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
