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--- Diff: python/pyspark/sql/readwriter.py ---
@@ -147,8 +147,8 @@ def load(self, path=None, format=None, schema=None,
**options):
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GitHub user jose-torres opened a pull request:
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[SPARK-23559][SS] Add epoch ID to DataWriterFactory.
## What changes were proposed in this pull request?
Add an epoch ID argument to DataWriterFactory for use in streaming. As
detailed
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@tdas @rdblue @cloud-fan
I haven't forgotten that we need a design doc before finalization;
SPARK-23556 tracks that.
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LGTM.
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// These are only updated by readExte
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--- Diff: python/pyspark/heapq3.py ---
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-impo
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--- Diff: python/pyspark/ml/stat.py ---
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# limitations under the License.
#
+import sys
--- End diff --
Github user rdblue commented on the issue:
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My intent is just to advocate for clear feedback on the content of PRs.
Good to hear your confidence in @mgaido91, and if he wants to work on a better
explain, that's great too.
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--- Diff: python/pyspark/mllib/random.py ---
@@ -19,6 +19,8 @@
Python package for random data generation.
"""
+import sys
+
-
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Github user HyukjinKwon commented on the issue:
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I think `dev/run_tests` doesn't actually test most of the changes (checking
doctest failure) here because it needs a test failure. Can you check one of
them with a manual test failure for sure?
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I'm almost sure there's a bug somewhere for that flaky test, but if there
isn't I'll file one.
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retest this please
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Github user vanzin commented on the issue:
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Hmm, I guess it was just luck that this didn't trigger the deps check,
since that jar is checked for a specific version (2.7.3 in the case of
hadoop2.7).
LGTM, merging to master / 2.3.
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Thank you for review and merging, @vanzin .
We generated both `spark-deps-hadoop-2.6` and `spark-deps-hadoop-2.7` with
the following.
```
./dev/test-dependencies.sh --replace-m
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@marmbrus can we target it for 2.4 ? need help on reviews. Been in waiting
state for very long
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we have back-ported it to 2.2, on production by an average it has saved us
at least 2x time.
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Yeah, I'm just wondering why that didn't happen in the dependency:tree
output in your description. Anyway, not really important to figure that out.
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retest this please
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Github user cloud-fan commented on the issue:
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My 2 cents: a data source may have a lot of columns, and a column may be a
complex struct type. So embedding the type in the plan may make the plan
unreadable.
If I'm debugging and see a
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BTW I think we do need a story to improve the explain result. The current
explain works pretty well with simple queries. But for complex queries, that a
single plan node across many lines, it's re
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sparkR.session(master = spark
GitHub user felixcheung opened a pull request:
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[SPARKR][DOC] fix link in vignettes
## What changes were proposed in this pull request?
Fix doc link that was changed in 2.3
@shivaram
You can merge this pull request into a
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