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https://issues.apache.org/jira/browse/SPARK-42582?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17693579#comment-17693579
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Tengfei Huang edited comment on SPARK-42582 at 2/26/23 3:27 AM:
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This is also discussed in PR: https://github.com/apache/spark/pull/39459
cc [~mridulm80] [~Ngone51]
Created this ticket to track the issue about inconsistent persisted rdd blocks
issue.
was (Author: ivoson):
This is also discussed in PR: https://github.com/apache/spark/pull/39459
cc [~mridulm80] cc [~Ngone51]
Created this ticket to track the issue about inconsistent persisted rdd blocks
issue.
> Persisted RDD blocks can be inconsistent if the RDD computation is
> indeterminate
> --------------------------------------------------------------------------------
>
> Key: SPARK-42582
> URL: https://issues.apache.org/jira/browse/SPARK-42582
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 3.3.2
> Reporter: Tengfei Huang
> Priority: Major
>
> When a rdd includes indeterminate operations, the rdd results can be
> different each time we recompute it.
> And when we cache such a rdd, we may have multiple rdd block replicas having
> different data. Here is an example:
> 1. Task A generated the rdd block rdd_1_1 on executor E1;
> 2. Task B on executor E2 tried to fetch remote rdd_1_1 from E1 but failed,
> then it will compute and cache another block on E2;
> If the results on E1 and E2 are differnet, we'll have 2 blocks for the same
> rdd partition with different data.
> The behavior will be unexpcted for such cases.
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