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ok to test
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@dbtsai Nope. I did not hit any issue. :)
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Just curious.
It is very interesting to me since the recent three tries consistently
cause a timeout failure at the same test.
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> great! looking forward to seeing arrow 0.10.0 come out.
@cloud-fan Arrow has already been released and the artifacts are available
- sorry I should have made a post to indicate that. T
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@srowen @budde @ajfabbri
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As noted in #22146; stripping off bouncy castle and upgrading the SDK
worked. But a local test run of just this patch brought up the same error seen
in #22081
```
WithoutAggregat
GitHub user steveloughran opened a pull request:
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[SPARK-25111][BUILD] increment kinesis client/producer & aws-sdk versions
## What changes were proposed in this pull request?
Increment the kinesis client, producer and transient AWS
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--- Diff: python/pyspark/taskcontext.py ---
@@ -95,3 +95,92 @@ def getLocalProperty(self, key):
Get a local property set upstream in the dri
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Github user steveloughran commented on the issue:
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And if you bump up the kinesis client and AWS SDK version to 1.11.271,
those failures go away.
```
Run completed in 15 minutes, 28 seconds.
Total number of tests run: 59
S
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LGTM as well
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I took a quick look at the shuffle writer and feel it will be hard to
insert a sort there.
I have a simpler proposal for the fix. To trigger this bug, there must be a
shuffle before the `
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FYI, I just did a kinesis test run with this PR on a JVM with the unlimited
JCE installed (explicitly verified by shasum of the relevant JARs); failure
with cert errors.
```
succe
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LGTM. I think to have transformer framework working properly, it's required
to have attributes in `CountVector`. Being said that, we should deal with the
issue of allocating big attributes for sparse
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Merged into master. Thanks.
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great! looking forward to seeing arrow 0.10.0 come out.
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retest this please
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@squito @tgravescs I am probably missing something about why hash
partitioner helps, can you please clarify ?
IIRC the partitioner for CoalescedRDD when shuffle is enabled is
HashPartitioner ...
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cc @koeninger as well
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@jiangxb1987
Here are some of the differences from the original PR
- I also ported the follow up PR #20426
- I ported #20088 (for SPARK-22905) to get the tests to pass. I also p
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@felixcheung Testsuites is added. Thanks for reviewing!
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@tgravescs I vaguely remember someone at y! labs telling me (more than a
decade back) about MR always doing a sort as part of its shuffle to avoid a
variant of this problem by design.
Essentiall
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**Teradata**:
![image](https://user-images.githubusercontent.com/5399861/44069251-778312cc-9fb0-11e8-8cf1-aa2e5f6b79d3.png)
**Oracle**:
![image](https://user-images.githubuserconten
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Github user squito commented on the issue:
https://github.com/apache/spark/pull/22079
overall I'm in favor of backporting this, and it looks like the only
changes to the original were very small, so I'm in favor of this.
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Github user squito commented on the issue:
https://github.com/apache/spark/pull/21698
I also think @tgravescs solution of using the HashPartitioner is an
acceptable one, though as you've noted it doesn't deal w/ skew (which may be a
lot of the existing use of `repartition()`). I thin
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/21451
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Github user AmplabJenkins commented on the issue:
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Github user AmplabJenkins commented on the issue:
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/21320
That's more work for @ajacques though on the other hand. Either way works
fine.
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Github user xuejianbest commented on the issue:
https://github.com/apache/spark/pull/22048
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