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Posted to commits@spark.apache.org by rx...@apache.org on 2015/06/03 09:23:39 UTC
spark git commit: [SPARK-8060] Improve DataFrame Python test coverage
and documentation.
Repository: spark
Updated Branches:
refs/heads/master 452eb82dd -> ce320cb2d
[SPARK-8060] Improve DataFrame Python test coverage and documentation.
Author: Reynold Xin <rx...@databricks.com>
Closes #6601 from rxin/python-read-write-test-and-doc and squashes the following commits:
baa8ad5 [Reynold Xin] Code review feedback.
f081d47 [Reynold Xin] More documentation updates.
c9902fa [Reynold Xin] [SPARK-8060] Improve DataFrame Python reader/writer interface doc and testing.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/ce320cb2
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/ce320cb2
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/ce320cb2
Branch: refs/heads/master
Commit: ce320cb2dbf28825f80795ce569735888f98d6e8
Parents: 452eb82
Author: Reynold Xin <rx...@databricks.com>
Authored: Wed Jun 3 00:23:34 2015 -0700
Committer: Reynold Xin <rx...@databricks.com>
Committed: Wed Jun 3 00:23:34 2015 -0700
----------------------------------------------------------------------
.rat-excludes | 1 +
python/pyspark/sql/__init__.py | 13 +-
python/pyspark/sql/context.py | 89 ++++----
python/pyspark/sql/dataframe.py | 82 +++----
python/pyspark/sql/readwriter.py | 217 +++++++++----------
python/pyspark/sql/tests.py | 2 +
.../sql/parquet_partitioned/_SUCCESS | 0
.../sql/parquet_partitioned/_common_metadata | Bin 0 -> 210 bytes
.../sql/parquet_partitioned/_metadata | Bin 0 -> 743 bytes
.../month=9/day=1/.part-r-00008.gz.parquet.crc | Bin 0 -> 12 bytes
.../month=9/day=1/part-r-00008.gz.parquet | Bin 0 -> 322 bytes
.../day=25/.part-r-00002.gz.parquet.crc | Bin 0 -> 12 bytes
.../day=25/.part-r-00004.gz.parquet.crc | Bin 0 -> 12 bytes
.../month=10/day=25/part-r-00002.gz.parquet | Bin 0 -> 343 bytes
.../month=10/day=25/part-r-00004.gz.parquet | Bin 0 -> 343 bytes
.../day=26/.part-r-00005.gz.parquet.crc | Bin 0 -> 12 bytes
.../month=10/day=26/part-r-00005.gz.parquet | Bin 0 -> 333 bytes
.../month=9/day=1/.part-r-00007.gz.parquet.crc | Bin 0 -> 12 bytes
.../month=9/day=1/part-r-00007.gz.parquet | Bin 0 -> 343 bytes
python/test_support/sql/people.json | 3 +
20 files changed, 180 insertions(+), 227 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/ce320cb2/.rat-excludes
----------------------------------------------------------------------
diff --git a/.rat-excludes b/.rat-excludes
index c0f81b5..8f2722c 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -82,3 +82,4 @@ local-1426633911242/*
local-1430917381534/*
DESCRIPTION
NAMESPACE
+test_support/*
http://git-wip-us.apache.org/repos/asf/spark/blob/ce320cb2/python/pyspark/sql/__init__.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py
index 726d288..ad9c891 100644
--- a/python/pyspark/sql/__init__.py
+++ b/python/pyspark/sql/__init__.py
@@ -45,11 +45,20 @@ from __future__ import absolute_import
def since(version):
+ """
+ A decorator that annotates a function to append the version of Spark the function was added.
+ """
+ import re
+ indent_p = re.compile(r'\n( +)')
+
def deco(f):
- f.__doc__ = f.__doc__.rstrip() + "\n\n.. versionadded:: %s" % version
+ indents = indent_p.findall(f.__doc__)
+ indent = ' ' * (min(len(m) for m in indents) if indents else 0)
+ f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
return f
return deco
+
from pyspark.sql.types import Row
from pyspark.sql.context import SQLContext, HiveContext
from pyspark.sql.column import Column
@@ -58,7 +67,9 @@ from pyspark.sql.group import GroupedData
from pyspark.sql.readwriter import DataFrameReader, DataFrameWriter
from pyspark.sql.window import Window, WindowSpec
+
__all__ = [
'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row',
'DataFrameNaFunctions', 'DataFrameStatFunctions', 'Window', 'WindowSpec',
+ 'DataFrameReader', 'DataFrameWriter'
]
http://git-wip-us.apache.org/repos/asf/spark/blob/ce320cb2/python/pyspark/sql/context.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 22f6257..9fdf43c 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -124,7 +124,10 @@ class SQLContext(object):
@property
@since("1.3.1")
def udf(self):
- """Returns a :class:`UDFRegistration` for UDF registration."""
+ """Returns a :class:`UDFRegistration` for UDF registration.
+
+ :return: :class:`UDFRegistration`
+ """
return UDFRegistration(self)
@since(1.4)
@@ -138,7 +141,7 @@ class SQLContext(object):
:param end: the end value (exclusive)
:param step: the incremental step (default: 1)
:param numPartitions: the number of partitions of the DataFrame
- :return: A new DataFrame
+ :return: :class:`DataFrame`
>>> sqlContext.range(1, 7, 2).collect()
[Row(id=1), Row(id=3), Row(id=5)]
@@ -195,8 +198,8 @@ class SQLContext(object):
raise ValueError("The first row in RDD is empty, "
"can not infer schema")
if type(first) is dict:
- warnings.warn("Using RDD of dict to inferSchema is deprecated,"
- "please use pyspark.sql.Row instead")
+ warnings.warn("Using RDD of dict to inferSchema is deprecated. "
+ "Use pyspark.sql.Row instead")
if samplingRatio is None:
schema = _infer_schema(first)
@@ -219,7 +222,7 @@ class SQLContext(object):
"""
.. note:: Deprecated in 1.3, use :func:`createDataFrame` instead.
"""
- warnings.warn("inferSchema is deprecated, please use createDataFrame instead")
+ warnings.warn("inferSchema is deprecated, please use createDataFrame instead.")
if isinstance(rdd, DataFrame):
raise TypeError("Cannot apply schema to DataFrame")
@@ -262,6 +265,7 @@ class SQLContext(object):
:class:`list`, or :class:`pandas.DataFrame`.
:param schema: a :class:`StructType` or list of column names. default None.
:param samplingRatio: the sample ratio of rows used for inferring
+ :return: :class:`DataFrame`
>>> l = [('Alice', 1)]
>>> sqlContext.createDataFrame(l).collect()
@@ -359,18 +363,15 @@ class SQLContext(object):
else:
raise ValueError("Can only register DataFrame as table")
- @since(1.0)
def parquetFile(self, *paths):
"""Loads a Parquet file, returning the result as a :class:`DataFrame`.
- >>> import tempfile, shutil
- >>> parquetFile = tempfile.mkdtemp()
- >>> shutil.rmtree(parquetFile)
- >>> df.saveAsParquetFile(parquetFile)
- >>> df2 = sqlContext.parquetFile(parquetFile)
- >>> sorted(df.collect()) == sorted(df2.collect())
- True
+ .. note:: Deprecated in 1.4, use :func:`DataFrameReader.parquet` instead.
+
+ >>> sqlContext.parquetFile('python/test_support/sql/parquet_partitioned').dtypes
+ [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
"""
+ warnings.warn("parquetFile is deprecated. Use read.parquet() instead.")
gateway = self._sc._gateway
jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths))
for i in range(0, len(paths)):
@@ -378,39 +379,15 @@ class SQLContext(object):
jdf = self._ssql_ctx.parquetFile(jpaths)
return DataFrame(jdf, self)
- @since(1.0)
def jsonFile(self, path, schema=None, samplingRatio=1.0):
"""Loads a text file storing one JSON object per line as a :class:`DataFrame`.
- If the schema is provided, applies the given schema to this JSON dataset.
- Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema.
-
- >>> import tempfile, shutil
- >>> jsonFile = tempfile.mkdtemp()
- >>> shutil.rmtree(jsonFile)
- >>> with open(jsonFile, 'w') as f:
- ... f.writelines(jsonStrings)
- >>> df1 = sqlContext.jsonFile(jsonFile)
- >>> df1.printSchema()
- root
- |-- field1: long (nullable = true)
- |-- field2: string (nullable = true)
- |-- field3: struct (nullable = true)
- | |-- field4: long (nullable = true)
+ .. note:: Deprecated in 1.4, use :func:`DataFrameReader.json` instead.
- >>> from pyspark.sql.types import *
- >>> schema = StructType([
- ... StructField("field2", StringType()),
- ... StructField("field3",
- ... StructType([StructField("field5", ArrayType(IntegerType()))]))])
- >>> df2 = sqlContext.jsonFile(jsonFile, schema)
- >>> df2.printSchema()
- root
- |-- field2: string (nullable = true)
- |-- field3: struct (nullable = true)
- | |-- field5: array (nullable = true)
- | | |-- element: integer (containsNull = true)
+ >>> sqlContext.jsonFile('python/test_support/sql/people.json').dtypes
+ [('age', 'bigint'), ('name', 'string')]
"""
+ warnings.warn("jsonFile is deprecated. Use read.json() instead.")
if schema is None:
df = self._ssql_ctx.jsonFile(path, samplingRatio)
else:
@@ -462,21 +439,16 @@ class SQLContext(object):
df = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
return DataFrame(df, self)
- @since(1.3)
def load(self, path=None, source=None, schema=None, **options):
"""Returns the dataset in a data source as a :class:`DataFrame`.
- The data source is specified by the ``source`` and a set of ``options``.
- If ``source`` is not specified, the default data source configured by
- ``spark.sql.sources.default`` will be used.
-
- Optionally, a schema can be provided as the schema of the returned DataFrame.
+ .. note:: Deprecated in 1.4, use :func:`DataFrameReader.load` instead.
"""
+ warnings.warn("load is deprecated. Use read.load() instead.")
return self.read.load(path, source, schema, **options)
@since(1.3)
- def createExternalTable(self, tableName, path=None, source=None,
- schema=None, **options):
+ def createExternalTable(self, tableName, path=None, source=None, schema=None, **options):
"""Creates an external table based on the dataset in a data source.
It returns the DataFrame associated with the external table.
@@ -487,6 +459,8 @@ class SQLContext(object):
Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and
created external table.
+
+ :return: :class:`DataFrame`
"""
if path is not None:
options["path"] = path
@@ -508,6 +482,8 @@ class SQLContext(object):
def sql(self, sqlQuery):
"""Returns a :class:`DataFrame` representing the result of the given query.
+ :return: :class:`DataFrame`
+
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1")
>>> df2.collect()
@@ -519,6 +495,8 @@ class SQLContext(object):
def table(self, tableName):
"""Returns the specified table as a :class:`DataFrame`.
+ :return: :class:`DataFrame`
+
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> df2 = sqlContext.table("table1")
>>> sorted(df.collect()) == sorted(df2.collect())
@@ -536,6 +514,9 @@ class SQLContext(object):
The returned DataFrame has two columns: ``tableName`` and ``isTemporary``
(a column with :class:`BooleanType` indicating if a table is a temporary one or not).
+ :param dbName: string, name of the database to use.
+ :return: :class:`DataFrame`
+
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> df2 = sqlContext.tables()
>>> df2.filter("tableName = 'table1'").first()
@@ -550,7 +531,8 @@ class SQLContext(object):
def tableNames(self, dbName=None):
"""Returns a list of names of tables in the database ``dbName``.
- If ``dbName`` is not specified, the current database will be used.
+ :param dbName: string, name of the database to use. Default to the current database.
+ :return: list of table names, in string
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> "table1" in sqlContext.tableNames()
@@ -585,8 +567,7 @@ class SQLContext(object):
Returns a :class:`DataFrameReader` that can be used to read data
in as a :class:`DataFrame`.
- >>> sqlContext.read
- <pyspark.sql.readwriter.DataFrameReader object at ...>
+ :return: :class:`DataFrameReader`
"""
return DataFrameReader(self)
@@ -644,10 +625,14 @@ class UDFRegistration(object):
def _test():
+ import os
import doctest
from pyspark.context import SparkContext
from pyspark.sql import Row, SQLContext
import pyspark.sql.context
+
+ os.chdir(os.environ["SPARK_HOME"])
+
globs = pyspark.sql.context.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
http://git-wip-us.apache.org/repos/asf/spark/blob/ce320cb2/python/pyspark/sql/dataframe.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index a82b6b8..7673153 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -44,7 +44,7 @@ class DataFrame(object):
A :class:`DataFrame` is equivalent to a relational table in Spark SQL,
and can be created using various functions in :class:`SQLContext`::
- people = sqlContext.parquetFile("...")
+ people = sqlContext.read.parquet("...")
Once created, it can be manipulated using the various domain-specific-language
(DSL) functions defined in: :class:`DataFrame`, :class:`Column`.
@@ -56,8 +56,8 @@ class DataFrame(object):
A more concrete example::
# To create DataFrame using SQLContext
- people = sqlContext.parquetFile("...")
- department = sqlContext.parquetFile("...")
+ people = sqlContext.read.parquet("...")
+ department = sqlContext.read.parquet("...")
people.filter(people.age > 30).join(department, people.deptId == department.id)) \
.groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"})
@@ -120,21 +120,12 @@ class DataFrame(object):
rdd = self._jdf.toJSON()
return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))
- @since(1.3)
def saveAsParquetFile(self, path):
"""Saves the contents as a Parquet file, preserving the schema.
- Files that are written out using this method can be read back in as
- a :class:`DataFrame` using :func:`SQLContext.parquetFile`.
-
- >>> import tempfile, shutil
- >>> parquetFile = tempfile.mkdtemp()
- >>> shutil.rmtree(parquetFile)
- >>> df.saveAsParquetFile(parquetFile)
- >>> df2 = sqlContext.parquetFile(parquetFile)
- >>> sorted(df2.collect()) == sorted(df.collect())
- True
+ .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.parquet` instead.
"""
+ warnings.warn("saveAsParquetFile is deprecated. Use write.parquet() instead.")
self._jdf.saveAsParquetFile(path)
@since(1.3)
@@ -151,69 +142,45 @@ class DataFrame(object):
"""
self._jdf.registerTempTable(name)
- @since(1.3)
def registerAsTable(self, name):
- """DEPRECATED: use :func:`registerTempTable` instead"""
- warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning)
+ """
+ .. note:: Deprecated in 1.4, use :func:`registerTempTable` instead.
+ """
+ warnings.warn("Use registerTempTable instead of registerAsTable.")
self.registerTempTable(name)
- @since(1.3)
def insertInto(self, tableName, overwrite=False):
"""Inserts the contents of this :class:`DataFrame` into the specified table.
- Optionally overwriting any existing data.
+ .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.insertInto` instead.
"""
+ warnings.warn("insertInto is deprecated. Use write.insertInto() instead.")
self.write.insertInto(tableName, overwrite)
- @since(1.3)
def saveAsTable(self, tableName, source=None, mode="error", **options):
"""Saves the contents of this :class:`DataFrame` to a data source as a table.
- The data source is specified by the ``source`` and a set of ``options``.
- If ``source`` is not specified, the default data source configured by
- ``spark.sql.sources.default`` will be used.
-
- Additionally, mode is used to specify the behavior of the saveAsTable operation when
- table already exists in the data source. There are four modes:
-
- * `append`: Append contents of this :class:`DataFrame` to existing data.
- * `overwrite`: Overwrite existing data.
- * `error`: Throw an exception if data already exists.
- * `ignore`: Silently ignore this operation if data already exists.
+ .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.saveAsTable` instead.
"""
+ warnings.warn("insertInto is deprecated. Use write.saveAsTable() instead.")
self.write.saveAsTable(tableName, source, mode, **options)
@since(1.3)
def save(self, path=None, source=None, mode="error", **options):
"""Saves the contents of the :class:`DataFrame` to a data source.
- The data source is specified by the ``source`` and a set of ``options``.
- If ``source`` is not specified, the default data source configured by
- ``spark.sql.sources.default`` will be used.
-
- Additionally, mode is used to specify the behavior of the save operation when
- data already exists in the data source. There are four modes:
-
- * `append`: Append contents of this :class:`DataFrame` to existing data.
- * `overwrite`: Overwrite existing data.
- * `error`: Throw an exception if data already exists.
- * `ignore`: Silently ignore this operation if data already exists.
+ .. note:: Deprecated in 1.4, use :func:`DataFrameWriter.save` instead.
"""
+ warnings.warn("insertInto is deprecated. Use write.save() instead.")
return self.write.save(path, source, mode, **options)
@property
@since(1.4)
def write(self):
"""
- Interface for saving the content of the :class:`DataFrame` out
- into external storage.
-
- :return :class:`DataFrameWriter`
+ Interface for saving the content of the :class:`DataFrame` out into external storage.
- .. note:: Experimental
-
- >>> df.write
- <pyspark.sql.readwriter.DataFrameWriter object at ...>
+ :return: :class:`DataFrameWriter`
"""
return DataFrameWriter(self)
@@ -636,6 +603,9 @@ class DataFrame(object):
This include count, mean, stddev, min, and max. If no columns are
given, this function computes statistics for all numerical columns.
+ .. note:: This function is meant for exploratory data analysis, as we make no \
+ guarantee about the backward compatibility of the schema of the resulting DataFrame.
+
>>> df.describe().show()
+-------+---+
|summary|age|
@@ -653,9 +623,11 @@ class DataFrame(object):
@ignore_unicode_prefix
@since(1.3)
def head(self, n=None):
- """
- Returns the first ``n`` rows as a list of :class:`Row`,
- or the first :class:`Row` if ``n`` is ``None.``
+ """Returns the first ``n`` rows.
+
+ :param n: int, default 1. Number of rows to return.
+ :return: If n is greater than 1, return a list of :class:`Row`.
+ If n is 1, return a single Row.
>>> df.head()
Row(age=2, name=u'Alice')
@@ -1170,8 +1142,8 @@ class DataFrame(object):
"http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
:func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases.
- This function is meant for exploratory data analysis, as we make no guarantee about the
- backward compatibility of the schema of the resulting DataFrame.
+ .. note:: This function is meant for exploratory data analysis, as we make no \
+ guarantee about the backward compatibility of the schema of the resulting DataFrame.
:param cols: Names of the columns to calculate frequent items for as a list or tuple of
strings.
http://git-wip-us.apache.org/repos/asf/spark/blob/ce320cb2/python/pyspark/sql/readwriter.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index d17d874..f036644 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -45,18 +45,24 @@ class DataFrameReader(object):
@since(1.4)
def format(self, source):
- """
- Specifies the input data source format.
+ """Specifies the input data source format.
+
+ :param source: string, name of the data source, e.g. 'json', 'parquet'.
+
+ >>> df = sqlContext.read.format('json').load('python/test_support/sql/people.json')
+ >>> df.dtypes
+ [('age', 'bigint'), ('name', 'string')]
+
"""
self._jreader = self._jreader.format(source)
return self
@since(1.4)
def schema(self, schema):
- """
- Specifies the input schema. Some data sources (e.g. JSON) can
- infer the input schema automatically from data. By specifying
- the schema here, the underlying data source can skip the schema
+ """Specifies the input schema.
+
+ Some data sources (e.g. JSON) can infer the input schema automatically from data.
+ By specifying the schema here, the underlying data source can skip the schema
inference step, and thus speed up data loading.
:param schema: a StructType object
@@ -69,8 +75,7 @@ class DataFrameReader(object):
@since(1.4)
def options(self, **options):
- """
- Adds input options for the underlying data source.
+ """Adds input options for the underlying data source.
"""
for k in options:
self._jreader = self._jreader.option(k, options[k])
@@ -84,6 +89,10 @@ class DataFrameReader(object):
:param format: optional string for format of the data source. Default to 'parquet'.
:param schema: optional :class:`StructType` for the input schema.
:param options: all other string options
+
+ >>> df = sqlContext.read.load('python/test_support/sql/parquet_partitioned')
+ >>> df.dtypes
+ [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
"""
if format is not None:
self.format(format)
@@ -107,31 +116,10 @@ class DataFrameReader(object):
:param path: string, path to the JSON dataset.
:param schema: an optional :class:`StructType` for the input schema.
- >>> import tempfile, shutil
- >>> jsonFile = tempfile.mkdtemp()
- >>> shutil.rmtree(jsonFile)
- >>> with open(jsonFile, 'w') as f:
- ... f.writelines(jsonStrings)
- >>> df1 = sqlContext.read.json(jsonFile)
- >>> df1.printSchema()
- root
- |-- field1: long (nullable = true)
- |-- field2: string (nullable = true)
- |-- field3: struct (nullable = true)
- | |-- field4: long (nullable = true)
-
- >>> from pyspark.sql.types import *
- >>> schema = StructType([
- ... StructField("field2", StringType()),
- ... StructField("field3",
- ... StructType([StructField("field5", ArrayType(IntegerType()))]))])
- >>> df2 = sqlContext.read.json(jsonFile, schema)
- >>> df2.printSchema()
- root
- |-- field2: string (nullable = true)
- |-- field3: struct (nullable = true)
- | |-- field5: array (nullable = true)
- | | |-- element: integer (containsNull = true)
+ >>> df = sqlContext.read.json('python/test_support/sql/people.json')
+ >>> df.dtypes
+ [('age', 'bigint'), ('name', 'string')]
+
"""
if schema is not None:
self.schema(schema)
@@ -141,10 +129,12 @@ class DataFrameReader(object):
def table(self, tableName):
"""Returns the specified table as a :class:`DataFrame`.
- >>> sqlContext.registerDataFrameAsTable(df, "table1")
- >>> df2 = sqlContext.read.table("table1")
- >>> sorted(df.collect()) == sorted(df2.collect())
- True
+ :param tableName: string, name of the table.
+
+ >>> df = sqlContext.read.parquet('python/test_support/sql/parquet_partitioned')
+ >>> df.registerTempTable('tmpTable')
+ >>> sqlContext.read.table('tmpTable').dtypes
+ [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
"""
return self._df(self._jreader.table(tableName))
@@ -152,13 +142,9 @@ class DataFrameReader(object):
def parquet(self, *path):
"""Loads a Parquet file, returning the result as a :class:`DataFrame`.
- >>> import tempfile, shutil
- >>> parquetFile = tempfile.mkdtemp()
- >>> shutil.rmtree(parquetFile)
- >>> df.saveAsParquetFile(parquetFile)
- >>> df2 = sqlContext.read.parquet(parquetFile)
- >>> sorted(df.collect()) == sorted(df2.collect())
- True
+ >>> df = sqlContext.read.parquet('python/test_support/sql/parquet_partitioned')
+ >>> df.dtypes
+ [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
"""
return self._df(self._jreader.parquet(_to_seq(self._sqlContext._sc, path)))
@@ -221,30 +207,34 @@ class DataFrameWriter(object):
@since(1.4)
def mode(self, saveMode):
- """
- Specifies the behavior when data or table already exists. Options include:
+ """Specifies the behavior when data or table already exists.
+
+ Options include:
* `append`: Append contents of this :class:`DataFrame` to existing data.
* `overwrite`: Overwrite existing data.
* `error`: Throw an exception if data already exists.
* `ignore`: Silently ignore this operation if data already exists.
+
+ >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self._jwrite = self._jwrite.mode(saveMode)
return self
@since(1.4)
def format(self, source):
- """
- Specifies the underlying output data source. Built-in options include
- "parquet", "json", etc.
+ """Specifies the underlying output data source.
+
+ :param source: string, name of the data source, e.g. 'json', 'parquet'.
+
+ >>> df.write.format('json').save(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self._jwrite = self._jwrite.format(source)
return self
@since(1.4)
def options(self, **options):
- """
- Adds output options for the underlying data source.
+ """Adds output options for the underlying data source.
"""
for k in options:
self._jwrite = self._jwrite.option(k, options[k])
@@ -252,12 +242,14 @@ class DataFrameWriter(object):
@since(1.4)
def partitionBy(self, *cols):
- """
- Partitions the output by the given columns on the file system.
+ """Partitions the output by the given columns on the file system.
+
If specified, the output is laid out on the file system similar
to Hive's partitioning scheme.
:param cols: name of columns
+
+ >>> df.write.partitionBy('year', 'month').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
cols = cols[0]
@@ -266,25 +258,23 @@ class DataFrameWriter(object):
@since(1.4)
def save(self, path=None, format=None, mode="error", **options):
- """
- Saves the contents of the :class:`DataFrame` to a data source.
+ """Saves the contents of the :class:`DataFrame` to a data source.
The data source is specified by the ``format`` and a set of ``options``.
If ``format`` is not specified, the default data source configured by
``spark.sql.sources.default`` will be used.
- Additionally, mode is used to specify the behavior of the save operation when
- data already exists in the data source. There are four modes:
-
- * `append`: Append contents of this :class:`DataFrame` to existing data.
- * `overwrite`: Overwrite existing data.
- * `error`: Throw an exception if data already exists.
- * `ignore`: Silently ignore this operation if data already exists.
-
:param path: the path in a Hadoop supported file system
:param format: the format used to save
- :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
+ :param mode: specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` (default case): Throw an exception if data already exists.
:param options: all other string options
+
+ >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self.mode(mode).options(**options)
if format is not None:
@@ -296,8 +286,8 @@ class DataFrameWriter(object):
@since(1.4)
def insertInto(self, tableName, overwrite=False):
- """
- Inserts the content of the :class:`DataFrame` to the specified table.
+ """Inserts the content of the :class:`DataFrame` to the specified table.
+
It requires that the schema of the class:`DataFrame` is the same as the
schema of the table.
@@ -307,8 +297,7 @@ class DataFrameWriter(object):
@since(1.4)
def saveAsTable(self, name, format=None, mode="error", **options):
- """
- Saves the content of the :class:`DataFrame` as the specified table.
+ """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
save mode, specified by the `mode` function (default to throwing an exception).
@@ -328,67 +317,58 @@ class DataFrameWriter(object):
self.mode(mode).options(**options)
if format is not None:
self.format(format)
- return self._jwrite.saveAsTable(name)
+ self._jwrite.saveAsTable(name)
@since(1.4)
def json(self, path, mode="error"):
- """
- Saves the content of the :class:`DataFrame` in JSON format at the
- specified path.
+ """Saves the content of the :class:`DataFrame` in JSON format at the specified path.
- Additionally, mode is used to specify the behavior of the save operation when
- data already exists in the data source. There are four modes:
+ :param path: the path in any Hadoop supported file system
+ :param mode: specifies the behavior of the save operation when data already exists.
- * `append`: Append contents of this :class:`DataFrame` to existing data.
- * `overwrite`: Overwrite existing data.
- * `error`: Throw an exception if data already exists.
- * `ignore`: Silently ignore this operation if data already exists.
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` (default case): Throw an exception if data already exists.
- :param path: the path in any Hadoop supported file system
- :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
+ >>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))
"""
- return self._jwrite.mode(mode).json(path)
+ self._jwrite.mode(mode).json(path)
@since(1.4)
def parquet(self, path, mode="error"):
- """
- Saves the content of the :class:`DataFrame` in Parquet format at the
- specified path.
+ """Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
- Additionally, mode is used to specify the behavior of the save operation when
- data already exists in the data source. There are four modes:
+ :param path: the path in any Hadoop supported file system
+ :param mode: specifies the behavior of the save operation when data already exists.
- * `append`: Append contents of this :class:`DataFrame` to existing data.
- * `overwrite`: Overwrite existing data.
- * `error`: Throw an exception if data already exists.
- * `ignore`: Silently ignore this operation if data already exists.
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` (default case): Throw an exception if data already exists.
- :param path: the path in any Hadoop supported file system
- :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
+ >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
"""
- return self._jwrite.mode(mode).parquet(path)
+ self._jwrite.mode(mode).parquet(path)
@since(1.4)
def jdbc(self, url, table, mode="error", properties={}):
- """
- Saves the content of the :class:`DataFrame` to a external database table
- via JDBC.
-
- In the case the table already exists in the external database,
- behavior of this function depends on the save mode, specified by the `mode`
- function (default to throwing an exception). There are four modes:
+ """Saves the content of the :class:`DataFrame` to a external database table via JDBC.
- * `append`: Append contents of this :class:`DataFrame` to existing data.
- * `overwrite`: Overwrite existing data.
- * `error`: Throw an exception if data already exists.
- * `ignore`: Silently ignore this operation if data already exists.
+ .. note:: Don't create too many partitions in parallel on a large cluster;\
+ otherwise Spark might crash your external database systems.
- :param url: a JDBC URL of the form `jdbc:subprotocol:subname`
+ :param url: a JDBC URL of the form ``jdbc:subprotocol:subname``
:param table: Name of the table in the external database.
- :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
+ :param mode: specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` (default case): Throw an exception if data already exists.
:param properties: JDBC database connection arguments, a list of
- arbitrary string tag/value. Normally at least a
- "user" and "password" property should be included.
+ arbitrary string tag/value. Normally at least a
+ "user" and "password" property should be included.
"""
jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
for k in properties:
@@ -398,24 +378,23 @@ class DataFrameWriter(object):
def _test():
import doctest
+ import os
+ import tempfile
from pyspark.context import SparkContext
from pyspark.sql import Row, SQLContext
import pyspark.sql.readwriter
+
+ os.chdir(os.environ["SPARK_HOME"])
+
globs = pyspark.sql.readwriter.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
+
+ globs['tempfile'] = tempfile
+ globs['os'] = os
globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
- globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
- .toDF(StructType([StructField('age', IntegerType()),
- StructField('name', StringType())]))
- jsonStrings = [
- '{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
- '{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},'
- '"field6":[{"field7": "row2"}]}',
- '{"field1" : null, "field2": "row3", '
- '"field3":{"field4":33, "field5": []}}'
- ]
- globs['jsonStrings'] = jsonStrings
+ globs['df'] = globs['sqlContext'].read.parquet('python/test_support/sql/parquet_partitioned')
+
(failure_count, test_count) = doctest.testmod(
pyspark.sql.readwriter, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
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diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 76384d3..6e498f0 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -753,8 +753,10 @@ class HiveContextSQLTests(ReusedPySparkTestCase):
try:
cls.sc._jvm.org.apache.hadoop.hive.conf.HiveConf()
except py4j.protocol.Py4JError:
+ cls.tearDownClass()
raise unittest.SkipTest("Hive is not available")
except TypeError:
+ cls.tearDownClass()
raise unittest.SkipTest("Hive is not available")
os.unlink(cls.tempdir.name)
_scala_HiveContext =\
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diff --git a/python/test_support/sql/people.json b/python/test_support/sql/people.json
new file mode 100644
index 0000000..50a859c
--- /dev/null
+++ b/python/test_support/sql/people.json
@@ -0,0 +1,3 @@
+{"name":"Michael"}
+{"name":"Andy", "age":30}
+{"name":"Justin", "age":19}
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