From 1fdfe69352e4d4714c1f06d61d7ad475ce3a7f1f Mon Sep 17 00:00:00 2001
From: goldmedal <liugs963@gmail.com>
Date: Wed, 27 Sep 2017 11:19:45 +0900
Subject: [PATCH] [SPARK-22112][PYSPARK] Supports RDD of strings as input in
spark.read.csv in PySpark
## What changes were proposed in this pull request?
We added a method to the scala API for creating a `DataFrame` from `DataSet[String]` storing CSV in [SPARK-15463](https://issues.apache.org/jira/browse/SPARK-15463) but PySpark doesn't have `Dataset` to support this feature. Therfore, I add an API to create a `DataFrame` from `RDD[String]` storing csv and it's also consistent with PySpark's `spark.read.json`.
For example as below
```
>>> rdd = sc.textFile('python/test_support/sql/ages.csv')
>>> df2 = spark.read.csv(rdd)
>>> df2.dtypes
[('_c0', 'string'), ('_c1', 'string')]
```
## How was this patch tested?
add unit test cases.
Author: goldmedal <liugs963@gmail.com>
Closes #19339 from goldmedal/SPARK-22112.
---
python/pyspark/sql/readwriter.py | 31 +++++++++++++++++++++++++++++--
1 file changed, 29 insertions(+), 2 deletions(-)
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index cb847a0420..f3092918ab 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -335,7 +335,8 @@ class DataFrameReader(OptionUtils):
``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.
@@ -408,6 +409,10 @@ class DataFrameReader(OptionUtils):
>>> 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,
@@ -420,7 +425,29 @@ class DataFrameReader(OptionUtils):
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())
+ # see SPARK-22112
+ # There aren't any jvm api for creating a dataframe from rdd storing csv.
+ # 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):
--
GitLab