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Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15601428
--- Diff: core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
---
@@ -65,20 +66,49 @@ private[python] object SerDeUtil extends Logging {
Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50656448
I think we should remove the `batchSerialized` arguments from PythonRDD's
`saveAs*` methods and add a `batchSerialized` field to PythonRDD's constructor,
since it's an
Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15601800
--- Diff: core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
---
@@ -65,20 +66,52 @@ private[python] object SerDeUtil extends Logging {
Github user kanzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15607176
--- Diff: core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
---
@@ -65,20 +66,52 @@ private[python] object SerDeUtil extends Logging {
Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50672937
Ah, I see. I don't mind deferring that refactoring to a later patch. I'll
create some PySpark refactoring JIRAs later.
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Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50673853
I've merged this. Thanks!
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Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50442216
Looks good to me as well. @JoshRosen any comments?
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Github user JoshRosen commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
---
@@ -65,20 +66,49 @@ private[python] object SerDeUtil extends Logging {
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Github user kanzhang commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
---
@@ -65,20 +66,49 @@ private[python] object SerDeUtil extends Logging {
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50545354
Now I have got the following error, since ```saveAsHadoopFile``` has 11
params. Can relax it a bit?
```
Scalastyle checks failed at following occurrences:
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Github user kanzhang commented on the pull request:
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Nevermind. I'm refactoring.
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Github user MLnick commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50313472
This looks good to me - I'm +1 to merge.
@JoshRosen @mateiz any further comments?
Thanks @kanzhang for all the work!
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50378599
For completeness, added one ```saveAsHadoopDataset()``` and one
```saveAsNewAPIHadoopDataset()``` test.
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Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50111889
@kanzhang it might mean that we're reusing the Bean object on the Java side
when we read from the InputFormat. Hadoop's RecordReaders actually reuse the
same object as
Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50111934
The same thing happens in normal Spark if you create a hadoopRDD or
sequenceFile with Writables inside it, and then call cache(). There will be
only one key element and
Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15387719
--- Diff: python/pyspark/rdd.py ---
@@ -964,6 +964,106 @@ def first(self):
return self.take(1)[0]
+def
Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50112244
BTW you can try WritableUtils.clone. At some point we tried cloning data by
default in hadoopRDD, or having a flag for it, and we gave up because it didn't
seem to work
Github user ash211 commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50112894
Matei, what InputFormats did you have problems with when cloning by
default? I'd love to figure out what it would take to solve the one
element/one value problem.
Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15388452
--- Diff: python/pyspark/rdd.py ---
@@ -964,6 +964,106 @@ def first(self):
return self.take(1)[0]
+def
Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50116221
@ash211 this was the JIRA:
https://issues.apache.org/jira/browse/SPARK-1018. I don't remember the
problematic types exactly, but one might've been Avro records, so try
Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15388927
--- Diff: python/pyspark/rdd.py ---
@@ -964,6 +964,106 @@ def first(self):
return self.take(1)[0]
+def
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50183228
it might mean that we're reusing the Bean object on the Java side when
we read from the InputFormat. Hadoop's RecordReaders actually reuse the same
object as you read
Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50186945
The batch size is a performance-tuning knob that controls the granularity
of data transfer between Python and Java. Larger batch sizes help to amortize
certain costs
Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50187471
One difference between this case and `saveAsPickleFile` is that here the
actual on-disk output is independent of the batch size, since the data gets
unbatched in Java,
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50197882
The latest update fixed JevaBean test failure. To use
```WritableUtils.clone```, I broadcasted Hadoop conf the second time. Is there
a way to share the one used by
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50202364
Just wanted to make sure my understanding is correct, calling
```RDD.first()``` doesn't materialize (read in) the full RDD, right? I saw a
```first``` being called
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Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50206155
If you call `first()` on an RDD, we only perform as much computation as
necessary to return the first result. If the RDD's lineage chain contains a
shuffle stage, we
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50207653
@JoshRosen hi, thanks for your detailed explanation. I'm not sure I get
your point or maybe I didn't make myself clear. I'm mainly concerned about the
case of reading
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50208305
@JoshRosen BTW, thanks for the detailed explanation on serialization!
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Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50208947
@kanzhang I think it's unsafe to unpersist the read job's Hadoop
configuration. Even if we cached the results of the read, I think we'd still
need to keep a copy of
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50210598
Even if we cached the results of the read, I think we'd still need to
keep a copy of the configuration in case any of those cached partitions are
lost and we need to
Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50211345
Whoops, I was thinking of `destroy` instead of `unpersist.` Since the
driver keeps a copy of the broadcast variable, it should always be safe to
unpersist.
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Github user kanzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15431198
--- Diff: python/pyspark/rdd.py ---
@@ -964,6 +964,106 @@ def first(self):
return self.take(1)[0]
+def
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50223574
Updated the patch to allow users to specify batch size when reading in a
sequence file. When batch size is 1, the pickled data is unbatched (and custom
Writables are
Github user MLnick commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50068945
Ok I've been through it in detail and everything looks good, with the
exception of one issue that I picked up. The saving currently inherently
assumes that the
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50078131
@MLnick Thanks, Nick! Oops, I totally forgot about re-serialization, which
I specifically dealt with when I worked on ```saveAsPickleFile```. Will update
now.
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50090301
Update addressing serialization issue raised by Nick, along with tests.
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50093417
There is one additional issue though. Currently, when reading the Python
rdd is not batch serialized. This will trigger re-serialization upon writing
the same rdd,
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Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15385187
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -92,6 +104,46 @@ private[python] class DefaultConverter extends
Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15386068
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -92,6 +104,46 @@ private[python] class DefaultConverter extends
Github user kanzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15386313
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -92,6 +104,46 @@ private[python] class DefaultConverter extends
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50108287
Uploaded a new patch adding batch serialization to Python when reading
sequence files. For some reason, the test on custom class (Java Bean) no longer
works and I
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-50109757
More on the JavaBean test failure, it seems for RDD[(key, Bean)], all the
keys in a partition (or batch, I didn't test partition size batch size) are
paired with the
Github user SparkQA commented on the pull request:
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Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49843828
The style check error is different, see
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/16994/consoleFull.
It's a bit hidden in there but it says:
Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15275379
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -31,13 +31,14 @@ import org.apache.spark.annotation.Experimental
Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15275404
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -31,13 +31,14 @@ import org.apache.spark.annotation.Experimental
Github user kanzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15296619
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -31,13 +31,14 @@ import org.apache.spark.annotation.Experimental
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49912846
The latest test failure in ```org.apache.spark.streaming.CheckpointSuite```
seems unrelated.
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Github user MLnick commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49708463
@kanzhang @mateiz Yeah this is one issue with Pyrolite vs MsgPack. MsgPack
supported case classes out the box, which would likely be a bit more common
that beans.
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49807264
@MLnick I merely removed it from programming guide. The functionality (and
your test) is still there should anyone wants to try it.
@mateiz I meant when
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49810291
Major changes for the updated patch.
1. Replaced doctests with standalone tests
2. Fixed converter for converting BytesWritables and added read/write tests
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49810818
@pwendell I renamed file HBaseConverter.scala to HBaseConverters.scala. Now
I failed Scala style checks. How can I fix it? Thx.
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Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49675615
Regarding the JavaBeans, is there a reason to believe Pyrolite won't
support them in the future? Or are you just suggesting to remove it because we
can't also save data?
Github user kanzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15150914
--- Diff: docs/programming-guide.md ---
@@ -403,31 +403,30 @@ PySpark SequenceFile support loads an RDD within
Java, and pickles the resulting
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49554126
@MLnick I'm thinking of removing the tests and programming guide entry for
custom classes (JavaBeans). It seems to be a feature of Pyrolite and I can't
think of any
Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15152359
--- Diff: docs/programming-guide.md ---
@@ -403,31 +403,30 @@ PySpark SequenceFile support loads an RDD within
Java, and pickles the resulting
Github user kanzhang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r15153275
--- Diff: docs/programming-guide.md ---
@@ -403,31 +403,30 @@ PySpark SequenceFile support loads an RDD within
Java, and pickles the resulting
Github user MLnick commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-49198901
Great - I will review in more detail after that. Would be great to get this
merged before 1.1 freeze so PySpark I/O for inputformat and outputformat is
in for the
Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48962322
@davies @mateiz @MLnick thanks for the review and suggestions. I'll try to
add standalone tests for every data type.
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Github user kanzhang commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48971710
@MLnick I'll see if I can add couple output converter examples as well. Thx.
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Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48818290
Jenkins, retest this please
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Github user SparkQA commented on the pull request:
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Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r14854296
--- Diff: docs/programming-guide.md ---
@@ -403,31 +403,30 @@ PySpark SequenceFile support loads an RDD within
Java, and pickles the resulting
Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1338#discussion_r14854311
--- Diff: python/pyspark/tests.py ---
@@ -273,6 +274,14 @@ def test_sequencefiles(self):
(3, {2.0: u'dd'})]
Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48818519
This looks awesome, thanks for putting it together! One comment I have
though is that we should add more test coverage, to make sure we cover all the
data types
Github user MLnick commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48820160
I have had a quick look over and will try to do a more detailed one this
weekend.
High level looks good, 2 comments so far:
1. Agree with Matei that I
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48820892
QA results for PR 1338:br- This patch PASSES unit tests.br- This patch
merges cleanlybr- This patch adds the following public classes
(experimental):br tddict of
Github user davies commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48783095
LGTM, awesome!
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48501125
Merged build triggered.
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48501142
Merged build started.
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/1338#issuecomment-48508588
Merged build finished.
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