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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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31 changes: 29 additions & 2 deletions python/pyspark/sql/readwriter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -335,7 +335,8 @@ def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=Non
``inferSchema`` is enabled. To avoid going through the entire data once, disable
``inferSchema`` option or specify the schema explicitly using ``schema``.

:param path: string, or list of strings, for input path(s).
:param path: string, or list of strings, for input path(s),
or RDD of Strings storing CSV rows.
:param schema: an optional :class:`pyspark.sql.types.StructType` for the input schema
or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
:param sep: sets the single character as a separator for each field and value.
Expand Down Expand Up @@ -408,6 +409,10 @@ def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=Non
>>> df = spark.read.csv('python/test_support/sql/ages.csv')
>>> df.dtypes
[('_c0', 'string'), ('_c1', 'string')]
>>> rdd = sc.textFile('python/test_support/sql/ages.csv')
>>> df2 = spark.read.csv(rdd)
>>> df2.dtypes
[('_c0', 'string'), ('_c1', 'string')]
"""
self._set_opts(
schema=schema, sep=sep, encoding=encoding, quote=quote, escape=escape, comment=comment,
Expand All @@ -420,7 +425,29 @@ def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=Non
columnNameOfCorruptRecord=columnNameOfCorruptRecord, multiLine=multiLine)
if isinstance(path, basestring):
path = [path]
return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
if type(path) == list:
return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
elif isinstance(path, RDD):
def func(iterator):
for x in iterator:
if not isinstance(x, basestring):
x = unicode(x)
if isinstance(x, unicode):
x = x.encode("utf-8")
yield x
keyed = path.mapPartitions(func)
keyed._bypass_serializer = True
jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
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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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@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 •
edited
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ok, This way looks good. I'll try it. Thanks for your suggestion.

# see SPARK-22112
# There aren't any jvm api for creating a dataframe from rdd storing csv.
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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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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(
jrdd.rdd(),
self._spark._jvm.Encoders.STRING())
return self._df(self._jreader.csv(jdataset))
else:
raise TypeError("path can be only string, list or RDD")

@since(1.5)
def orc(self, path):
Expand Down