多个线程运行MR程序时hadoop出现的问题
夜间多个任务同时并行,总有几个随机性有任务失败,查看日志: 刷选关键词 Caused by 或者 FAILED
cat -n ads_channel.log |grep "Caused by"
7732 Caused by: java.util.concurrent.ExecutionException: java.io.IOException: Rename cannot overwrite non empty destination directory /tmp/hadoop-hdfs/mapred/local/1576781334421
7737 Caused by: java.io.IOException: Rename cannot overwrite non empty destination directory /tmp/hadoop-hdfs/mapred/local/1576781334421
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> java.io.IOException: java.util.concurrent.ExecutionException: org.apache.hadoop.fs.FileAlreadyExistsException: rename destination /tmp/hadoop-hdfs/mapred/local/1579374502408 already exists. at org.apache.hadoop.mapred.LocalDistributedCacheManager.setup(LocalDistributedCacheManager.java:143) at org.apache.hadoop.mapred.LocalJobRunner$Job.<init>(LocalJobRunner.java:171) at org.apache.hadoop.mapred.LocalJobRunner.submitJob(LocalJobRunner.java:758) at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:244) at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1307) at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1304) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1920) at org.apache.hadoop.mapreduce.Job.submit(Job.java:1304) at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:578) at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:573) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1920) at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:573) at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:564) at org.apache.hadoop.hive.ql.exec.mr.ExecDriver.execute(ExecDriver.java:436) at org.apache.hadoop.hive.ql.exec.mr.MapRedTask.execute(MapRedTask.java:142) at org.apache.hadoop.hive.ql.exec.Task.executeTask(Task.java:214) at org.apache.hadoop.hive.ql.exec.TaskRunner.runSequential(TaskRunner.java:99) at org.apache.hadoop.hive.ql.Driver.launchTask(Driver.java:2052) at org.apache.hadoop.hive.ql.Driver.execute(Driver.java:1748) at org.apache.hadoop.hive.ql.Driver.runInternal(Driver.java:1501) at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1285) at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1275) at org.apache.hadoop.hive.cli.CliDriver.processLocalCmd(CliDriver.java:226) at org.apache.hadoop.hive.cli.CliDriver.processCmd(CliDriver.java:175) at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:389) at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:324) at org.apache.hadoop.hive.cli.CliDriver.executeDriver(CliDriver.java:726) at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:699) at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:634) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.hadoop.util.RunJar.run(RunJar.java:221) at org.apache.hadoop.util.RunJar.main(RunJar.java:136) Caused by: java.util.concurrent.ExecutionException: org.apache.hadoop.fs.FileAlreadyExistsException: rename destination /tmp/hadoop-hdfs/mapred/local/1579374502408 already exists. at java.util.concurrent.FutureTask.report(FutureTask.java:122) at java.util.concurrent.FutureTask.get(FutureTask.java:192) at org.apache.hadoop.mapred.LocalDistributedCacheManager.setup(LocalDistributedCacheManager.java:139) ... 38 more Caused by: org.apache.hadoop.fs.FileAlreadyExistsException: rename destination /tmp/hadoop-hdfs/mapred/local/1579374502408 already exists. at org.apache.hadoop.fs.FileSystem.rename(FileSystem.java:1310) at org.apache.hadoop.fs.DelegateToFileSystem.renameInternal(DelegateToFileSystem.java:193) at org.apache.hadoop.fs.AbstractFileSystem.renameInternal(AbstractFileSystem.java:744) at org.apache.hadoop.fs.FilterFs.renameInternal(FilterFs.java:236) at org.apache.hadoop.fs.AbstractFileSystem.rename(AbstractFileSystem.java:674) at org.apache.hadoop.fs.FileContext.rename(FileContext.java:932) at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:369) at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:60) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Job Submission failed with exception 'java.io.IOException(java.util.concurrent.ExecutionException: org.apache.hadoop.fs.FileAlreadyExistsException: rename destination /tmp/hadoop-hdfs/mapred/local/1579374502408 already exists.)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.mr.MapRedTask
扩展:
cat -n ads_channel.log |grep "Caused by" 或者 grep ads_channel.log -e "Caused by" 或者 grep -E "Caused by|FAILED" ads_channel.log #两个关键词
grep "2019-12-21" ads_channel.log | grep "Caused by" ads_channel.log
cat ads_channel.log | grep "Caused by" -B 10 ##根据关键字查看前20行日志
cat ads_channel.log | grep "Caused by" -A 10 ##根据关键字查看后20行日志
cat ads_channel.log | grep "Caused by" -C 10 #根据关键字查看前后10行日志
说明:
-A 表示关键字之后,After
-B 表示关键字之前,Before
-C 表示关键字前后,Context
vim ads_channel.log
:set nu :7749 (跳转到指定行数)
实时查询多个关键字的日志信息
命令:tail -f ads_channel.log |grep -E “Caused by"
问题原因:
当多个线程运行MR程序时hadoop出现的问题:
https://issues.apache.org/jira/browse/MAPREDUCE-6992
https://issues.apache.org/jira/browse/MAPREDUCE-6441
hdfs会创建一个以当前时间的时间戳命名的文件.当两个mr任务在同一毫秒提交,造成了文件的并发访问问题.
yarn的运行模式:
1-本地模式(LocalJobRunner实现)
mapreduce.framework.name设置为local,则不会使用YARN集群来分配资源,在本地节点执行。在本地模式运行的任务,无法发挥集群的优势。注:在web UI是查看不到本地模式运行的任务。
对 hive有些了解的人都会知道,hive 会将 SQL 语句最终转化成分布式执行的 mapreduce 任务计划。对于大数量集的数据启动 mapreduce 所花费的时间是渺小的。因为数据量大,并且分布再不同的机器上,在不同的机器上处理,这样做是 hive 的优势之一。然而当处理小数量,并且数据都聚集再一台机器上时,那么启动本地模式是非常有意的,不可避免的启动 mapreduce,将数据拉回客户端,本地处理,这样减少了分处理后合并花费的时间。如此一来,对数据量比较小的操作,就可以在本地执行,这样要比提交任务到集群执行效率要快很多。
启动本地模式,需要配置如下参数:
hive.exec.mode.local.auto 决定 Hive 是否应该自动地根据输入文件大小,在本地运行。
hive.exec.mode.local.auto.inputbytes.max 最大输入数据量,当输入数据量小于这个值的时候将会启动本地模式,默认是 128M。
hive.exec.mode.local.auto.tasks.max 最大输入文件个数,当输入文件个数小于这个值的时候将会启动本地模式。(默认4)
当一个job满足如下条件才能真正使用本地模式:
1.job的输入数据大小必须小于参数:hive.exec.mode.local.auto.inputbytes.max(默认128MB) 2.job的map数必须小于参数:hive.exec.mode.local.auto.tasks.max(默认4) 3.job的reduce数必须为0或者1
2-Yarn模式(YARNRunner实现)
mapreduce.framework.name设置为yarn,当客户端配置mapreduce.framework.name为yarn时, 客户端会使用YARNRunner与服务端通信, 而YARNRunner真正的实现是通过ClientRMProtocol与RM交互, 包括提交Application, 查询状态等功能。但是根据任务的特性,分为两种方式执行任务
3-Uber模式:
为降低小作业延迟而设计的一种模式,所有任务,不管是Map Task,还是Reduce Task,均在同一个Container中顺序执行,这个Container其实也是MRAppMaster所在Container
4-Non-Uber模式:
对于运行时间较长的大作业,先为Map Task申请资源,当Map Task运行完成数目达到一定比例后再为Reduce Task申请资源。
解决办法:
1-在不改源代码的情况下,取消自动启动本地模式,根据集群环境,临时在运行程序时设置:
set hive.exec.mode.local.auto = false
2-在调度系统中设置设置失败重试.
azkaban配置失败重试如下: type =command command = xxxxxx retries=3 retry.backoff=60000 #毫秒数
参考:https://blog.csdn.net/weixin_39445556/article/details/80348976
在官网找到了这个bug,在2.7.1版本中已经修复了这个bug,对集群进行升级:
This is a bug in Hadoop 2.6.0. It's been marked as fixed but it still happens occasionally (see: https://issues.apache.org/jira/browse/YARN-2624).
[hdfs@el-hadoop-1 logs]$ hadoop dfsadmin -report ##查看hadoop状况: DEPRECATED: Use of this script to execute hdfs command is deprecated. Instead use the hdfs command for it. Configured Capacity: 1242537227061 (1.13 TB) Present Capacity: 1154802876345 (1.05 TB) DFS Remaining: 1125514018745 (1.02 TB) DFS Used: 29288857600 (27.28 GB) DFS Used%: 2.54% Under replicated blocks: 0 Blocks with corrupt replicas: 0 Missing blocks: 0 Missing blocks (with replication factor 1): 0 ------------------------------------------------- Live datanodes (3): Name: 172.26.0.106:50010 (el-hadoop-1) Hostname: el-hadoop-1 Rack: /default Decommission Status : Normal Configured Capacity: 414179075687 (385.73 GB) DFS Used: 9740627968 (9.07 GB) Non DFS Used: 22051710567 (20.54 GB) DFS Remaining: 360492523769 (335.73 GB) DFS Used%: 2.35% DFS Remaining%: 87.04% Configured Cache Capacity: 4294967296 (4 GB) Cache Used: 0 (0 B) Cache Remaining: 4294967296 (4 GB) Cache Used%: 0.00% Cache Remaining%: 100.00% Xceivers: 8 Last contact: Sat Dec 21 11:29:07 CST 2019 Name: 172.26.0.108:50010 (el-hadoop-2) Hostname: el-hadoop-2 Rack: /default Decommission Status : Normal Configured Capacity: 414179075687 (385.73 GB) DFS Used: 9774043136 (9.10 GB) Non DFS Used: 0 (0 B) DFS Remaining: 382510819168 (356.24 GB) DFS Used%: 2.36% DFS Remaining%: 92.35% Configured Cache Capacity: 4294967296 (4 GB) Cache Used: 0 (0 B) Cache Remaining: 4294967296 (4 GB) Cache Used%: 0.00% Cache Remaining%: 100.00% Xceivers: 8 Last contact: Sat Dec 21 11:29:06 CST 2019 Name: 172.26.0.109:50010 (el-hadoop-3) Hostname: el-hadoop-3 Rack: /default Decommission Status : Normal Configured Capacity: 414179075687 (385.73 GB) DFS Used: 9774186496 (9.10 GB) Non DFS Used: 0 (0 B) DFS Remaining: 382510675808 (356.24 GB) DFS Used%: 2.36% DFS Remaining%: 92.35% Configured Cache Capacity: 4294967296 (4 GB) Cache Used: 0 (0 B) Cache Remaining: 4294967296 (4 GB) Cache Used%: 0.00% Cache Remaining%: 100.00% Xceivers: 8 Last contact: Sat Dec 21 11:29:08 CST 2019