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@shaneknapp any idea why the PR builder is not testing this?
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err, ignore that
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package org.apache.spark.status
GitHub user tdas opened a pull request:
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[SPARK-22187][SS][REVERT] Revert change in state row format for
mapGroupsWithState
## What changes were proposed in this pull request?
#19416 changed the format in which rows were encoded in
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jenkins retest this please
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Github user foxish commented on the issue:
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I understand the concern here. I think we can guard against accidental use
in client mode to post a nicer error message. @liyinan926, how about testing if
`kubernetes.default.svc` is accessible?
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yay! LGTM
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Github user liyinan926 commented on the issue:
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There are two cases to consider here. 1) If the client that creates the
`SparkContext` is running in a Kubernetes pod, it should work the same way as
the driver pod of an application submitted
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cc @liufengdb
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> For example with 5 categories, we don't know [0.0, 0.0, 0.0, 0.0, 0.0]
means last category or invalid value.
For the semantics I described ("OPTION 1"), I think it is clear:
* Last
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Actually given that in our implementations the executors talk to the driver
through a Kubernetes headless service that is only created by the submission
client. I don't think any of the two
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thanks, merging to master!
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I'll make a call: Given that the SQL tests do not use clearActive, let's
not bother with it. If we see flakiness, then we can try adding clearActive as
a fix.
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ok to test
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I end up removing `UDFColumn` class and using the existing `Aggregate`
logical plan for pandas group_agg UDF. This reuses a lot of code being to
existing `Aggregate` and minimize the code
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All existing clusters work with client mode, so you're on your own here...
maybe setting an environment variable when starting the driver and checking
that it's set in your code. Or something.
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LGTM
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--- Diff: python/pyspark/sql/types.py ---
@@ -1658,13 +1657,13 @@ def from_arrow_type(at):
spark_type = FloatType()
elif at ==
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Github user vanzin commented on the issue:
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Yeah, I don't think this can be added before figuring out SQL tests.
```
$ grep 'POSSIBLE THREAD LEAK' unit-tests.log | wc -l
158
```
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And to @holdenk 's question. Pandas group_agg udf fundamentally uses
different physical plan than the existing java/scala udf and therefore it's
hard to combine them together. I don't know a
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LGTM
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Thanks! Merged to master.
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@vanzin Sorry I didn't see your question. We don't yet support client mode
and no one has ever tried a true client mode, i.e., the driver runs on the
client machine outside a Kubernetes cluster.
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I don't think anyone answered my previous question:
> One question though: what happens if someone does new
SparkContext(myConf) in their code with a > k8s URL? That's a valid way of
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It's more than a docs issue though, it should be enforced in the code, and
as far as I've seen the code path I mentioned does not generate an error (or at
least not an obvious one).
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https://github.com/apache/spark/pull/19872
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