Github user liu-zhaokun commented on the issue:
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@srowen
Where does the default value "2" of spark.executor.instances used?
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[WIP] OVR transform optimization
## What changes were proposed in this pull request?
optimize OVR transform
## How was this patch tested?
existing tests
You can merge this
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LGTM
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thanks, merging to master!
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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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Github user vanzin commented on the issue:
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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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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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Github user icexelloss commented on the issue:
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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
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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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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> 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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cc @liufengdb
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We do enforce that by:
```
case (KUBERNETES, CLIENT) =>
printErrorAndExit("Client mode is currently not supported for
Kubernetes.")
```
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Github user vanzin commented on the issue:
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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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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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@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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jenkins retest this please
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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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ok to test
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GitHub user joseph-torres opened a pull request:
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[SPARK-22733] Split StreamExecution into MicroBatchExecution and
StreamExecution.
## What changes were proposed in this pull request?
StreamExecution is now an abstract base class,
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Thanks! Merged to master.
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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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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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[SPARK-22732] Add Structured Streaming APIs to DataSourceV2
## What changes were proposed in this pull request?
This PR provides DataSourceV2 API support for structured streaming,
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LGTM pending tests
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/19924
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