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Thanks! Merging to master.
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Github user vanzin commented on the issue:
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It's a little weird to use the attempt id here anyway; the stage ID can
change for a different execution of the same stage, IIRC, and that would reset
the attempt id.
@squito probably rememb
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GitHub user mukulmurthy opened a pull request:
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[SPARK-24525][SS] Provide an option to limit number of rows in a MemorySink
## What changes were proposed in this pull request?
Provide an option to limit number of rows in a MemorySink
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@jose-torres @brkyvz for review
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Github user tedyu commented on the issue:
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Located the test output:
```
-rw-r--r-- 1 hbase hadoop 35335485506 Jun 13 20:36 target/unit-tests.log
```
Still need to find out cause for assertion failure.
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Github user squito commented on the issue:
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IMO your change is the right fix, not just a workaround. I don't think its
a scheduler bug (though its definitely unclear). I'll move that discussion to
the jira.
An alternative would be u
Github user maryannxue-databricks commented on a diff in the pull request:
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@@ -96,4 +99,38 @@ class DatasetCacheSuite extends QueryTes
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Github user cloud-fan commented on the issue:
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for a write job, the writing only happens at the last stage, so stage id
doesn't matter, and data source v2 API assumes `(job id, partition id, task
attemp id)` can uniquely define a write task, e
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@mgaido91 can you fix the conflicts? thanks!
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@HyukjinKwon I double checked, and there is nothing else that needs to be
fixed here in this file.
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ok to test
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As @squito suggested, we can either use taskAttemptId or combine
stageAttemptId and taskAttemptNumber together, both shall be able to represent
a unique task attempt.
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--- Diff: docs/configuration.md ---
@@ -472,15 +472,17 @@ Apart from these, the following properties are also
available, and may be useful
20
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This should go into branch-2.3 too.
I also think this is a little fragile (which is why I filed SPARK-24532),
and I've been playing with some alternative code, but it feels like a bit too
mu
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retest this please
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> So the problem here is, when we retry a stage, Spark doesn't kill the
tasks of the old stage and just launch tasks for the new stage
I think that's something that should be fixed, but it wo
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> IMO your change is the right fix, not just a workaround
@squito, part of the problem is that the output commit coordinator -- that
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@jiangxb1987 @gatorsmile @wzhfy Anyone could review this? Thanks!
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Github user edwinalu commented on the issue:
https://github.com/apache/spark/pull/21221
@squito , I've replaced PeakMemoryMetrics with just an Array[Long], and
added a custom serializer an deserializer for it.
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/21389
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/21559
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Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/21537#discussion_r195284269
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
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@@ -579,6 +579,22 @@ class CodegenContext {
Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/21556#discussion_r195283330
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
---
@@ -62,6 +62,16 @@ private[parquet] class Par
Github user AmplabJenkins commented on the issue:
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