You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Nan Zhu (JIRA)" <ji...@apache.org> on 2016/12/16 20:32:59 UTC
[jira] [Updated] (SPARK-18905) Potential Issue of Semantics of
BatchCompleted
[ https://issues.apache.org/jira/browse/SPARK-18905?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nan Zhu updated SPARK-18905:
----------------------------
Description:
the current implementation of Spark streaming considers a batch is completed no matter the results of the jobs (https://github.com/apache/spark/blob/1169db44bc1d51e68feb6ba2552520b2d660c2c0/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala#L203)
Let's consider the following case:
A micro batch contains 2 jobs and they read from two different kafka topics respectively. One of this job is failed due to some problem in the user defined logic.
1. The main thread in the Spark streaming application will execute the line mentioned above,
2. and another thread (checkpoint writer) will make a checkpoint file immediately after this line is executed.
3. Then due to the current error handling mechanism in Spark Streaming, StreamingContext will be closed (https://github.com/apache/spark/blob/1169db44bc1d51e68feb6ba2552520b2d660c2c0/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala#L214)
the user recovers from the checkpoint file, and because the JobSet containing the failed job has been removed (taken as completed) before the checkpoint is constructed, the data being processed by the failed job would never be reprocessed?
I might have missed something in the checkpoint thread or this handleJobCompletion()....or it is a potential bug
was:
the current implementation of Spark streaming considers a batch is completed no matter the result of the jobs (https://github.com/apache/spark/blob/1169db44bc1d51e68feb6ba2552520b2d660c2c0/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala#L203)
Let's consider the following case:
A micro batch contains 2 jobs and they read from two different kafka topics respectively. One of this job is failed due to some problem in the user defined logic.
1. The main thread in the Spark streaming application will execute the line mentioned above,
2. and another thread (checkpoint writer) will make a checkpoint file immediately after this line is executed.
3. Then due to the current error handling mechanism in Spark Streaming, StreamingContext will be closed (https://github.com/apache/spark/blob/1169db44bc1d51e68feb6ba2552520b2d660c2c0/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala#L214)
the user recovers from the checkpoint file, and because the JobSet containing the failed job has been removed (taken as completed) before the checkpoint is constructed, the data being processed by the failed job would never be reprocessed?
I might have missed something in the checkpoint thread or this handleJobCompletion()....or it is a potential bug
> Potential Issue of Semantics of BatchCompleted
> ----------------------------------------------
>
> Key: SPARK-18905
> URL: https://issues.apache.org/jira/browse/SPARK-18905
> Project: Spark
> Issue Type: Bug
> Components: DStreams
> Affects Versions: 2.0.0, 2.0.1, 2.0.2
> Reporter: Nan Zhu
>
> the current implementation of Spark streaming considers a batch is completed no matter the results of the jobs (https://github.com/apache/spark/blob/1169db44bc1d51e68feb6ba2552520b2d660c2c0/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala#L203)
> Let's consider the following case:
> A micro batch contains 2 jobs and they read from two different kafka topics respectively. One of this job is failed due to some problem in the user defined logic.
> 1. The main thread in the Spark streaming application will execute the line mentioned above,
> 2. and another thread (checkpoint writer) will make a checkpoint file immediately after this line is executed.
> 3. Then due to the current error handling mechanism in Spark Streaming, StreamingContext will be closed (https://github.com/apache/spark/blob/1169db44bc1d51e68feb6ba2552520b2d660c2c0/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala#L214)
> the user recovers from the checkpoint file, and because the JobSet containing the failed job has been removed (taken as completed) before the checkpoint is constructed, the data being processed by the failed job would never be reprocessed?
> I might have missed something in the checkpoint thread or this handleJobCompletion()....or it is a potential bug
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org