【问题标题】:hive hadoop: selecting data from table getting errorhive hadoop:从表中选择数据出错
【发布时间】:2016-05-29 22:20:32
【问题描述】:

在 Hive 中创建外部表后,我想知道推文的数量,所以我编写了以下查询,但出现此错误,请问如何解决此问题,这是 mapred-site.xml 的配置

    <configuration>

 <property>
    <name>mapred.job.tracker</name>
    <value>localhost:8021</value>
  </property>

hive> select count(*) from tweet;        
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapred.reduce.tasks=<number>
Starting Job = job_1464556774961_0005, Tracking URL = http://ubuntu:8088/proxy/application_1464556774961_0005/
Kill Command = /usr/local/hadoop/bin/hadoop job  -Dmapred.job.tracker=localhost:8021 -kill job_1464556774961_0005
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2016-05-29 15:14:24,207 Stage-1 map = 0%,  reduce = 0%
2016-05-29 15:14:30,496 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:31,532 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:32,558 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:33,592 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:34,625 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:35,649 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:36,676 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:37,697 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:38,720 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:39,745 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.91 sec
2016-05-29 15:14:40,776 Stage-1 map = 50%,  reduce = 17%, Cumulative CPU 2.14 sec
2016-05-29 15:14:41,804 Stage-1 map = 50%,  reduce = 17%, Cumulative CPU 2.14 sec
2016-05-29 15:14:42,823 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.91 sec
2016-05-29 15:14:43,847 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.91 sec
MapReduce Total cumulative CPU time: 1 seconds 910 msec
Ended Job = job_1464556774961_0005 with errors
Error during job, obtaining debugging information...
Examining task ID: task_1464556774961_0005_m_000000 (and more) from job job_1464556774961_0005
Exception in thread "Thread-128" java.lang.RuntimeException: Error while reading from task log url
    at org.apache.hadoop.hive.ql.exec.errors.TaskLogProcessor.getStackTraces(TaskLogProcessor.java:240)
    at org.apache.hadoop.hive.ql.exec.JobDebugger.showJobFailDebugInfo(JobDebugger.java:227)
    at org.apache.hadoop.hive.ql.exec.JobDebugger.run(JobDebugger.java:92)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.IOException: Server returned HTTP response code: 400 for URL: http://ubuntu:13562/tasklog?taskid=attempt_1464556774961_0005_m_000000_3&start=-8193
    at sun.net.www.protocol.http.HttpURLConnection.getInputStream0(HttpURLConnection.java:1840)
    at sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1441)
    at java.net.URL.openStream(URL.java:1045)
    at org.apache.hadoop.hive.ql.exec.errors.TaskLogProcessor.getStackTraces(TaskLogProcessor.java:192)
    ... 3 more
FAILED: Execution Error, return code 2 from org.apache.hadoop.hive.ql.exec.MapRedTask
MapReduce Jobs Launched: 
Job 0: Map: 2  Reduce: 1   Cumulative CPU: 1.91 sec   HDFS Read: 277 HDFS Write: 0 FAIL
Total MapReduce CPU Time Spent: 1 seconds 910 msec
hive> 

【问题讨论】:

  • HDFS 写入:0 FAIL 看起来要么没有 HDFS 权限,要么空间问题 ....
  • 请帮我解决这个问题,权限是哪个(hadoop fs -chmod 777 for which dir/file)和哪个空间??

标签: hadoop hive hadoop-streaming hadoop-partitioning flume-twitter


【解决方案1】:

这是由于 hive-exec jar 中缺少 hive-storage-api 模块。您需要将 hive 更新到最新版本才能获取最新的 hive 修复。

临时解决方法是显式添加存储 api jar。

add jar ./dist/hive/lib/hive-storage-api-2.0.0-SNAPSHOT.jar;

【讨论】:

  • @javac :如果它帮助您解决问题,请勾选此作为已接受的答案
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