将Hive统计分析结果导入到MySQL数据库表中（一）——Sqoop导入方式，hivesqoop
将Hive统计分析结果导入到MySQL数据库表中（一）——Sqoop导入方式，hivesqoop
最近在做一个交通流的数据分析，需求是对于海量的城市交通数据，需要使用MapReduce清洗后导入到HBase中存储，然后使用Hive外部表关联HBase，对HBase中数据进行查询、统计分析，将分析结果保存在一张Hive表中，最后使用Sqoop将该表中数据导入到MySQL中。整个流程大概如下：
一、HBase数据库表
下面我主要介绍Hive关联HBase表——Sqoop导出Hive表到MySQL这些流程，原始数据集收集、MapReduce清洗及WEB界面展示此处不介绍。
hbase(main):003:0> list TABLE transtable 1 row(s) in 0.0250 seconds => ["transtable"] hbase(main):004:0> describe 'transtable' DESCRIPTION ENABLED 'transtable', {NAME => 'jtxx', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0', VER true SIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2147483647', KEEP_DELETED_CELLS => 'false', BLO CKSIZE => '65536', IN_MEMORY => 'false', BLOCKCACHE => 'true'} 1 row(s) in 0.0480 seconds创建一个名叫“transtable”的HBase表，列族是：“jtxx”。HBase中的部分数据如下：hbase(main):008:0> get 'transtable','32108417000000013220140317000701' COLUMN CELL jtxx:cdbh timestamp=1429597736296, value=03 jtxx:clbj timestamp=1429597736296, value=0 jtxx:cllb timestamp=1429597736296, value=0 jtxx:cllx timestamp=1429597736296, value=3 jtxx:clsd timestamp=1429597736296, value=127.00 jtxx:hphm timestamp=1429597736296, value=\xE8\x8B\x8FKYV152 jtxx:wflx timestamp=1429597736296, value=0 jtxx:xsfx timestamp=1429597736296, value=03 8 row(s) in 0.1550 seconds
二、创建Hive外部表关联HBase表
create external table transJtxx_Hbase
(
clxxbh string,
xsfx string,
cdbh string,
hphm string,
clsd string,
cllx string,
clbj string,
cllb string,
wflx string
)
stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
with serdeproperties ("hbase.columns.mapping" =":key,jtxx:xsfx,jtxx:cdbh,jtxx:hphm,jtxx:clsd,jtxx:cllx,jtxx:clbj,jtxx:cllb,jtxx:wflx") TBLPROPERTIES ("hbase.table.name" = "transtable");
hbase.columns.mapping要对应hbase数据库transtable表中列族下的列限定符。此处一定要是外部表
查看是否关联成功，如何执行一条语句能够查询出HBase表中数据，则关联成功。
hive> select * from transjtxx_hbase where clxxbh like '321084170000000132%'; Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_1428394594787_0007, Tracking URL = http://secondmgt:8088/proxy/application_1428394594787_0007/ Kill Command = /home/hadoopUser/cloud/hadoop/programs/hadoop-2.2.0/bin/hadoop job -kill job_1428394594787_0007 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 2015-04-21 17:27:18,136 Stage-1 map = 0%, reduce = 0% 2015-04-21 17:27:35,029 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 12.31 sec MapReduce Total cumulative CPU time: 12 seconds 310 msec Ended Job = job_1428394594787_0007 MapReduce Jobs Launched: Job 0: Map: 1 Cumulative CPU: 12.31 sec HDFS Read: 256 HDFS Write: 636 SUCCESS Total MapReduce CPU Time Spent: 12 seconds 310 msec OK 32108417000000013220140317000701 03 03 苏KYV152 127.00 3 0 0 0 32108417000000013220140317000705 02 03 苏KRU593 127.00 2 0 0 0 32108417000000013220140317000857 03 02 苏KYL920 28.00 4 0 0 0 32108417000000013220140317001145 02 02 苏K19V75 136.00 6 0 0 0 32108417000000013220140317001157 02 02 鲁QV0897 150.00 4 0 0 0 32108417000000013220140317001726 02 02 苏KL2938 23.00 1 0 0 0 32108417000000013220140317001836 02 02 苏J5S373 142.00 4 0 0 0 32108417000000013220140317001844 02 02 苏KK8332 158.00 3 0 0 0 32108417000000013220140317002039 03 02 苏KK8820 17.00 0 0 0 0 32108417000000013220140317002206 03 03 苏KK8902 32.00 4 0 0 0 Time taken: 36.018 seconds, Fetched: 10 row(s)三、创建用于存放查询、统计分析结果的Hive表
因为此处我是模拟环境，所以我创建一个和hive关联表transjtxx_hbase一样字段类型的表，用于存放查询结果，如下：
hive> create table temptrans
> (clxxbh string,
> xsfx string,
> cdbh string,
> hphm string,
> clsd string,
> cllx string,
> clbj string,
> cllb string,
> wflx string
> ) ;
OK
Time taken: 0.112 seconds四、通过查询结果向Hive表中插入数据
使用Hive表四种数据导入方式之一——通过SQL查询语句向Hive表中插入数据。（详细介绍可以查看我的另外一篇博文：Hive表中四种不同数据导出方式以及如何自定义导出列分隔符）此处我以查询某个路口过往车辆为例。因为，clxxbh是由路口编号+日期组成，此处不使用overwrite，是因为后续会循环执行，之前导入的数据需要保留，所以必须使用into，如下：
hive> insert into table temptrans select * from transjtxx_hbase where clxxbh like '321084170000000133%'; Total jobs = 3 Launching Job 1 out of 3 Number of reduce tasks is set to 0 since there's no reduce operator Starting Job = job_1428394594787_0009, Tracking URL = http://secondmgt:8088/proxy/application_1428394594787_0009/ Kill Command = /home/hadoopUser/cloud/hadoop/programs/hadoop-2.2.0/bin/hadoop job -kill job_1428394594787_0009 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 2015-04-21 19:05:03,398 Stage-1 map = 0%, reduce = 0% 2015-04-21 19:05:24,091 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 12.71 sec MapReduce Total cumulative CPU time: 12 seconds 710 msec Ended Job = job_1428394594787_0009 Stage-4 is selected by condition resolver. Stage-3 is filtered out by condition resolver. Stage-5 is filtered out by condition resolver. Moving data to: hdfs://secondmgt:8020/hive/scratchdir/hive_2015-04-21_19-04-48_325_2835499611469580351-1/-ext-10000 Loading data to table hive.temptrans Table hive.temptrans stats: [numFiles=2, numRows=12, totalSize=1380, rawDataSize=732] MapReduce Jobs Launched: Job 0: Map: 1 Cumulative CPU: 12.71 sec HDFS Read: 256 HDFS Write: 815 SUCCESS Total MapReduce CPU Time Spent: 12 seconds 710 msec OK Time taken: 37.229 seconds
hive> select * from tempTrans; OK 32108417000000013220140317000701 03 03 苏KYV152 127.00 3 0 0 0 32108417000000013220140317000705 02 03 苏KRU593 127.00 2 0 0 0 32108417000000013220140317000857 03 02 苏KYL920 28.00 4 0 0 0 32108417000000013220140317001145 02 02 苏K19V75 136.00 6 0 0 0 32108417000000013220140317001157 02 02 鲁QV0897 150.00 4 0 0 0 32108417000000013220140317001726 02 02 苏KL2938 23.00 1 0 0 0 32108417000000013220140317001836 02 02 苏J5S373 142.00 4 0 0 0 32108417000000013220140317001844 02 02 苏KK8332 158.00 3 0 0 0 32108417000000013220140317002039 03 02 苏KK8820 17.00 0 0 0 0 32108417000000013220140317002206 03 03 苏KK8902 32.00 4 0 0 0 32108417000000013320140317000120 02 02 苏KRW076 0.00 7 0 0 0 32108417000000013320140317000206 00 02 苏AHF730 0.00 4 0 0 0 32108417000000013320140317000207 02 02 苏KYJ792 0.00 6 0 0 0 32108417000000013320140317000530 00 01 苏K53T85 0.00 1 0 0 0 32108417000000013320140317000548 03 01 苏KR0737 0.00 7 0 0 0 32108417000000013320140317000605 03 02 苏KYU203 0.00 1 0 0 0 32108417000000013320140317000659 01 02 苏K3R762 0.00 4 0 0 0 32108417000000013320140317001042 02 03 苏KYK578 0.00 6 0 0 0 32108417000000013320140317001222 02 03 苏KK8385 0.00 2 0 0 0 32108417000000013320140317001418 02 03 苏K26F89 0.00 7 0 0 0 32108417000000013320140317001538 02 03 苏KK8987 0.00 5 0 0 0 32108417000000013320140317001732 01 01 苏KYB127 0.00 7 0 0 0 Time taken: 0.055 seconds, Fetched: 22 row(s)五、创建用于导入数据的MySQL数据库及其表
mysql> create database transport;
Query OK, 1 row affected (0.00 sec)
mysql> use transport;
Database changed
mysql> create table jtxx
-> (
-> clxxbh varchar(64) not null primary key,
-> xsfx varchar(2),
-> cdbh varchar(4),
-> hphm varchar(32),
-> clsd varchar(16),
-> cllx varchar(2),
-> clbj varchar(8),
-> cllb varchar(8),
-> wflx varchar(8)
-> );
Query OK, 0 rows affected (0.04 sec)
mysql> show tables;
+---------------------+
| Tables_in_transport |
+---------------------+
| jtxx |
+---------------------+
1 row in set (0.00 sec)
mysql> select * from jtxx;
Empty set (0.00 sec)
六、Sqoop将Hive表数据导入到MySQL中此处创建MySQL表的时候，一定要注意字段名称要和Hive表中对应
[hadoopUser@secondmgt ~]$ sqoop-export --connect jdbc:mysql://secondmgt:3306/transport --username hive --password hive --table jtxx --export-dir /hive/warehouse/hive.db/temptrans
使用以上导出命令会报如下错误：
15/04/21 19:38:52 INFO mapreduce.Job: Task Id : attempt_1428394594787_0010_m_000001_0, Status : FAILED
Error: java.io.IOException: Can't export data, please check task tracker logs
at org.apache.sqoop.mapreduce.TextExportMapper.map(TextExportMapper.java:112)
at org.apache.sqoop.mapreduce.TextExportMapper.map(TextExportMapper.java:39)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
at org.apache.sqoop.mapreduce.AutoProgressMapper.run(AutoProgressMapper.java:64)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:763)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:339)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:162)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1491)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:157)
Caused by: java.util.NoSuchElementException
at java.util.ArrayList$Itr.next(ArrayList.java:834)
at jtxx.__loadFromFields(jtxx.java:387)
at jtxx.parse(jtxx.java:336)
at org.apache.sqoop.mapreduce.TextExportMapper.map(TextExportMapper.java:83)
... 10 more
这个错误的原因是指定Hive中表字段之间使用的分隔符错误，供Sqoop读取解析不正确。如果是由hive执行mapreduce操作汇总的结果，默认的分隔符是 '\001'，否则如果是从HDFS文件导入的则分隔符则应该是'\t'。此处我是hive执行mapreduce分析汇总的结果，所以默认的分隔是'\001'。Sqoop命令修改如下，指定分隔符：
[hadoopUser@secondmgt ~]$ sqoop-export --connect jdbc:mysql://secondmgt:3306/transport --username hive --password hive --table jtxx --export-dir /hive/warehouse/hive.db/temptrans --input-fields-terminated-by '\001'注意：
此处的Sqoop导出命令，当MySQL数据库中对应表为空，无数据的时候可以成功执行，但是当里面有数据，即从Hive表中需要导出的数据字段中，和MySQL表中关键字有重复的记录时候，进程会死住，不再往下执行，查看Hadoop任务界面出现内存被大部分占用，队列被占100%占用情况，如下：
[hadoopUser@secondmgt ~]$ sqoop-export --connect jdbc:mysql://secondmgt:3306/transport --username hive --password hive --table jtxx --export-dir /hive/warehouse/hive.db/temptrans --input-fields-terminated-by '\001' Warning: /usr/lib/hcatalog does not exist! HCatalog jobs will fail. Please set $HCAT_HOME to the root of your HCatalog installation. 15/04/21 20:08:28 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 15/04/21 20:08:28 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 15/04/21 20:08:28 INFO tool.CodeGenTool: Beginning code generation 15/04/21 20:08:29 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `jtxx` AS t LIMIT 1 15/04/21 20:08:29 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `jtxx` AS t LIMIT 1 15/04/21 20:08:29 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoopUser/cloud/hadoop/programs/hadoop-2.2.0 Note: /tmp/sqoop-hadoopUser/compile/67173774b957b511b4d62bc4ebe56e23/jtxx.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 15/04/21 20:08:30 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoopUser/compile/67173774b957b511b4d62bc4ebe56e23/jtxx.jar 15/04/21 20:08:30 INFO mapreduce.ExportJobBase: Beginning export of jtxx 15/04/21 20:08:30 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:/home/hadoopUser/cloud/hadoop/programs/hadoop-2.2.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/home/hadoopUser/cloud/hbase/hbase-0.96.2-hadoop2/lib/slf4j-log4j12-1.6.4.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] 15/04/21 20:08:30 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar 15/04/21 20:08:31 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative 15/04/21 20:08:31 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative 15/04/21 20:08:31 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps 15/04/21 20:08:31 INFO client.RMProxy: Connecting to ResourceManager at secondmgt/192.168.2.133:8032 15/04/21 20:08:32 INFO input.FileInputFormat: Total input paths to process : 2 15/04/21 20:08:32 INFO input.FileInputFormat: Total input paths to process : 2 15/04/21 20:08:32 INFO mapreduce.JobSubmitter: number of splits:3 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.job.classpath.files is deprecated. Instead, use mapreduce.job.classpath.files 15/04/21 20:08:32 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.cache.files.filesizes is deprecated. Instead, use mapreduce.job.cache.files.filesizes 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.cache.files is deprecated. Instead, use mapreduce.job.cache.files 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.class 15/04/21 20:08:32 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name 15/04/21 20:08:32 INFO Configuration.deprecation: mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir 15/04/21 20:08:32 INFO Configuration.deprecation: mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.cache.files.timestamps is deprecated. Instead, use mapreduce.job.cache.files.timestamps 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.class 15/04/21 20:08:32 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir 15/04/21 20:08:32 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1428394594787_0016 15/04/21 20:08:33 INFO impl.YarnClientImpl: Submitted application application_1428394594787_0016 to ResourceManager at secondmgt/192.168.2.133:8032 15/04/21 20:08:33 INFO mapreduce.Job: The url to track the job: http://secondmgt:8088/proxy/application_1428394594787_0016/ 15/04/21 20:08:33 INFO mapreduce.Job: Running job: job_1428394594787_0016
sqoop任务无法提交，一直处于以上状态无法继续执行，查看Hadoop任务界面，出现如下情况，队列被100%占用：
解决办法：
1、查看Hadoop正在运行的进程有哪些，hadoop job -list
2、杀死僵尸进程，hadoop job -kill [job-id]
3、修改Sqoop执行命令如下：
sqoop-export --connect jdbc:mysql://secondmgt:3306/transport --username hive --password hive --table jtxx --update-key clxxbh -- update-mode allowinsert --export-dir /hive/warehouse/hive.db/temptrans --input-fields-terminated-by '\001'添加了两个参数：--update-key clxxbh --update-mode allowinsert，前面一个表示如果后期导入的数据关键字和MySQL数据库中数据存在相同的，则更新该行记录，后一个表示将目标数据库中原来不存在的数据也导入到数据库表中，即存在的数据保留，新的数据插入，它后接另一个选项是updateonly，即只更新数据，不插入新数据。详细介绍，查看另外一篇博文（ Sqoop1.4.4将文件数据集从HDFS中导出到MySQL数据库表中）
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