报错锦集

2021-06-28/2022~报错总结

2021-06-28  本文已影响0人  勇于自信
1.运行内存问题报错

运行从ods到dwd层的脚本，跑MapReduce任务时报错内容如下：

Logging initialized using configuration in jar:file:/usr/local/src/apache-hive-3.1.0-bin/lib/hive-common-3.1.0.jar!/hive-log4j2.properties Async: true
Hive Session ID = 2928985c-003d-4e65-9c9f-7df704a0e929
Query ID = root_20210628143131_870136d1-7127-4fdc-a8b5-518215736057
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks not specified. Estimated from input data size: 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 mapreduce.job.reduces=<number>
Starting Job = job_1624695615043_0009, Tracking URL = http://master:8088/proxy/application_1624695615043_0009/
Kill Command = /usr/local/src/hadoop-2.7.3/bin/mapred job  -kill job_1624695615043_0009
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
  21-06-28 14:31:52,049 Stage-1 map = 0%,  reduce = 0%
2021-06-28 14:32:16,979 Stage-1 map = 100%,  reduce = 100%
Ended Job = job_1624695615043_0009 with errors
Error during job, obtaining debugging information...
Examining task ID: task_1624695615043_0009_m_000000 (and more) from job job_1624695615043_0009

Task with the most failures(4):
-----
Task ID:
  task_1624695615043_0009_m_000000

URL:
  http://master:8088/taskdetails.jsp?jobid=job_1624695615043_0009&tipid=task_1624695615043_0009_m_000000
-----
Diagnostic Messages for this Task:
Container [pid=20836,containerID=container_1624695615043_0009_01_000005] is running beyond virtual memory limits. Current usage: 372.0 MB of 1 GB physical memory used; 6.9 GB of 2.1 GB virtual memory used. Killing container.
Dump of the process-tree for container_1624695615043_0009_01_000005 :
        |- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
        |- 21057 20836 20836 20836 (java) 765 25 7280078848 94845 /usr/local/src/jdk1.8.0_144/bin/java -Djava.net.preferIPv4Stack=true -Dhadoop.metrics.log.level=WARN -Xmx5006m -Djava.io.tmpdir=/usr/local/src/hadoop-2.7.3/tmp/nm-local-dir/usercache/root/appcache/application_1624695615043_0009/container_1624695615043_0009_01_000005/tmp -Dlog4j.configuration=container-log4j.properties -Dyarn.app.container.log.dir=/usr/local/src/hadoop-2.7.3/logs/userlogs/application_1624695615043_0009/container_1624695615043_0009_01_000005 -Dyarn.app.container.log.filesize=0 -Dhadoop.root.logger=INFO,CLA -Dhadoop.root.logfile=syslog org.apache.hadoop.mapred.YarnChild 172.16.157.182 41533 attempt_1624695615043_0009_m_000000_3 5
        |- 20836 20834 20836 20836 (bash) 4 5 116170752 375 /bin/bash -c /usr/local/src/jdk1.8.0_144/bin/java -Djava.net.preferIPv4Stack=true -Dhadoop.metrics.log.level=WARN  -Xmx5006m -Djava.io.tmpdir=/usr/local/src/hadoop-2.7.3/tmp/nm-local-dir/usercache/root/appcache/application_1624695615043_0009/container_1624695615043_0009_01_000005/tmp -Dlog4j.configuration=container-log4j.properties -Dyarn.app.container.log.dir=/usr/local/src/hadoop-2.7.3/logs/userlogs/application_1624695615043_0009/container_1624695615043_0009_01_000005 -Dyarn.app.container.log.filesize=0 -Dhadoop.root.logger=INFO,CLA -Dhadoop.root.logfile=syslog org.apache.hadoop.mapred.YarnChild 172.16.157.182 41533 attempt_1624695615043_0009_m_000000_3 5 1>/usr/local/src/hadoop-2.7.3/logs/userlogs/application_1624695615043_0009/container_1624695615043_0009_01_000005/stdout 2>/usr/local/src/hadoop-2.7.3/logs/userlogs/application_1624695615043_0009/container_1624695615043_0009_01_000005/stderr

Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143


FAILED: Execution Error, return code 2 from org.apache.hadoop.hive.ql.exec.mr.MapRedTask
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1  Reduce: 1   HDFS Read: 0 HDFS Write: 0 FAIL

Total MapReduce CPU Time Spent: 0 msec

报错原因：
MapReduce运行的Container试图使用过多的内存，而被
NodeManager kill掉了。

解决办法：

mapred-site.xml中设置map和reduce任务的内存配置如下：(value中实际配置的内存需要根据自己机器内存大小及应用情况进行修改)

yarn-site.xml中添加配置：

<property>
　　<name>mapreduce.map.memory.mb</name>
　　<value>1536</value>
</property>
<property>
　　<name>mapreduce.map.java.opts</name>
　　<value>-Xmx1024M</value>
</property>
<property>
　　<name>mapreduce.reduce.memory.mb</name>
　　<value>3072</value>
</property>
<property>
　　<name>mapreduce.reduce.java.opts</name>
　　<value>-Xmx2560M</value>
</property>
2.执行Hadoop的mapreduce的jar包出现内存问题

报错如下：



产生原因：
因为从机上运行的Container试图使用过多的内存，而被NodeManager kill掉了
解决办法：
修改mapred-site.xml文件：添加以下配置

<property>
　　<name>mapreduce.map.memory.mb</name>
　　<value>1536</value>
</property>
<property>
　　<name>mapreduce.map.java.opts</name>
　　<value>-Xmx1024M</value>
</property>
<property>
　　<name>mapreduce.reduce.memory.mb</name>
　　<value>3072</value>
</property>
<property>
　　<name>mapreduce.reduce.java.opts</name>
　　<value>-Xmx2560M</value>
</property>

重启yarn即可

3.hive3.1.2安装后执行插入语句报错

报错信息：


4.运行Hadoop MapReduce示例报错

报错信息如下：



日志打印到mapreduce.Job: Running job: job_1649499142410_0001长时间卡住。
分析原因：
这个问题是由于yarn默认设置的内存比较大，但是虚拟机内存没那么大，所以要调整内存大小，在yarn-site.xml文件添加一下配置：

<property>
  <name>yarn.app.mapreduce.am.resource.mb</name>
  <value>256</value>
</property>

再次运行MapReduce示例，成功执行：



5.sqoop导入MySQL数据到HDFS卡住问题

导入执行命令：

sqoop import --connect jdbc:mysql://bigdata101:3306/company --username root --password 123456 --table staff \--target-dir /user/company --delete-target-dir --num-mappers 1 --fields-terminated-by "\t"

报错信息：



执行以上脚本一直卡在这里很久没动，结果还是报错。
解决办法：
在hive的conf目录下增加如下配置：

[root@bigdata101 conf]# vim sqoop-env.sh
[root@bigdata101 bin]# cd /usr/local/src/sqoop-1.4.7.bin__hadoop-2.6.0/conf/
[root@bigdata101 conf]# vim sqoop-env.sh  
增加
export ZOOKEEPER_HOME=/usr/local/src/zookeeper-3.4.10
export ZOOCFGDIR=/usr/local/src/zookeeper-3.4.10

整体配置如下：

export HADOOP_COMMON_HOME=/usr/local/src/hadoop-3.1.3
export HADOOP_MAPRED_HOME=/usr/local/src/hadoop-3.1.3
export HIVE_HOME=/usr/local/src/apache-hive-3.1.2-bin
export ZOOKEEPER_HOME=/usr/local/src/zookeeper-3.4.10
export ZOOCFGDIR=/usr/local/src/zookeeper-3.4.10

增加了zookeeper的配置后，再次运行就很快导入了。


6.datax部署填坑

1）下载DataX安装包并上传到hadoop102的/opt/software
下载地址：http://datax-opensource.oss-cn-hangzhou.aliyuncs.com/datax.tar.gz
2）解压datax.tar.gz到/opt/module

[root@bigdata101 datax]# tar -zxvf datax.tar.gz -C /usr/local/src/

3）自检，执行如下命令

[root@bigdata101 datax]# python bin/datax.py job/job.json

执行报错如下：



解决办法：

  1. 删除datax/plugin/reader下所有.xxxx隐藏文件
    注意：一定要.    *er这种方式匹配文件，否则会匹配到里面的隐藏jar包
[root@bigdata101 datax]# find /opt/module/datax/plugin/reader/ -type f -name "._*er" | xargs rm -rf

同理也删除datax/plugin/writer/下所有._xxxx隐藏文件

[root@bigdata101 datax]# find /opt/module/datax/plugin/writer/ -type f -name "._*er" | xargs rm -rf

再次执行成功！

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