117/01/23 23:59:18 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets` AS t LIMIT 117/01/23 23:59:18 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /wdcloud/app/hadoop-2.7.3Note: /tmp/sqoop-hadoop/compile/591fd797fbbe57ce38b4492a1c9a0300/widgets.java uses or overrides a deprecated Note: Recompile with -Xlint:deprecation for details.17/01/23 23:59:21 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/591fd797fbbe57ce381c9a0300/widgets.jar17/01/23 23:59:21 WARN manager.MySQLManager: It looks like you are importing from mysql.17/01/23 23:59:21 WARN manager.MySQLManager: This transfer can be faster! Use the --direct17/01/23 23:59:21 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.17/01/23 23:59:21 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)17/01/23 23:59:21 INFO mapreduce.ImportJobBase: Beginning import of widgets17/01/23 23:59:21 INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.joer.address17/01/23 23:59:22 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar17/01/23 23:59:23 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.17/01/23 23:59:24 INFO client.RMProxy: Connecting to ResourceManager at hadoop-allinone-200-123.wdcloud.locl/8.200.123:803217/01/23 23:59:30 INFO db.DBInputFormat: Using read commited transaction isolation17/01/23 23:59:30 INFO mapreduce.JobSubmitter: number of splits:117/01/23 23:59:31 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1485230213604_000117/01/23 23:59:32 INFO impl.YarnClientImpl: Submitted application application_1485230213604_000117/01/23 23:59:32 INFO mapreduce.Job: The url to track the job: http://hadoop-allinone-200-123.wdcloud.locl:80213604_0001/17/01/23 23:59:32 INFO mapreduce.Job: Running job: job_1485230213604_000117/01/23 23:59:50 INFO mapreduce.Job: Job job_1485230213604_0001 running in uber mode : false17/01/23 23:59:50 INFO mapreduce.Job:  map 0% reduce 0%17/01/24 00:00:00 INFO mapreduce.Job:  map 100% reduce 0%17/01/24 00:00:01 INFO mapreduce.Job: Job job_1485230213604_0001 completed successfully17/01/24 00:00:02 INFO mapreduce.Job: Counters: 30    File System Counters        FILE: Number of bytes read=0        FILE: Number of bytes written=138186        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=129        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)=7933        Total time spent by all reduces in occupied slots (ms)=0        Total time spent by all map tasks (ms)=7933        Total vcore-milliseconds taken by all map tasks=7933        Total megabyte-milliseconds taken by all map tasks=8123392    Map-Reduce Framework        Map input records=3        Map output records=3        Input split bytes=87        Spilled Records=0        Failed Shuffles=0        Merged Map outputs=0        GC time elapsed (ms)=59        CPU time spent (ms)=2210        Physical memory (bytes) snapshot=190287872        Virtual memory (bytes) snapshot=2924978176        Total committed heap usage (bytes)=220725248    File Input Format Counters         Bytes Read=0    File Output Format Counters         Bytes Written=12917/01/24 00:00:02 INFO mapreduce.ImportJobBase: Transferred 129 bytes in 38.2028 seconds (3.3767 bytes/sec)17/01/24 00:00:02 INFO mapreduce.ImportJobBase: Retrieved 3 records.
 
查看作业历史服务器以了解MR任务执行详情，发现查看不到，原因是因为没有启动作业历史服务器
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启动之：
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再来查看下，就可以看到作业历史记录了
http://hadoop-allinone-200-123.wdcloud.locl:19888/jobhistory/job/job_1485230213604_0001
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可以看到，sqoop导入数据到hdfs只有map任务而没有reduce任务，map任务数目为1，执行完成数目为1，成功数目为1 ，点击Map链接，查看详细
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现在，看看是否真的已经导入了这个数据表
 
第三步：验证导入结果
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可以看到 widgets 表的数据已经导入到了HDFS中
除了导入数据到HDFS中，sqoop在导入时还生成导入源代码.java .jar和.class文件
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如果只想生成代码而不导入数据，执行以下命令：
sqoop codegen --connect uri --table 表 --class-name 生成的类名称
 
第四步：追加数据
--direct：能更快速的从表中读取数据，需要数据库支持，如mysql使用外部工具mysqldump
--append：使用追加数据模式来导入数据

现在，我们在mysql中新插入了一条数据
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来执行追加命令
sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --table widgets -m 1 -username root -password weidong --direct --append
 执行成功
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查看下HDFS中的数据
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可以看到，已经追加成功
 
 
第五步：将HDFS中的数据导出到mysql
复制表widgets为widgets_copy并清空widgets_copy表数据
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执行导出命令 
当将密码写在命令行，会为安全造成影响，这时，可以使用参数-P取代 --password
在任务执行时动态的输入密码
Setting your password on the command-line is insecure. Consider using -P instead.
所以命令如下：
 sqoop export 
--connect jdbc:mysql://192.168.200.250/sqoop 
-m 1 
--table widgets_copy 
--export-dir widgets/part-m-00002  
--username root 
-P
Enter password:不会回显字符
 
成功执行日志信息
[hadoop@hadoop-allinone-200-123 /]$ sqoop export --connect jdbc:mysql://192.168.200.250/sqoop -m 1 --table widgets_copy --export-dir widgets/part-m-00002  --username root -P17/01/24 01:04:19 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6Enter password: 17/01/24 01:04:22 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.17/01/24 01:04:22 INFO tool.CodeGenTool: Beginning code generation17/01/24 01:04:23 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT 117/01/24 01:04:23 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT 117/01/24 01:04:23 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /wdcloud/app/hadoop-2.7.3Note: /tmp/sqoop-hadoop/compile/c66df558e872801e493fbc78458e6914/widgets_copy.java uses or overrides a deprecated API.Note: Recompile with -Xlint:deprecation for details.17/01/24 01:04:26 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/c66df558e872801e493fbc78458e6914/widgets_copy.jar17/01/24 01:04:26 INFO mapreduce.ExportJobBase: Beginning export of widgets_copy17/01/24 01:04:26 INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address17/01/24 01:04:26 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar17/01/24 01:04:28 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative17/01/24 01:04:28 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative17/01/24 01:04:28 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps17/01/24 01:04:28 INFO client.RMProxy: Connecting to ResourceManager at hadoop-allinone-200-123.wdcloud.locl/192.168.200.123:803217/01/24 01:04:30 WARN hdfs.DFSClient: Caught exception java.lang.InterruptedException    at java.lang.Object.wait(Native Method)    at java.lang.Thread.join(Thread.java:1281)    at java.lang.Thread.join(Thread.java:1355)    at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.closeResponder(DFSOutputStream.java:609)    at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.endBlock(DFSOutputStream.java:370)    at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:546)17/01/24 01:04:32 INFO input.FileInputFormat: Total input paths to process : 1（仅处理一个路径的数据导出）17/01/24 01:04:32 INFO input.FileInputFormat: Total input paths to process : 117/01/24 01:04:32 INFO mapreduce.JobSubmitter: number of splits:117/01/24 01:04:32 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative17/01/24 01:04:33 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1485230213604_000517/01/24 01:04:34 INFO impl.YarnClientImpl: Submitted application application_1485230213604_000517/01/24 01:04:34 INFO mapreduce.Job: The url to track the job: http://hadoop-allinone-200-123.wdcloud.locl:8088/proxy/application_1485230213604_0005/17/01/24 01:04:34 INFO mapreduce.Job: Running job: job_1485230213604_000517/01/24 01:04:46 INFO mapreduce.Job: Job job_1485230213604_0005 running in uber mode : false17/01/24 01:04:46 INFO mapreduce.Job:  map 0% reduce 0%17/01/24 01:04:57 INFO mapreduce.Job:  map 100% reduce 0%17/01/24 01:04:58 INFO mapreduce.Job: Job job_1485230213604_0005 completed successfully17/01/24 01:04:59 INFO mapreduce.Job: Counters: 30    File System Counters        FILE: Number of bytes read=0        FILE: Number of bytes written=137897        FILE: Number of read operations=0        FILE: Number of large read operations=0        FILE: Number of write operations=0        HDFS: Number of bytes read=334        HDFS: Number of bytes written=0        HDFS: Number of read operations=4        HDFS: Number of large read operations=0        HDFS: Number of write operations=0    Job Counters         Launched map tasks=1        Data-local map tasks=1        Total time spent by all maps in occupied slots (ms)=7444        Total time spent by all reduces in occupied slots (ms)=0        Total time spent by all map tasks (ms)=7444        Total vcore-milliseconds taken by all map tasks=7444        Total megabyte-milliseconds taken by all map tasks=7622656    Map-Reduce Framework        Map input records=4        Map output records=4        Input split bytes=162        Spilled Records=0        Failed Shuffles=0        Merged Map outputs=0        GC time elapsed (ms)=149        CPU time spent (ms)=2890        Physical memory (bytes) snapshot=184639488        Virtual memory (bytes) snapshot=2923687936        Total committed heap usage (bytes)=155713536    File Input Format Counters         Bytes Read=0    File Output Format Counters         Bytes Written=017/01/24 01:04:59 INFO mapreduce.ExportJobBase: Transferred 334 bytes in 30.6866 seconds (10.8842 bytes/sec)17/01/24 01:04:59 INFO mapreduce.ExportJobBase: Exported 4 records.（导出了4条记录）
 
可以看见，mysql表已导入数据
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至此，mysql和hdfs相互的数据导入导出就完毕了
 
[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 数据在mysq和hdfs之间的相互转换






























        

            
                
                  
                
                

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