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[SPARK-8532] [SQL] In Python's DataFrameWriter, save/saveAsTable/json/parquet/jdbc always override mode

Browse source 
https://issues.apache.org/jira/browse/SPARK-8532

This PR has two changes. First, it fixes the bug that save actions (i.e. `save/saveAsTable/json/parquet/jdbc`) always override mode. Second, it adds input argument `partitionBy` to `save/saveAsTable/parquet`.

Author: Yin Huai <yhuai@databricks.com>

Closes #6937 from yhuai/SPARK-8532 and squashes the following commits:

f972d5d [Yin Huai] davies's comment.
d37abd2 [Yin Huai] style.
d21290a [Yin Huai] Python doc.
889eb25 [Yin Huai] Minor refactoring and add partitionBy to save, saveAsTable, and parquet.
7fbc24b [Yin Huai] Use None instead of "error" as the default value of mode since JVM-side already uses "error" as the default value.
d696dff [Yin Huai] Python style.
88eb6c4 [Yin Huai] If mode is "error", do not call mode method.
c40c461 [Yin Huai] Regression test.
This commit is contained in:
Yin Huai 2015-06-22 13:51:23 -07:00
parent da7bbb9435
commit 5ab9fcfb01
2 changed files with 51 additions and 11 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

30
python/pyspark/sql/readwriter.py
View file

@ -218,7 +218,10 @@ class DataFrameWriter(object):
>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self._jwrite = self._jwrite.mode(saveMode)
# At the JVM side, the default value of mode is already set to "error".
# So, if the given saveMode is None, we will not call JVM-side's mode method.
if saveMode is not None:
self._jwrite = self._jwrite.mode(saveMode)
return self
@since(1.4)
@ -253,11 +256,12 @@ class DataFrameWriter(object):
"""
if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
cols = cols[0]
self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
if len(cols) > 0:
self._jwrite = self._jwrite.partitionBy(_to_seq(self._sqlContext._sc, cols))
return self
@since(1.4)
def save(self, path=None, format=None, mode="error", **options):
def save(self, path=None, format=None, mode=None, partitionBy=(), **options):
"""Saves the contents of the :class:`DataFrame` to a data source.
The data source is specified by the ``format`` and a set of ``options``.
@ -272,11 +276,12 @@ class DataFrameWriter(object):
* ``overwrite``: Overwrite existing data.
* ``ignore``: Silently ignore this operation if data already exists.
* ``error`` (default case): Throw an exception if data already exists.
:param partitionBy: names of partitioning columns
:param options: all other string options
>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self.mode(mode).options(**options)
self.partitionBy(partitionBy).mode(mode).options(**options)
if format is not None:
self.format(format)
if path is None:
@ -296,7 +301,7 @@ class DataFrameWriter(object):
self._jwrite.mode("overwrite" if overwrite else "append").insertInto(tableName)
@since(1.4)
def saveAsTable(self, name, format=None, mode="error", **options):
def saveAsTable(self, name, format=None, mode=None, partitionBy=(), **options):
"""Saves the content of the :class:`DataFrame` as the specified table.
In the case the table already exists, behavior of this function depends on the
@ -312,15 +317,16 @@ class DataFrameWriter(object):
:param name: the table name
:param format: the format used to save
:param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
:param partitionBy: names of partitioning columns
:param options: all other string options
"""
self.mode(mode).options(**options)
self.partitionBy(partitionBy).mode(mode).options(**options)
if format is not None:
self.format(format)
self._jwrite.saveAsTable(name)
@since(1.4)
def json(self, path, mode="error"):
def json(self, path, mode=None):
"""Saves the content of the :class:`DataFrame` in JSON format at the specified path.
:param path: the path in any Hadoop supported file system
@ -333,10 +339,10 @@ class DataFrameWriter(object):
>>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self._jwrite.mode(mode).json(path)
self.mode(mode)._jwrite.json(path)
@since(1.4)
def parquet(self, path, mode="error"):
def parquet(self, path, mode=None, partitionBy=()):
"""Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
:param path: the path in any Hadoop supported file system
@ -346,13 +352,15 @@ class DataFrameWriter(object):
* ``overwrite``: Overwrite existing data.
* ``ignore``: Silently ignore this operation if data already exists.
* ``error`` (default case): Throw an exception if data already exists.
:param partitionBy: names of partitioning columns
>>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self._jwrite.mode(mode).parquet(path)
self.partitionBy(partitionBy).mode(mode)
self._jwrite.parquet(path)
@since(1.4)
def jdbc(self, url, table, mode="error", properties={}):
def jdbc(self, url, table, mode=None, properties={}):
"""Saves the content of the :class:`DataFrame` to a external database table via JDBC.
.. note:: Don't create too many partitions in parallel on a large cluster;\

32
python/pyspark/sql/tests.py
View file

@ -539,6 +539,38 @@ class SQLTests(ReusedPySparkTestCase):
shutil.rmtree(tmpPath)
def test_save_and_load_builder(self):
df = self.df
tmpPath = tempfile.mkdtemp()
shutil.rmtree(tmpPath)
df.write.json(tmpPath)
actual = self.sqlCtx.read.json(tmpPath)
self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
schema = StructType([StructField("value", StringType(), True)])
actual = self.sqlCtx.read.json(tmpPath, schema)
self.assertEqual(sorted(df.select("value").collect()), sorted(actual.collect()))
df.write.mode("overwrite").json(tmpPath)
actual = self.sqlCtx.read.json(tmpPath)
self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
df.write.mode("overwrite").options(noUse="this options will not be used in save.")\
.format("json").save(path=tmpPath)
actual =\
self.sqlCtx.read.format("json")\
.load(path=tmpPath, noUse="this options will not be used in load.")
self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default",
"org.apache.spark.sql.parquet")
self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
actual = self.sqlCtx.load(path=tmpPath)
self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
shutil.rmtree(tmpPath)
def test_help_command(self):
# Regression test for SPARK-5464
rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])