apache - 500GB或1TB上的Hadoop 2.6和2.7 Apache Terasort

Question

在运行 map 和 reducer 启动时，它从0变为100失败，并显示:

15/05/12 07:21:27 INFO terasort.TeraSort: starting
15/05/12 07:21:27 WARN util.NativeCodeLoader: Unable to load native-hadoop     library for your platform... using builtin-java classes where applicable
15/05/12 07:21:29 INFO input.FileInputFormat: Total input paths to process :    18000

Spent 1514ms computing base-splits.
Spent 109ms computing TeraScheduler splits.
Computing input splits took 1624ms
Sampling 10 splits of 18000
Making 1 from 100000 sampled records
Computing parititions took 315ms
Spent 1941ms computing partitions.
15/05/12 07:21:30 INFO client.RMProxy: Connecting to ResourceManager at    n1/192.168.2.1:8032
15/05/12 07:21:31 INFO mapreduce.JobSubmitter: number of splits:18000
15/05/12 07:21:31 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1431389162125_0001
15/05/12 07:21:31 INFO impl.YarnClientImpl: Submitted application application_1431389162125_0001
15/05/12 07:21:31 INFO mapreduce.Job: The url to track the job: http://n1:8088/proxy/application_1431389162125_0001/
15/05/12 07:21:31 INFO mapreduce.Job: Running job: job_1431389162125_0001
15/05/12 07:21:37 INFO mapreduce.Job: Job job_1431389162125_0001 running in uber mode : false
15/05/12 07:21:37 INFO mapreduce.Job:  map 0% reduce 0%
15/05/12 07:21:47 INFO mapreduce.Job:  map 1% reduce 0%
15/05/12 07:22:01 INFO mapreduce.Job:  map 2% reduce 0%
15/05/12 07:22:13 INFO mapreduce.Job:  map 3% reduce 0%
15/05/12 07:22:25 INFO mapreduce.Job:  map 4% reduce 0%
15/05/12 07:22:38 INFO mapreduce.Job:  map 5% reduce 0%
15/05/12 07:22:50 INFO mapreduce.Job:  map 6% reduce 0%
15/05/12 07:23:02 INFO mapreduce.Job:  map 7% reduce 0%
15/05/12 07:23:15 INFO mapreduce.Job:  map 8% reduce 0%
15/05/12 07:23:27 INFO mapreduce.Job:  map 9% reduce 0%
15/05/12 07:23:40 INFO mapreduce.Job:  map 10% reduce 0%
15/05/12 07:23:52 INFO mapreduce.Job:  map 11% reduce 0%
15/05/12 07:24:02 INFO mapreduce.Job:  map 100% reduce 100%
15/05/12 07:24:06 INFO mapreduce.Job: Job job_1431389162125_0001 failed with  state FAILED due to: Task failed task_1431389162125_0001_r_000000
Job failed as tasks failed. failedMaps:0 failedReduces:1

这是默认配置，每次都会失败。

我插入到xml中的所有配置我都注释掉了，以发现此问题，但是我仍然遇到问题，仅在还原开始时作业会失败。

Answer 1

Yarn处理资源管理，还提供可以使用MapReduce和实时工作负载的批处理工作负载。

可以在“ yarn ”容器级别以及映射器和化简器级别设置内存设置。请求内存以 yarn 容器大小为增量。映射器和化简器任务在容器内运行。

mapreduce.map.memory.mb and mapreduce.reduce.memory.mb

上面的参数描述了map-reduce任务的内存上限，如果该任务预订的内存超过该限制，则相应的容器将被杀死。

这些参数确定可以分别分配给映射器和缩减任务的最大内存量。让我们看一个例子:Mapper由配置参数mapreduce.map.memory.mb中定义的内存上限限制。

但是，如果yarn.scheduler.minimum-allocation-mb的值大于mapreduce.map.memory.mb的值，则将遵守yarn.scheduler.minimum-allocation-mb并给出该大小的容器出来。

该参数需要仔细设置，如果设置不正确，可能会导致性能下降或内存不足错误。

mapreduce.reduce.java.opts and mapreduce.map.java.opts

此属性值必须小于mapreduce.map.memory.mb / mapreduce.reduce.memory.mb中定义的map / reduce任务的上限，因为它应该适合map / reduce任务的内存分配。