Weiqing Yang created SPARK-15857:
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Summary: Add Caller Context in Spark
Key: SPARK-15857
URL: https://issues.apache.org/jira/browse/SPARK-15857
Project: Spark
Issue Type: New Feature
Reporter: Weiqing Yang
Hadoop has implemented a feature of log tracing – caller context (Jira:
HDFS-9184 and 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 in
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), Hive (HIVE-12249, HIVE-12254)
and Pig(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.
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