hadoop - Hive 执行“插入......值......”非常慢

Question

我建立了一个 hadoop & hive 集群并尝试做一些测试。但它真的很慢。

桌子

表值计数

+--------------------------------------------------------------+--+
|                        createtab_stmt                        |
+--------------------------------------------------------------+--+
| CREATE TABLE `value_count`(                                  |
|   `key` int,                                                 |
|   `count` int,                                               |
|   `create_date` date COMMENT '????')                         |
| COMMENT 'This is a group table'                              |
| ROW FORMAT SERDE                                             |
|   'org.apache.hadoop.hive.ql.io.orc.OrcSerde'                |
| STORED AS INPUTFORMAT                                        |
|   'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'          |
| OUTPUTFORMAT                                                 |
|   'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'         |
| LOCATION                                                     |
|   'hdfs://avatarcluster/hive/warehouse/test.db/value_count'  |
| TBLPROPERTIES (                                              |
|   'COLUMN_STATS_ACCURATE'='{\"BASIC_STATS\":\"true\"}',      |
|   'numFiles'='7',                                            |
|   'numRows'='7',                                             |
|   'rawDataSize'='448',                                       |
|   'totalSize'='2297',                                        |
|   'transient_lastDdlTime'='1496217645')                      |
+--------------------------------------------------------------+--+

sql执行

insert into value_count values (5, 1, '2017-05-06');

我已经多次执行这个 sql，每次大约需要 4 或 5 分钟

hadoop 容器日志

2017-05-31 16:00:45,041 [INFO] [Dispatcher thread {Central}] |app.DAGAppMaster|: Central Dispatcher queue size after DAG completion, before cleanup: 0
2017-05-31 16:00:45,041 [INFO] [Dispatcher thread {Central}] |app.DAGAppMaster|: Waiting for next DAG to be submitted.
2017-05-31 16:00:45,042 [INFO] [Dispatcher thread {Central}] |app.DAGAppMaster|: Cleaning up DAG: name=insert into value_count valu...'2017-05-06')(Stage-1), with id=dag_1490688643958_53401_1
2017-05-31 16:00:45,042 [INFO] [Dispatcher thread {Central}] |container.AMContainerMap|: Cleaned up completed containers on dagComplete. Removed=0, Remaining=1
2017-05-31 16:00:45,044 [INFO] [Dispatcher thread {Central}] |app.DAGAppMaster|: Completed cleanup for DAG: name=insert into value_count valu...'2017-05-06')(Stage-1), with id=dag_1490688643958_53401_1
2017-05-31 16:00:50,749 [INFO] [DelayedContainerManager] |rm.YarnTaskSchedulerService|: No taskRequests. Container's idle timeout delay expired or is new. Releasing container, containerId=container_1490688643958_53401_01_000002, containerExpiryTime=1496217650577, idleTimeout=5000, taskRequestsCount=0, heldContainers=1, delayedContainers=0, isNew=false
2017-05-31 16:00:50,752 [INFO] [Dispatcher thread {Central}] |history.HistoryEventHandler|: [HISTORY][DAG:dag_1490688643958_53401_1][Event:CONTAINER_STOPPED]: containerId=container_1490688643958_53401_01_000002, stoppedTime=1496217650751, exitStatus=0
2017-05-31 16:00:50,753 [INFO] [ContainerLauncher #1] |launcher.TezContainerLauncherImpl|: Stopping container_1490688643958_53401_01_000002
2017-05-31 16:00:50,753 [INFO] [ContainerLauncher #1] |impl.ContainerManagementProtocolProxy|: Opening proxy : app08.hp.sp.tst.bmsre.com:51640
2017-05-31 16:00:51,628 [INFO] [Dispatcher thread {Central}] |container.AMContainerImpl|: Container container_1490688643958_53401_01_000002 exited with diagnostics set to Container failed, exitCode=-105. Container killed by the ApplicationMaster.
Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143

2017-05-31 16:01:29,678 [INFO] [AMRM Callback Handler Thread] |rm.YarnTaskSchedulerService|: Allocated: <memory:0, vCores:0> Free: <memory:6144, vCores:1> pendingRequests: 0 delayedContainers: 0 heartbeats: 51 lastPreemptionHeartbeat: 50
2017-05-31 16:02:19,740 [INFO] [AMRM Callback Handler Thread] |rm.YarnTaskSchedulerService|: Allocated: <memory:0, vCores:0> Free: <memory:6144, vCores:1> pendingRequests: 0 delayedContainers: 0 heartbeats: 101 lastPreemptionHeartbeat: 100
2017-05-31 16:03:09,801 [INFO] [AMRM Callback Handler Thread] |rm.YarnTaskSchedulerService|: Allocated: <memory:0, vCores:0> Free: <memory:6144, vCores:1> pendingRequests: 0 delayedContainers: 0 heartbeats: 151 lastPreemptionHeartbeat: 150
2017-05-31 16:03:59,858 [INFO] [AMRM Callback Handler Thread] |rm.YarnTaskSchedulerService|: Allocated: <memory:0, vCores:0> Free: <memory:6144, vCores:1> pendingRequests: 0 delayedContainers: 0 heartbeats: 201 lastPreemptionHeartbeat: 200
2017-05-31 16:04:49,915 [INFO] [AMRM Callback Handler Thread] |rm.YarnTaskSchedulerService|: Allocated: <memory:0, vCores:0> Free: <memory:6144, vCores:1> pendingRequests: 0 delayedContainers: 0 heartbeats: 251 lastPreemptionHeartbeat: 250
2017-05-31 16:05:39,971 [INFO] [AMRM Callback Handler Thread] |rm.YarnTaskSchedulerService|: Allocated: <memory:0, vCores:0> Free: <memory:6144, vCores:1> pendingRequests: 0 delayedContainers: 0 heartbeats: 301 lastPreemptionHeartbeat: 300
2017-05-31 16:06:09,581 [INFO] [DAGSubmissionTimer] |rm.TaskSchedulerManager|: TaskScheduler notified that it should unregister from RM
2017-05-31 16:06:09,581 [INFO] [DAGSubmissionTimer] |app.DAGAppMaster|: No current running DAG, shutting down the AM
2017-05-31 16:06:09,581 [INFO] [DAGSubmissionTimer] |app.DAGAppMaster|: DAGAppMasterShutdownHandler invoked
2017-05-31 16:06:09,581 [INFO] [DAGSubmissionTimer] |app.DAGAppMaster|: Handling DAGAppMaster shutdown
2017-05-31 16:06:09,582 [INFO] [AMShutdownThread] |app.DAGAppMaster|: Sleeping for 5 seconds before shutting down
2017-05-31 16:06:14,582 [INFO] [AMShutdownThread] |app.DAGAppMaster|: Calling stop for all the services
2017-05-31 16:06:14,582 [INFO] [AMShutdownThread] |rm.YarnTaskSchedulerService|: Initiating stop of YarnTaskScheduler
2017-05-31 16:06:14,582 [INFO] [AMShutdownThread] |rm.YarnTaskSchedulerService|: Releasing held containers
2017-05-31 16:06:14,583 [INFO] [AMShutdownThread] |rm.YarnTaskSchedulerService|: Removing all pending taskRequests
2017-05-31 16:06:14,583 [INFO] [AMShutdownThread] |history.HistoryEventHandler|: Stopping HistoryEventHandler
2017-05-31 16:06:14,583 [INFO] [AMShutdownThread] |recovery.RecoveryService|: Stopping RecoveryService
2017-05-31 16:06:14,583 [INFO] [AMShutdownThread] |recovery.RecoveryService|: Handle the remaining events in queue, queue size=0
2017-05-31 16:06:14,584 [INFO] [RecoveryEventHandlingThread] |recovery.RecoveryService|: EventQueue take interrupted. Returning
2017-05-31 16:06:14,584 [INFO] [AMShutdownThread] |recovery.RecoveryService|: Closing Summary Stream
2017-05-31 16:06:14,611 [INFO] [AMShutdownThread] |impl.SimpleHistoryLoggingService|: Stopping SimpleHistoryLoggingService, eventQueueBacklog=0
2017-05-31 16:06:14,611 [INFO] [HistoryEventHandlingThread] |impl.SimpleHistoryLoggingService|: EventQueue take interrupted. Returning
2017-05-31 16:06:14,613 [INFO] [DelayedContainerManager] |rm.YarnTaskSchedulerService|: AllocatedContainerManager Thread interrupted
2017-05-31 16:06:14,615 [INFO] [AMShutdownThread] |rm.YarnTaskSchedulerService|: Unregistering application from RM, exitStatus=SUCCEEDED, exitMessage=Session stats:submittedDAGs=0, successfulDAGs=1, failedDAGs=0, killedDAGs=0
, trackingURL=
2017-05-31 16:06:14,620 [INFO] [AMShutdownThread] |impl.AMRMClientImpl|: Waiting for application to be successfully unregistered.
2017-05-31 16:06:14,720 [INFO] [AMShutdownThread] |rm.YarnTaskSchedulerService|: Successfully unregistered application from RM
2017-05-31 16:06:14,721 [INFO] [AMShutdownThread] |rm.TaskSchedulerManager|: Shutting down AppCallbackExecutor
2017-05-31 16:06:14,721 [INFO] [AMRM Callback Handler Thread] |impl.AMRMClientAsyncImpl|: Interrupted while waiting for queue
java.lang.InterruptedException
    at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.reportInterruptAfterWait(AbstractQueuedSynchronizer.java:2014)
    at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:2048)
    at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:442)
    at org.apache.hadoop.yarn.client.api.async.impl.AMRMClientAsyncImpl$CallbackHandlerThread.run(AMRMClientAsyncImpl.java:274)
2017-05-31 16:06:14,726 [INFO] [AMShutdownThread] |mortbay.log|: Stopped HttpServer2$SelectChannelConnectorWithSafeStartup@0.0.0.0:0
2017-05-31 16:06:14,826 [INFO] [AMShutdownThread] |ipc.Server|: Stopping server on 49703
2017-05-31 16:06:14,827 [INFO] [IPC Server listener on 49703] |ipc.Server|: Stopping IPC Server listener on 49703
2017-05-31 16:06:14,827 [INFO] [AMShutdownThread] |ipc.Server|: Stopping server on 43709
2017-05-31 16:06:14,827 [INFO] [IPC Server Responder] |ipc.Server|: Stopping IPC Server Responder
2017-05-31 16:06:14,827 [INFO] [IPC Server listener on 43709] |ipc.Server|: Stopping IPC Server listener on 43709
2017-05-31 16:06:14,827 [INFO] [IPC Server Responder] |ipc.Server|: Stopping IPC Server Responder
2017-05-31 16:06:14,830 [INFO] [Thread-2] |app.DAGAppMaster|: DAGAppMasterShutdownHook invoked
2017-05-31 16:06:14,830 [INFO] [Thread-2] |app.DAGAppMaster|: The shutdown handler is still running, waiting for it to complete
2017-05-31 16:06:14,844 [INFO] [AMShutdownThread] |app.DAGAppMaster|: Completed deletion of tez scratch data dir, path=hdfs://avatarcluster/tmp/hive/hadoop/_tez_session_dir/46c45420-9bdf-40a5-83a5-c8d1d496abb8/.tez/application_1490688643958_53401
2017-05-31 16:06:14,844 [INFO] [AMShutdownThread] |app.DAGAppMaster|: Exiting DAGAppMaster..GoodBye!
2017-05-31 16:06:14,844 [INFO] [Thread-2] |app.DAGAppMaster|: The shutdown handler has completed

hadoop 集群模式

app05: NameNode/ResourceManager/JournalNode
app10: NameNode(Standby)/JournalNode
app08: DataNode/NodeManager/JournalNode
app09: DataNode/NodeManager

而 app05/08/09/10 是我的测试版机器，每台都有 32 个 vcore 和 48GB 内存。

hadoop 配置

核心站点.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://avatarcluster</value>
    </property>
    <property>
        <name>io.file.buffer.size</name>
        <value>131072</value>
    </property>
    <property>
        <name>hadoop.proxyuser.hadoop.hosts</name>
        <value>*</value>
    </property>
    <property>
        <name>hadoop.proxyuser.hadoop.groups</name>
        <value>*</value>
    </property>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/home/hadoop/hadoop-data/</value>
    </property>
    <property>
        <name>ha.zookeeper.quorum</name>
        <value>app02.hp.sp.tst.bmsre.com:2181</value>
    </property>
<property>
    <name>io.compression.codecs</name>
    <value>org.apache.hadoop.io.compress.SnappyCodec</value>
</property>
</configuration>

hdfs-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>dfs.nameservices</name>
        <value>avatarcluster</value>
    </property>
    <property>
        <name>dfs.ha.namenodes.avatarcluster</name>
        <value>nn1,nn2</value>
    </property>
    <property>
        <name>dfs.namenode.rpc-address.avatarcluster.nn1</name>
        <value>app05.hp.sp.tst.bmsre.com:8020</value>
    </property>
    <property>
        <name>dfs.namenode.rpc-address.avatarcluster.nn2</name>
        <value>app10.hp.sp.tst.bmsre.com:8020</value>
    </property>
    <property>
        <name>dfs.namenode.http-address.avatarcluster.nn1</name>
        <value>app05.hp.sp.tst.bmsre.com:50070</value>
    </property>
    <property>
        <name>dfs.namenode.http-address.avatarcluster.nn2</name>
        <value>app10.hp.sp.tst.bmsre.com:50070</value>
    </property>
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://app05.hp.sp.tst.bmsre.com:8485;app10.hp.sp.tst.bmsre.com:8485;app08.hp.sp.tst.bmsre.com:8485/avatarcluster
        </value>
    </property>
    <property>
        <name>dfs.client.failover.proxy.provider.avatarcluster</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property>
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>sshfence</value>
    </property>
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/home/hadoop/.ssh/id_rsa</value>
    </property>
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/home/hadoop/hadoop/journal-data</value>
    </property>
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/home/hadoop/hadoop/namenode</value>
    </property>
    <property>
        <name>dfs.blocksize</name>
        <value>134217728</value>
    </property>
    <property>
        <name>dfs.namenode.handler.count</name>
        <value>100</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>2</value>
    </property>
</configuration>

maprd-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>app05.hp.sp.tst.bmsre.com:10020</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>app05.hp.sp.tst.bmsre.com:19888</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.intermediate-done-dir</name>
        <value>/mr-history/tmp</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.done-dir</name>
        <value>/mr-history/done</value>
    </property>
    <property>
        <name>mapred.output.compress</name>
        <value>true</value>
    </property>
    <property>
        <name>mapred.output.compression.codec</name>
        <value>org.apache.hadoop.io.compress.SnappyCodec</value>
    </property>
    <property>
        <name>mapred.compress.map.output</name>
        <value>true</value>
    </property>
    <property>
        <name>mapreduce.map.memory.mb</name>
        <value>3048</value>
    </property>
    <property>
        <name>mapreduce.reduce.memory.mb</name>
        <value>3048</value>
    </property>
    <property>
        <name>mapreduce.map.java.opts</name>
        <value>-Xmx2024m</value>
    </property>
    <property>
        <name>mapreduce.reduce.java.opts</name>
        <value>-Xmx2024m</value>
    </property>
</configuration>

tez-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>
    <property>
        <name>tez.lib.uris</name>
        <value>${fs.defaultFS}/apps/tez-0.8.5.tar.gz</value>
    </property>
    <property>
        <name>tez.am.resource.memory.mb</name>
        <value>2048</value>
    </property>
    <property>
        <name>tez.task.resource.memory.mb</name>
        <value>2048</value>
    </property>
</configuration>

纱线站点.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>yarn.admin.acl</name>
        <value>*</value>
    </property>
    <property>
        <name>yarn.resourcemanager.address</name>
        <value>app05.hp.sp.tst.bmsre.com:8032</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>app05.hp.sp.tst.bmsre.com:8030</value>
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>app05.hp.sp.tst.bmsre.com:8031</value>
    </property>
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>app05.hp.sp.tst.bmsre.com:8033</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>app05.hp.sp.tst.bmsre.com:8088</value>
    </property>
    <property>
        <name>yarn.nodemanager.local-dirs</name>
        <value>/home/hadoop/hadoop/nodemanager-workdir</value>
    </property>
    <property>
        <name>yarn.nodemanager.log-dirs</name>
        <value>/home/hadoop/hadoop/nodemanager-logs</value>
    </property>
    <property>
        <name>yarn.nodemanager.log.retain-seconds</name>
        <value>3600</value>
    </property>
    <property>
        <name>yarn.nodemanager.remote-app-log-dir</name>
        <value>/home/hadoop/hadoop/nodemanager-remote-app-logs</value>
    </property>
    <property>
        <name>yarn.nodemanager.remote-app-log-dir-suffix</name>
        <value>logs</value>
    </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  </property>
    <property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>4096</value>
    </property>
    <property>
        <name>yarn.scheduler.minimum-allocation-mb</name>
        <value>1024</value>
    </property>
</configuration>

score 1 · Accepted Answer

我不确定您的集群配置（节点、内存、cpus），如果您可以使用该信息更新帖子将会很有用。同时，第一个猜测是您的 Tez 配置错误，所以我建议将 MB 减小到更小的值（对于微小的数据，几 mbs 应该足够了）。此外，尝试使用 mapred 引擎运行相同的命令以限制问题

hive.execution.engine=mr

score 1 · Accepted Answer

Hive 可以使用 Apache Tez 执行引擎，而不是古老的 Map-reduce 引擎。我不会详细介绍这里提到的使用 Tez 的许多好处；相反，我想提出一个简单的建议：如果在您的环境中默认情况下未打开它，请通过在 Hive 查询的开头将以下内容设置为“true”来使用 Tez：

set hive.execution.engine=tez;

score 0 · Accepted Answer

我知道这是两年前的帖子，但留下答案以供分享，以防我将来忘记自己。

在您的日志中，Tez 会话正在等待提交下一个 DAG（或查询），直到会话超时。

Tez 倾向于重用 Application Master 和 Containers 来加速作业执行，因此它正在等待下一个作业。

对我来说，这发生在通过 Hue 界面提交查询时。由于 Hue 网络会话仍处于打开状态，因此在会话期间保持 AM 活动是有意义的。

您可以使用禁用或减少等待时间tez.session.am.dag.submit.timeout.secs。默认值为 300 秒，这正是您的工作完成所需的时间。

如果通过脚本（python、java）提交查询，您不会看到此问题，因为脚本将在查询完成后执行并退出。

score 0 · Accepted Answer

一般来说，当你使用

插入值

每次执行语句时，它往往会创建小文件。由于 Hive 没有创建任何约束和索引，因此语句会继续添加小文件。如果为表启用了 ACID 属性，它还会尝试定期进行压缩，尝试将所有小增量文件合并为一个大文件。这个过程有时会很耗时。

因此，最好在本地创建一个文件

LOAD DATA LOCAL INPATH '/FILE/PATH' INTO TABLE TABLE_NAME ;

希望这可以帮助。

hadoop - Hive 执行“插入......值......”非常慢

4 回答 4

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