[SPARK-8060] Improve DataFrame Python reader/writer interface doc and…

… testing.

python/pyspark/sql/__init__.py

@@ -45,11 +45,20 @@
 
 
 def since(version):
+    """
+    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
@@ -58,6 +67,7 @@ def deco(f):
 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',

python/pyspark/sql/dataframe.py

@@ -636,6 +636,9 @@ def describe(self, *cols):
         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 +656,11 @@ def describe(self, *cols):
     @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.
+
+        If n is greater than 1, return a list of :class:`Row`. If n is 1, return a single Row.
+
+        :param n: int, default 1.
 
         >>> df.head()
         Row(age=2, name=u'Alice')
@@ -1170,8 +1175,8 @@ def freqItems(self, cols, support=None):
         "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.

python/pyspark/sql/readwriter.py

@@ -45,18 +45,24 @@ def _df(self, jdf):
 
     @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 @@ def schema(self, schema):
 
     @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 @@ def load(self, path=None, format=None, schema=None, **options):
         :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 @@ def json(self, path, schema=None):
         :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)
@@ -141,24 +129,22 @@ def json(self, path, schema=None):
     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))
 
     @since(1.4)
     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)))
 
@@ -221,43 +207,49 @@ def __init__(self, df):
 
     @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])
         return self
 
     @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]
@@ -266,8 +258,7 @@ def partitionBy(self, *cols):
 
     @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
@@ -285,6 +276,8 @@ def save(self, path=None, format=None, mode="error", **options):
         :param format: the format used to save
         :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
         :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 +289,8 @@ def save(self, path=None, format=None, mode="error", **options):
 
     @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 +300,7 @@ def insertInto(self, tableName, overwrite=False):
 
     @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).
@@ -328,13 +320,11 @@ def saveAsTable(self, name, format=None, mode="error", **options):
         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:
@@ -346,14 +336,14 @@ def json(self, path, mode="error"):
 
         :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:
@@ -365,14 +355,14 @@ def parquet(self, path, mode="error"):
 
         :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.
+        """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`
@@ -383,12 +373,15 @@ def jdbc(self, url, table, mode="error", properties={}):
         * `error`: Throw an exception if data already exists.
         * `ignore`: Silently ignore this operation if data already exists.
 
-        :param url: a JDBC URL of the form `jdbc:subprotocol:subname`
+        .. warning:: 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 table: Name of the table in the external database.
-        :param mode: one of `append`, `overwrite`, `error`, `ignore` (default: error)
+        :param mode: one of ``append``, ``overwrite``, ``error``, ``ignore`` (default: ``error``)
         :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 +391,23 @@ def jdbc(self, url, table, mode="error", properties={}):
 
 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)