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[SPARK-22112][PYSPARK] Supports RDD of strings as input in spark.read.csv in PySpark #19339

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[SPARK-22112][PYSPARK] Supports RDD of strings as input in spark.read.csv in PySpark #19339

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d557892
add csv from `RDD[String]` API and related test case
goldmedal Sep 25, 2017
baaa93f
fix test case
goldmedal Sep 25, 2017
d4ef30a
finish pyspark dataframe from rdd of csv string
goldmedal Sep 25, 2017
9bd4eed
modified comments
goldmedal Sep 25, 2017
7525b48
modified comments
goldmedal Sep 25, 2017
350a93d
remove scala-side api
goldmedal Sep 26, 2017
4040103
use java dataset to wrap rdd api
goldmedal Sep 26, 2017
f542967
simplify the code and add comments
goldmedal Sep 26, 2017
5988336
fix some comment
goldmedal Sep 26, 2017
032b0c8
fix some comment
goldmedal Sep 26, 2017
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simplify the code and add comments
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@goldmedal
goldmedal committed Sep 26, 2017
commit f5429677ac66ded20b63dc9641ff69193c08897f
6 changes: 5 additions & 1 deletion python/pyspark/sql/readwriter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -438,9 +438,13 @@ def func(iterator):
keyed = path.mapPartitions(func)
keyed._bypass_serializer = True
jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
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@HyukjinKwon HyukjinKwon Sep 26, 2017

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I tried a way within Python and this seems working:

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 1ed452d895b..0f54065b3ee 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -438,7 +438,10 @@ class DataFrameReader(OptionUtils):
             keyed = path.mapPartitions(func)
             keyed._bypass_serializer = True
             jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
-            return self._df(self._jreader.csv(jrdd))
+            jdataset = self._spark._jsqlContext.createDataset(
+                jrdd.rdd(),
+                self._spark._sc._jvm.Encoders.STRING())
+            return self._df(self._jreader.csv(jdataset))
         else:
             raise TypeError("path can be only string, list or RDD")

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@HyukjinKwon HyukjinKwon Sep 26, 2017

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@goldmedal, it'd be great if you could double check whether this really works and it can be shorten or cleaner. This was just my rough try only to reach the goal so I am not sure if it is the best way.

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@goldmedal goldmedal Sep 26, 2017 •
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ok, This way looks good. I'll try it. Thanks for your suggestion.

# [SPARK-22112]
# There aren't any jvm api for creating a dataframe from rdd storing csv.
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@HyukjinKwon HyukjinKwon Sep 26, 2017

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just personal preference: SPARK-22112: ... or see SPARK-22112 if you wouldn't mind ..

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@goldmedal goldmedal Sep 26, 2017

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ok let me fix it. thanks :)

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@viirya viirya Sep 26, 2017

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Yeah, the usual style.

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@HyukjinKwon HyukjinKwon Sep 27, 2017

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Let's fix these comments like,

SPARK-22112: There aren't any jvm api for creating a dataframe from rdd storing csv.
...

or

There aren't any jvm api ...
...
for creating a dataframe from dataset storing csv. See SPARK-22112.

when we happened to fix some code around here or review other PRs fixing some codes around here in the future.

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@goldmedal goldmedal Sep 27, 2017

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Ok thanks

# We can do it through creating a jvm dataset firstly and using the jvm api
# for creating a dataframe from dataset storing csv.
jdataset = self._spark._ssql_ctx.createDataset(
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@HyukjinKwon HyukjinKwon Sep 26, 2017

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Let's add a small comment here to explain why we should create the dataset (which could look a bit weird in PySpark I believe).

jrdd.rdd(),
self._spark._sc._jvm.Encoders.STRING())
self._spark._jvm.Encoders.STRING())
return self._df(self._jreader.csv(jdataset))
else:
raise TypeError("path can be only string, list or RDD")
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