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--- Diff:
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--- Diff:
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---
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the
Github user HyukjinKwon commented on the issue:
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I think it was changed back in https://github.com/apache/spark/pull/7363. I
think that avoids precision loss from Python's `float` limitation:
```python
>>> decimal.Decimal(1 / 1e6)
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Can we add test code for `null` row in a column for each type?
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GitHub user cloud-fan opened a pull request:
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[SPARK-22047][TEST] ignore HiveExternalCatalogVersionsSuite
## What changes were proposed in this pull request?
As reported in https://issues.apache.org/jira/browse/SPARK-22047 ,
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merging to master/2.2
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--- Diff: python/pyspark/worker.py ---
@@ -71,7 +73,19 @@ def wrap_udf(f, return_type):
return lambda *a: f(*a)
-def
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@BryanCutler I'm ok to upgrade pyarrow to 0.7 except for the same concerns
as #18974.
I guess we need to discuss upgrade policy and strategy of pyarrow.
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--- Diff: python/pyspark/serializers.py ---
@@ -199,6 +211,46 @@ def __repr__(self):
return "ArrowSerializer"
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Github user WeichenXu123 commented on the issue:
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@viirya Oh, I am not saying the compatibility against old version scala
application. What I say is about new version `Bucketizer`, when spark user use
java language(not scala language), call
Github user xysun commented on the issue:
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@joseph-torres @brkyvz @lw-lin can you please take a look? (sorry for
uninvited mentions but i just took the latest commits on `FileStreamSource`)
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@cloud-fan Could you please review this again?
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Help to review the code, thanks.
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@viirya Scala `with trait` is a complex mechanism and `trait` isn't
equivalent to java's `interface`. Scala compiler will precompile and generate
many other codes, so java-side code cannot
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@loneknightpy did you open a new JIRA about this issue?
AFAIK, downloading resources to local disk is not supported for cluster
mode even from beginning, would you please elaborate the
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retest this please
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Github user fjh100456 commented on the issue:
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Thanks for your review. @gatorsmile
In the first question I mean that âparquet.compressionâ can be found in
the `table: Tabledesc` (maybe similar with `catalogtable`), and can also be
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@smurching Thanks! I will update later. And note that I will separate part
of this PR to a new PR (the separated part will be a bugfix for #16774 )
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gentle ping @logannc.
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private final long length;
Github user viirya commented on the issue:
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Sorry I have to reply on a phone, so I may not write codes smoothly.
What I mean it doesn't break binary compatibility, is the existing users
codes using Bucketizer don't need to
Github user jerryshao commented on the issue:
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LGTM, merging to master.
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Github user danielfx90 commented on the issue:
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Seems logical. Then, unless someone disagrees, feel free to close this PR
and we will create a new spark package with this feature in a new repository.
Thanks!
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Can one of the admins verify this patch?
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@ajbozarth Thank you for your comment on the previous PR. I have closed
that one. Apologies for the confusion caused in the previous PR!
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ping @jerryshao
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/19194
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Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18659#discussion_r139562988
--- Diff: python/pyspark/serializers.py ---
@@ -199,6 +211,46 @@ def __repr__(self):
return "ArrowSerializer"
+class
Github user BryanCutler commented on the issue:
https://github.com/apache/spark/pull/16774
@WeichenXu123 , it would be great if you could separate out the bugfix. I
looked in #19208 but couldn't find what you were referring to.
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Github user viirya commented on the issue:
https://github.com/apache/spark/pull/19229
@WeichenXu123 Thanks for verifying that.
Do you mean using ApproxQuantiles to compute mean and median? But I think
this change is not intended to improve this part.
---
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/19211
Merged build finished. Test PASSed.
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