[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

时间：2021-04-18 08:30:07

安装hive

1、下载hive-2.1.1（搭配hadoop版本为2.7.3）

2、解压到文件夹下

/wdcloud/app/hive-2.1.1

3、配置环境变量

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

4、在mysql上创建元数据库hive_metastore编码选latin，并授权

grant all on hive_metastore.* to 'root'@'%' IDENTIFIED BY 'weidong' with grant option;

flush privileges;

5、新建hive-site.xml，内容如下：

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?><!--
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
this work for additional information regarding copyright ownership.
The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><configuration>
<!-- WARNING!!! This file is auto generated for documentation purposes ONLY! -->
<!-- WARNING!!! Any changes you make to this file will be ignored by Hive. -->
<!-- WARNING!!! You must make your changes in hive-site.xml instead. -->
<!-- Hive Execution Parameters -->
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://192.168.200.250:3306/hive_metastore?createDatabaseIfNotExist=true</value>
</property>
<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
</property>
<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>root</value>
</property>
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>weidong</value>
</property>
<property>
<name>datanucleus.schema.autoCreateTables</name>
<value>true</value>
</property>
<property>
<name>hive.metastore.warehouse.dir</name>
<value>/hive/warehouse</value>
</property>
<property>
<name>hive.exec.scratchdir</name>
<value>/hive/warehouse</value>
</property>
<property>
<name>hive.querylog.location</name>
<value>/wdcloud/app/hive-2.1.1/logs</value>
</property>
<property>
<name>hive.aux.jars.path</name>
<value>/wdcloud/app/hbase-1.1.6/lib</value>
</property>
<property>
<name>hive.metastore.uris</name>
<value>thrift://192.168.200.123:9083</value>
</property>
</configuration>

6、hive-env.sh

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

7、放开日志

cp hive-log4j2.properties. template  hive-log4j2.properties

cp hive-exec-log4j2.properties.template  hive-exec-log4j2.properties

8、导入mysql connector jar包

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

9、启动元数据库服务

hive --service metastore &

出错：

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

再次启动

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

成功不报错

元数据库也生成了一些需要的表

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

调试 模式命令  hive -hiveconf hive.root.logger=DEBUG,console

客户端连接

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

OK，现在hive就安装完毕了，让我们来执行下将mysql数据通过sqoop导入hive

 sqoop import --connect jdbc:mysql://192.168.200.250:3306/sqoop --table widgets_copy -m 1 --hive-import --username root -P

出错

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

解决方法:

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

再次执行，导入成功，后台日志

[hadoop@hadoop-allinone-- conf]$ sqoop import --connect jdbc:mysql://192.168.200.250:3306/sqoop --table widgets_copy -m 1 --hive-import --username root -P

// :: INFO sqoop.Sqoop: Running Sqoop version: 1.4.

Enter password:

// :: INFO tool.BaseSqoopTool: Using Hive-specific delimiters for output. You can override

// :: INFO tool.BaseSqoopTool: delimiters with --fields-terminated-by, etc.

// :: INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

// :: INFO tool.CodeGenTool: Beginning code generation
// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT

// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT

// :: INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /wdcloud/app/hadoop-2.7.

Note: /tmp/sqoop-hadoop/compile/4a89a67225918969c1c0f4c7c13168e9/widgets_copy.java uses or overrides a deprecated API.

Note: Recompile with -Xlint:deprecation for details.

// :: INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/4a89a67225918969c1c0f4c7c13168e9/widgets_copy.jar

// :: WARN manager.MySQLManager: It looks like you are importing from mysql.

// :: WARN manager.MySQLManager: This transfer can be faster! Use the --direct

// :: WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.

// :: INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)

// :: INFO mapreduce.ImportJobBase: Beginning import of widgets_copy
// :: INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/wdcloud/app/hadoop-2.7./share/hadoop/common/lib/slf4j-log4j12-1.7..jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/wdcloud/app/hbase-1.1./lib/slf4j-log4j12-1.7..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]

// :: INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar

// :: INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

// :: INFO client.RMProxy: Connecting to ResourceManager at hadoop-allinone-200-123.wdcloud.locl/192.168.200.123:8032

// :: INFO db.DBInputFormat: Using read commited transaction isolation

// :: INFO mapreduce.JobSubmitter: number of splits:

// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1485230213604_0011
// :: INFO impl.YarnClientImpl: Submitted application application_1485230213604_0011
// :: INFO mapreduce.Job: The url to track the job: http://hadoop-allinone-200-123.wdcloud.locl:8088/proxy/application_1485230213604_0011/

// :: INFO mapreduce.Job: Running job: job_1485230213604_0011

// :: INFO mapreduce.Job: Job job_1485230213604_0011 running in uber mode : false

// :: INFO mapreduce.Job:  map % reduce %

// :: INFO mapreduce.Job:  map % reduce %

// :: INFO mapreduce.Job: Job job_1485230213604_0011 completed successfully
// :: INFO mapreduce.Job: Counters:

    File System Counters

        FILE: Number of bytes read=

        FILE: Number of bytes written=

        FILE: Number of read operations=

        FILE: Number of large read operations=

        FILE: Number of write operations=

        HDFS: Number of bytes read=

        HDFS: Number of bytes written=

        HDFS: Number of read operations=

        HDFS: Number of large read operations=

        HDFS: Number of write operations=

    Job Counters

        Launched map tasks=

        Other local map tasks=

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

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

        Total time spent by all map tasks (ms)=

        Total vcore-milliseconds taken by all map tasks=

        Total megabyte-milliseconds taken by all map tasks=

    Map-Reduce Framework

        Map input records=

        Map output records=

        Input split bytes=

        Spilled Records=

        Failed Shuffles=

        Merged Map outputs=

        GC time elapsed (ms)=

        CPU time spent (ms)=

        Physical memory (bytes) snapshot=

        Virtual memory (bytes) snapshot=

        Total committed heap usage (bytes)=

    File Input Format Counters

        Bytes Read=

    File Output Format Counters

        Bytes Written=

// :: INFO mapreduce.ImportJobBase: Transferred 169 bytes in 31.7543 seconds (5.3221 bytes/sec)
// :: INFO mapreduce.ImportJobBase: Retrieved 4 records.
// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT

// :: WARN hive.TableDefWriter: Column price had to be cast to a less precise type in Hive

// :: WARN hive.TableDefWriter: Column design_date had to be cast to a less precise type in Hive

// :: INFO hive.HiveImport: Loading uploaded data into Hive（将生成在HDFS的数据加载到HIVE中）
// :: INFO hive.HiveImport: SLF4J: Class path contains multiple SLF4J bindings.

// :: INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/wdcloud/app/hive-2.1./lib/log4j-slf4j-impl-2.4..jar!/org/slf4j/impl/StaticLoggerBinder.class]

// :: INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/wdcloud/app/hbase-1.1./lib/slf4j-log4j12-1.7..jar!/org/slf4j/impl/StaticLoggerBinder.class]

// :: INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/wdcloud/app/hadoop-2.7./share/hadoop/common/lib/slf4j-log4j12-1.7..jar!/org/slf4j/impl/StaticLoggerBinder.class]

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

// :: INFO hive.HiveImport: SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]

// :: INFO hive.HiveImport:

// :: INFO hive.HiveImport: Logging initialized using configuration in file:/wdcloud/app/hive-2.1./conf/hive-log4j2.properties Async: true

// :: INFO hive.HiveImport: OK

// :: INFO hive.HiveImport: Time taken: 3.687 seconds

// :: INFO hive.HiveImport: Loading data to table default.widgets_copy
// :: INFO hive.HiveImport: OK

// :: INFO hive.HiveImport: Time taken: 1.92 seconds

// :: INFO hive.HiveImport: Hive import complete.
// :: INFO hive.HiveImport: Export directory is contains the _SUCCESS file only, removing the directory.（加载进Hive成功后将HDFS上的中间数据删除掉）

如果曾经执行失败过，那再执行的时候，会有错误提示：

ERROR tool.ImportTool: Encountered IOException running import job: org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory xxx already exists

这时，执行hadoop fs -rmr xxx 即可

或者在命令行加上参数

--hive-overwrite ： Overwrite existing data inthe Hive table

这个参数会自动覆盖掉曾经存在与hive表的数据

这样即使失败了也会自动去覆盖

查看导入数据

[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 将mysq数据导入hive

拓展： Sqoop-1.4.4工具import和export使用详解

http://blog.csdn.net/wangmuming/article/details/25303831

附上最后的配置文件hive-site.xml

<?xml version="1.0" encoding="UTF-8" standalone="no"?>

<?xml-stylesheet type="text/xsl" href="configuration.xsl"?><!--

   Licensed to the Apache Software Foundation (ASF) under one or more

   contributor license agreements.  See the NOTICE file distributed with

   this work for additional information regarding copyright ownership.

   The ASF licenses this file to You under the Apache License, Version 2.0

   (the "License"); you may not use this file except in compliance with

   the License.  You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software

   distributed under the License is distributed on an "AS IS" BASIS,

   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

   See the License for the specific language governing permissions and

   limitations under the License.

--><configuration>

  <!-- WARNING!!! This file is auto generated for documentation purposes ONLY! -->

  <!-- WARNING!!! Any changes you make to this file will be ignored by Hive.   -->

  <!-- WARNING!!! You must make your changes in hive-site.xml instead.         -->

  <!-- Hive Execution Parameters -->

          <property>

            <name>javax.jdo.option.ConnectionURL</name>

            <value>jdbc:mysql://192.168.200.250:3306/hive_metastore?createDatabaseIfNotExist=true</value>

         </property>

         <property>

                  <name>javax.jdo.option.ConnectionDriverName</name>

                  <value>com.mysql.jdbc.Driver</value>

         </property>

         <property>

                  <name>javax.jdo.option.ConnectionUserName</name>

                  <value>root</value>

         </property>

         <property>

                   <name>javax.jdo.option.ConnectionPassword</name>

                   <value>weidong</value>

         </property>

         <property>

                  <name>datanucleus.schema.autoCreateTables</name>

                  <value>true</value>

         </property>

         <property>

                   <name>hive.metastore.warehouse.dir</name>

                   <value>/hive/warehouse</value>

         </property>

         <property>

                   <name>hive.exec.scratchdir</name>

                   <value>/hive/warehouse</value>

         </property>

         <property>

                   <name>hive.querylog.location</name>

                   <value>/wdcloud/app/hive-2.1./logs</value>

         </property>

         <property>

                   <name>hive.aux.jars.path</name>

                  <value>/wdcloud/app/hbase-1.1./lib</value>

         </property>

         <property>

                   <name>hive.metastore.uris</name>

                   <value>thrift://192.168.200.123:9083</value>

         </property>

         <property>

                   <name>hive.metastore.schema.verification</name>

                   <value>false</value>

         </property>

</configuration>

下一步需要考虑如何定时去执行这些数据同步和增量同步任务

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