Github user dongjoon-hyun commented on the issue:
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@gatorsmile and @cloud-fan .
For ORC compatibility, I checked the ORC code, but it's not clearly tested.
I'll try to add some suite as a separate issue.
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -2661,7 +2657,12 @@ class Dataset[T] private[sql](
*/
def
Github user kiszk commented on the issue:
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Good catch, LGTM.
Here is my observation. This problem occurs since (this
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@Whoosh can you please check if there is a similar problem that affects
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merged to master. thanks @vanzin
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Github user lokkju commented on the issue:
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So it turns out just copying the conversion code doesn't work, as seen in
spark-avro/#240 - and now I'm running into the same thing writing my own
datasource. As an datasource in the end requires
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--- Diff: python/pyspark/sql/types.py ---
@@ -1619,11 +1619,38 @@ def to_arrow_type(dt):
arrow_type = pa.decimal(dt.precision, dt.scale)
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import pandas as pd
if
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Github user WeichenXu123 commented on the issue:
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Yes, it changed the probability of samples indeed compared with current
code.
But according to the comments coming from @jkbradley in #18924 , "in order
to make **corpusSize**, batchSize,
Github user mgaido91 commented on a diff in the pull request:
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Github user akopich commented on the issue:
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And the empty docs were not explicitly filtered out. They've just been
ignored in `submitMiniBatch`.
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Github user akopich commented on the issue:
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I'm saying they are not the same, but for larger datasets this should not
matter.
There is a change in logic. The hack with
`val batchSize = (miniBatchFraction *
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I agree, they're the same. You said at
https://github.com/apache/spark/pull/19565#issuecomment-339638791 that they
weren't.
But if you're saying the code already filters out empty docs
Github user ArtRand commented on a diff in the pull request:
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Github user akopich commented on the issue:
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Consider the following scenario. Let `docs` be an RDD containing 1000 empty
documents and 1000 non-empty documents and let `miniBatchFraction = 0.05`.
Assume, we use
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LGTM
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@HyukjinKwon Thanks for reviewing.
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Thank you for review @falaki and @felixcheung.
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Merged to master, branch-2.2 and branch-2.1.
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -1753,6 +1753,27 @@ class Dataset[T] private[sql](
*
* Also as
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*
* Also as
Github user srowen commented on the issue:
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I assume not-selecting a record in a sample is cheaper than just about any
other operation, including filtering on a predicate. All else equal, I'd rather
sample, then evaluate a predicate on only
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*
* Also
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*
* Also
Github user akopich commented on the issue:
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@WeichenXu123, yes there indeed is a difference in logic. Eventually it
boils down to semantics of `miniBatchFraction`. If it is a fraction of
non-empty documents being sampled, the version with
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Github user jinxing64 commented on the issue:
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@DonnyZone
Thanks for taking a look.
I think not quite the same.
After https://github.com/apache/spark/pull/18270, all `grouping__id` are
transformed to be `GroupingID` , which makes
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Github user kiszk commented on the issue:
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@hvanhovell @tejasapatil would it be possible to review this? Or, do you
know who is appropriate for reviewing this?
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Github user thunterdb commented on the issue:
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@hhbyyh I recall now the reason for an extra `origin` field, which is to
get around the standard issue of many small image files in S3 or other
distributed file systems. It is standard to compact
Github user klion26 commented on the issue:
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received ClassNotFound error in Yarn-Cluster mode(spark 1.6.2)ï¼_doesn't
reproduce the problem_
The error message is such as below:
```
[2017-10-26 16:53:18,274] ERROR Error while
Github user srowen commented on the issue:
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Filtering after sampling makes more sense. Though sampling isn't
deterministic, it doesn't change the probability that any particular sample is
produced.
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Github user WeichenXu123 commented on the issue:
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@akopich IMO the filter won't cost too much, don't worry about the
performance. (Or you can make a test to make sure)
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Github user akopich commented on the issue:
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I am sure that caching may by avoided here. Hence, it should not be used.
@srowen, maybe I don't get something, but I'm afraid, that currently
lineage for a single mini-batch submission
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Github user srowen commented on the issue:
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Regarding caching: I think that can be ignored for purposes of this change.
All this does is add a filter, and it doesn't cause an RDD to computed more
than it was before.
The only question
Github user mgaido91 commented on a diff in the pull request:
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Github user DaimonPl commented on the issue:
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@mallman how about finalizing it as is? IMHO performance improvements are
worth more than (possibly) redundant workaround - it could be cleaned later
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Github user DonnyZone commented on the issue:
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Is it similar to the below issue?
https://github.com/apache/spark/pull/19178
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/image/ImageSchema.scala
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+/*
+ * Licensed to the Apache Software
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Github user DonnyZone commented on the issue:
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@gatorsmile @gatorsmile
There are still two issues need to be figured out.
(1)It will be complicated to determine whether a literal function should be
resolved as Expression or
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}
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LGTM, merging to master.
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https://github.com/apache/spark/pull/19559
**[Test build #83078 has
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/19559
retest this please
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Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/19529
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/19529
Thanks! Merged to master.
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