Python 太多工作进程失败时Dask应用程序失败
我正在EMR集群上运行一个使用Dask纱线(0.6.0)的Dask(1.2)应用程序。今天我遇到了一个情况,我的工作人员失败了(由于HDFS错误),skein.ApplicationMaster将继续重新创建新的工作人员。 如果太多工人失败,是否有办法指示Dask纱线取消申请 具体而言,我的应用程序主日志如下所示:Python 太多工作进程失败时Dask应用程序失败,python,dask,dask-distributed,Python,Dask,Dask Distributed,我正在EMR集群上运行一个使用Dask纱线(0.6.0)的Dask(1.2)应用程序。今天我遇到了一个情况,我的工作人员失败了(由于HDFS错误),skein.ApplicationMaster将继续重新创建新的工作人员。 如果太多工人失败,是否有办法指示Dask纱线取消申请 具体而言,我的应用程序主日志如下所示: 19/06/21 16:00:27 INFO skein.ApplicationMaster: RESTARTING: adding new container to replace
19/06/21 16:00:27 INFO skein.ApplicationMaster: RESTARTING: adding new container to replace dask.worker_805.
19/06/21 16:00:27 INFO skein.ApplicationMaster: REQUESTED: dask.worker_806
19/06/21 16:00:27 WARN skein.ApplicationMaster: FAILED: dask.worker_804 - Could not obtain block: BP-1234110000-10.174.17.184-1561122672601:blk_1073741831_1007 file=/user/hadoop/.skein/application_1561122685021_0003/FED3ABF369AAE224B4BB8A3A77120E1C/cached_volume.sqlite3
org.apache.hadoop.hdfs.BlockMissingException: Could not obtain block: BP-1234110000-10.174.17.184-1561122672601:blk_1073741831_1007 file=/user/hadoop/.skein/application_1561122685021_0003/FED3ABF369AAE224B4BB8A3A77120E1C/cached_volume.sqlite3
at org.apache.hadoop.hdfs.DFSInputStream.chooseDataNode(DFSInputStream.java:983)
at org.apache.hadoop.hdfs.DFSInputStream.blockSeekTo(DFSInputStream.java:642)
at org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:882)
at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:934)
at java.io.DataInputStream.read(DataInputStream.java:100)
at org.apache.hadoop.io.IOUtils.copyBytes(IOUtils.java:85)
at org.apache.hadoop.io.IOUtils.copyBytes(IOUtils.java:59)
at org.apache.hadoop.io.IOUtils.copyBytes(IOUtils.java:119)
at org.apache.hadoop.fs.FileUtil.copy(FileUtil.java:366)
at org.apache.hadoop.yarn.util.FSDownload.copy(FSDownload.java:267)
at org.apache.hadoop.yarn.util.FSDownload.access$000(FSDownload.java:63)
at org.apache.hadoop.yarn.util.FSDownload$2.run(FSDownload.java:361)
at org.apache.hadoop.yarn.util.FSDownload$2.run(FSDownload.java:359)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1698)
at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:358)
at org.apache.hadoop.yarn.util.FSDownload.call(FSDownload.java:62)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
无限次如果使用主构造函数,则可以使用
worker\u restarts
kwarg设置最大的worker重启次数:
# Allow a maximum of 3 worker restarts before failure
cluster = YarnCluster(worker_restarts=3, ...)
或者,如果使用,则可以使用指定允许的最大重新启动次数
# /path/to/spec.yaml
name: dask
queue: myqueue
services:
dask.worker:
# Don't start any workers initially
instances: 0
# A maximum of 3 worker failures are allowed before failure
max_restarts: 3
# Restrict workers to 4 GiB and 2 cores each
resources:
memory: 4 GiB
vcores: 2
# Distribute this python environment to every worker node
files:
environment: /path/to/my/environment.tar.gz
# The bash script to start the worker
# Here we activate the environment, then start the worker
script: |
source environment/bin/activate
dask-yarn services worker