spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Weiqing Yang <yangweiqing...@gmail.com>
Subject Add Caller Context in Spark
Date Thu, 09 Jun 2016 23:37:44 GMT
Hi,

Hadoop has implemented a feature of log tracing – caller context (Jira:
HDFS-9184 <https://issues.apache.org/jira/browse/HDFS-9184> and YARN-4349
<https://issues.apache.org/jira/browse/YARN-4349>). The motivation is to
better diagnose and understand how specific applications impacting parts of
the Hadoop system and potential problems they may be creating (e.g.
overloading NN). As HDFS mentioned inHDFS-9184
<https://issues.apache.org/jira/browse/HDFS-9184>, for a given HDFS
operation, it's very helpful to track which upper level job issues it. The
upper level callers may be specific Oozie tasks, MR jobs, hive queries,
Spark jobs.

Hadoop ecosystems like MapReduce, Tez (TEZ-2851
<https://issues.apache.org/jira/browse/TEZ-2851>), Hive (HIVE-12249
<https://issues.apache.org/jira/browse/HIVE-12249>, HIVE-12254
<https://issues.apache.org/jira/browse/HIVE-12254>) and Pig(PIG-4714
<https://issues.apache.org/jira/browse/PIG-4714>) have implemented their
caller contexts. Those systems invoke HDFS client API and Yarn client API
to setup caller context, and also expose an API to pass in caller context
into it.

Lots of Spark applications are running on Yarn/HDFS. Spark can also
implement its caller context via invoking HDFS/Yarn API, and also expose an
API to its upstream applications to set up their caller contexts. In the
end, the spark caller context written into Yarn log / HDFS log can
associate with task id, stage id, job id and app id.  That is also very
good for Spark users to identify tasks especially if Spark supports
multi-tenant environment in the future.

e.g.  Run SparkKmeans on Spark.

In HDFS log:
…
2016-05-25 15:36:23,748 INFO FSNamesystem.audit: allowed=true
ugi=yang(auth:SIMPLE)        ip=/127.0.0.1        cmd=getfileinfo
src=/data/mllib/kmeans_data.txt/_spark_metadata       dst=null
perm=null      proto=rpc callerContext=SparkKMeans
application_1464728991691_0009 running on Spark

 2016-05-25 15:36:27,893 INFO FSNamesystem.audit: allowed=true
ugi=yang (auth:SIMPLE)        ip=/127.0.0.1        cmd=open
src=/data/mllib/kmeans_data.txt       dst=null       perm=null
proto=rpc
callerContext=JobID_0_stageID_0_stageAttemptId_0_taskID_0_attemptNumber_0 on
Spark
…

“application_146472899169” is the application id.

I do have code that works with spark master branch. I am going to create a
Jira. Please feel free to let me know if you have any concern or comments.

Thanks,
Qing

Mime
View raw message