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spark-instrumented-optimizer/python/pyspark/sql.py
Davies Liu afb131637d [SPARK-5678] Convert DataFrame to pandas.DataFrame and Series 
```
pyspark.sql.DataFrame.to_pandas = to_pandas(self) unbound pyspark.sql.DataFrame method
    Collect all the rows and return a `pandas.DataFrame`.

    >>> df.to_pandas()  # doctest: +SKIP
       age   name
    0    2  Alice
    1    5    Bob

pyspark.sql.Column.to_pandas = to_pandas(self) unbound pyspark.sql.Column method
    Return a pandas.Series from the column

    >>> df.age.to_pandas()  # doctest: +SKIP
    0    2
    1    5
    dtype: int64
```

Not tests by jenkins (they depends on pandas)

Author: Davies Liu <davies@databricks.com>

Closes #4476 from davies/to_pandas and squashes the following commits:

6276fb6 [Davies Liu] Convert DataFrame to pandas.DataFrame and Series
2015-02-09 11:42:52 -08:00

2737 lines
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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
public classes of Spark SQL:
- L{SQLContext}
Main entry point for SQL functionality.
- L{DataFrame}
A Resilient Distributed Dataset (RDD) with Schema information for the data contained. In
addition to normal RDD operations, DataFrames also support SQL.
- L{GroupedData}
- L{Column}
Column is a DataFrame with a single column.
- L{Row}
A Row of data returned by a Spark SQL query.
- L{HiveContext}
Main entry point for accessing data stored in Apache Hive..
"""
import sys
import itertools
import decimal
import datetime
import keyword
import warnings
import json
import re
import random
import os
from tempfile import NamedTemporaryFile
from array import array
from operator import itemgetter
from itertools import imap
from py4j.protocol import Py4JError
from py4j.java_collections import ListConverter, MapConverter
from pyspark.context import SparkContext
from pyspark.rdd import RDD, _prepare_for_python_RDD
from pyspark.serializers import BatchedSerializer, AutoBatchedSerializer, PickleSerializer, \
CloudPickleSerializer, UTF8Deserializer
from pyspark.storagelevel import StorageLevel
from pyspark.traceback_utils import SCCallSiteSync
__all__ = [
"StringType", "BinaryType", "BooleanType", "DateType", "TimestampType", "DecimalType",
"DoubleType", "FloatType", "ByteType", "IntegerType", "LongType",
"ShortType", "ArrayType", "MapType", "StructField", "StructType",
"SQLContext", "HiveContext", "DataFrame", "GroupedData", "Column", "Row", "Dsl",
"SchemaRDD"]
class DataType(object):
"""Spark SQL DataType"""
def __repr__(self):
return self.__class__.__name__
def __hash__(self):
return hash(str(self))
def __eq__(self, other):
return (isinstance(other, self.__class__) and
self.__dict__ == other.__dict__)
def __ne__(self, other):
return not self.__eq__(other)
@classmethod
def typeName(cls):
return cls.__name__[:-4].lower()
def jsonValue(self):
return self.typeName()
def json(self):
return json.dumps(self.jsonValue(),
separators=(',', ':'),
sort_keys=True)
class PrimitiveTypeSingleton(type):
"""Metaclass for PrimitiveType"""
_instances = {}
def __call__(cls):
if cls not in cls._instances:
cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__()
return cls._instances[cls]
class PrimitiveType(DataType):
"""Spark SQL PrimitiveType"""
__metaclass__ = PrimitiveTypeSingleton
def __eq__(self, other):
# because they should be the same object
return self is other
class NullType(PrimitiveType):
"""Spark SQL NullType
The data type representing None, used for the types which has not
been inferred.
"""
class StringType(PrimitiveType):
"""Spark SQL StringType
The data type representing string values.
"""
class BinaryType(PrimitiveType):
"""Spark SQL BinaryType
The data type representing bytearray values.
"""
class BooleanType(PrimitiveType):
"""Spark SQL BooleanType
The data type representing bool values.
"""
class DateType(PrimitiveType):
"""Spark SQL DateType
The data type representing datetime.date values.
"""
class TimestampType(PrimitiveType):
"""Spark SQL TimestampType
The data type representing datetime.datetime values.
"""
class DecimalType(DataType):
"""Spark SQL DecimalType
The data type representing decimal.Decimal values.
"""
def __init__(self, precision=None, scale=None):
self.precision = precision
self.scale = scale
self.hasPrecisionInfo = precision is not None
def jsonValue(self):
if self.hasPrecisionInfo:
return "decimal(%d,%d)" % (self.precision, self.scale)
else:
return "decimal"
def __repr__(self):
if self.hasPrecisionInfo:
return "DecimalType(%d,%d)" % (self.precision, self.scale)
else:
return "DecimalType()"
class DoubleType(PrimitiveType):
"""Spark SQL DoubleType
The data type representing float values.
"""
class FloatType(PrimitiveType):
"""Spark SQL FloatType
The data type representing single precision floating-point values.
"""
class ByteType(PrimitiveType):
"""Spark SQL ByteType
The data type representing int values with 1 singed byte.
"""
class IntegerType(PrimitiveType):
"""Spark SQL IntegerType
The data type representing int values.
"""
class LongType(PrimitiveType):
"""Spark SQL LongType
The data type representing long values. If the any value is
beyond the range of [-9223372036854775808, 9223372036854775807],
please use DecimalType.
"""
class ShortType(PrimitiveType):
"""Spark SQL ShortType
The data type representing int values with 2 signed bytes.
"""
class ArrayType(DataType):
"""Spark SQL ArrayType
The data type representing list values. An ArrayType object
comprises two fields, elementType (a DataType) and containsNull (a bool).
The field of elementType is used to specify the type of array elements.
The field of containsNull is used to specify if the array has None values.
"""
def __init__(self, elementType, containsNull=True):
"""Creates an ArrayType
:param elementType: the data type of elements.
:param containsNull: indicates whether the list contains None values.
>>> ArrayType(StringType) == ArrayType(StringType, True)
True
>>> ArrayType(StringType, False) == ArrayType(StringType)
False
"""
self.elementType = elementType
self.containsNull = containsNull
def __repr__(self):
return "ArrayType(%s,%s)" % (self.elementType,
str(self.containsNull).lower())
def jsonValue(self):
return {"type": self.typeName(),
"elementType": self.elementType.jsonValue(),
"containsNull": self.containsNull}
@classmethod
def fromJson(cls, json):
return ArrayType(_parse_datatype_json_value(json["elementType"]),
json["containsNull"])
class MapType(DataType):
"""Spark SQL MapType
The data type representing dict values. A MapType object comprises
three fields, keyType (a DataType), valueType (a DataType) and
valueContainsNull (a bool).
The field of keyType is used to specify the type of keys in the map.
The field of valueType is used to specify the type of values in the map.
The field of valueContainsNull is used to specify if values of this
map has None values.
For values of a MapType column, keys are not allowed to have None values.
"""
def __init__(self, keyType, valueType, valueContainsNull=True):
"""Creates a MapType
:param keyType: the data type of keys.
:param valueType: the data type of values.
:param valueContainsNull: indicates whether values contains
null values.
>>> (MapType(StringType, IntegerType)
... == MapType(StringType, IntegerType, True))
True
>>> (MapType(StringType, IntegerType, False)
... == MapType(StringType, FloatType))
False
"""
self.keyType = keyType
self.valueType = valueType
self.valueContainsNull = valueContainsNull
def __repr__(self):
return "MapType(%s,%s,%s)" % (self.keyType, self.valueType,
str(self.valueContainsNull).lower())
def jsonValue(self):
return {"type": self.typeName(),
"keyType": self.keyType.jsonValue(),
"valueType": self.valueType.jsonValue(),
"valueContainsNull": self.valueContainsNull}
@classmethod
def fromJson(cls, json):
return MapType(_parse_datatype_json_value(json["keyType"]),
_parse_datatype_json_value(json["valueType"]),
json["valueContainsNull"])
class StructField(DataType):
"""Spark SQL StructField
Represents a field in a StructType.
A StructField object comprises three fields, name (a string),
dataType (a DataType) and nullable (a bool). The field of name
is the name of a StructField. The field of dataType specifies
the data type of a StructField.
The field of nullable specifies if values of a StructField can
contain None values.
"""
def __init__(self, name, dataType, nullable=True, metadata=None):
"""Creates a StructField
:param name: the name of this field.
:param dataType: the data type of this field.
:param nullable: indicates whether values of this field
can be null.
:param metadata: metadata of this field, which is a map from string
to simple type that can be serialized to JSON
automatically
>>> (StructField("f1", StringType, True)
... == StructField("f1", StringType, True))
True
>>> (StructField("f1", StringType, True)
... == StructField("f2", StringType, True))
False
"""
self.name = name
self.dataType = dataType
self.nullable = nullable
self.metadata = metadata or {}
def __repr__(self):
return "StructField(%s,%s,%s)" % (self.name, self.dataType,
str(self.nullable).lower())
def jsonValue(self):
return {"name": self.name,
"type": self.dataType.jsonValue(),
"nullable": self.nullable,
"metadata": self.metadata}
@classmethod
def fromJson(cls, json):
return StructField(json["name"],
_parse_datatype_json_value(json["type"]),
json["nullable"],
json["metadata"])
class StructType(DataType):
"""Spark SQL StructType
The data type representing rows.
A StructType object comprises a list of L{StructField}.
"""
def __init__(self, fields):
"""Creates a StructType
>>> struct1 = StructType([StructField("f1", StringType, True)])
>>> struct2 = StructType([StructField("f1", StringType, True)])
>>> struct1 == struct2
True
>>> struct1 = StructType([StructField("f1", StringType, True)])
>>> struct2 = StructType([StructField("f1", StringType, True),
... [StructField("f2", IntegerType, False)]])
>>> struct1 == struct2
False
"""
self.fields = fields
def __repr__(self):
return ("StructType(List(%s))" %
",".join(str(field) for field in self.fields))
def jsonValue(self):
return {"type": self.typeName(),
"fields": [f.jsonValue() for f in self.fields]}
@classmethod
def fromJson(cls, json):
return StructType([StructField.fromJson(f) for f in json["fields"]])
class UserDefinedType(DataType):
"""
.. note:: WARN: Spark Internal Use Only
SQL User-Defined Type (UDT).
"""
@classmethod
def typeName(cls):
return cls.__name__.lower()
@classmethod
def sqlType(cls):
"""
Underlying SQL storage type for this UDT.
"""
raise NotImplementedError("UDT must implement sqlType().")
@classmethod
def module(cls):
"""
The Python module of the UDT.
"""
raise NotImplementedError("UDT must implement module().")
@classmethod
def scalaUDT(cls):
"""
The class name of the paired Scala UDT.
"""
raise NotImplementedError("UDT must have a paired Scala UDT.")
def serialize(self, obj):
"""
Converts the a user-type object into a SQL datum.
"""
raise NotImplementedError("UDT must implement serialize().")
def deserialize(self, datum):
"""
Converts a SQL datum into a user-type object.
"""
raise NotImplementedError("UDT must implement deserialize().")
def json(self):
return json.dumps(self.jsonValue(), separators=(',', ':'), sort_keys=True)
def jsonValue(self):
schema = {
"type": "udt",
"class": self.scalaUDT(),
"pyClass": "%s.%s" % (self.module(), type(self).__name__),
"sqlType": self.sqlType().jsonValue()
}
return schema
@classmethod
def fromJson(cls, json):
pyUDT = json["pyClass"]
split = pyUDT.rfind(".")
pyModule = pyUDT[:split]
pyClass = pyUDT[split+1:]
m = __import__(pyModule, globals(), locals(), [pyClass], -1)
UDT = getattr(m, pyClass)
return UDT()
def __eq__(self, other):
return type(self) == type(other)
_all_primitive_types = dict((v.typeName(), v)
for v in globals().itervalues()
if type(v) is PrimitiveTypeSingleton and
v.__base__ == PrimitiveType)
_all_complex_types = dict((v.typeName(), v)
for v in [ArrayType, MapType, StructType])
def _parse_datatype_json_string(json_string):
"""Parses the given data type JSON string.
>>> def check_datatype(datatype):
... scala_datatype = sqlCtx._ssql_ctx.parseDataType(datatype.json())
... python_datatype = _parse_datatype_json_string(scala_datatype.json())
... return datatype == python_datatype
>>> all(check_datatype(cls()) for cls in _all_primitive_types.values())
True
>>> # Simple ArrayType.
>>> simple_arraytype = ArrayType(StringType(), True)
>>> check_datatype(simple_arraytype)
True
>>> # Simple MapType.
>>> simple_maptype = MapType(StringType(), LongType())
>>> check_datatype(simple_maptype)
True
>>> # Simple StructType.
>>> simple_structtype = StructType([
... StructField("a", DecimalType(), False),
... StructField("b", BooleanType(), True),
... StructField("c", LongType(), True),
... StructField("d", BinaryType(), False)])
>>> check_datatype(simple_structtype)
True
>>> # Complex StructType.
>>> complex_structtype = StructType([
... StructField("simpleArray", simple_arraytype, True),
... StructField("simpleMap", simple_maptype, True),
... StructField("simpleStruct", simple_structtype, True),
... StructField("boolean", BooleanType(), False),
... StructField("withMeta", DoubleType(), False, {"name": "age"})])
>>> check_datatype(complex_structtype)
True
>>> # Complex ArrayType.
>>> complex_arraytype = ArrayType(complex_structtype, True)
>>> check_datatype(complex_arraytype)
True
>>> # Complex MapType.
>>> complex_maptype = MapType(complex_structtype,
... complex_arraytype, False)
>>> check_datatype(complex_maptype)
True
>>> check_datatype(ExamplePointUDT())
True
>>> structtype_with_udt = StructType([StructField("label", DoubleType(), False),
... StructField("point", ExamplePointUDT(), False)])
>>> check_datatype(structtype_with_udt)
True
"""
return _parse_datatype_json_value(json.loads(json_string))
_FIXED_DECIMAL = re.compile("decimal\\((\\d+),(\\d+)\\)")
def _parse_datatype_json_value(json_value):
if type(json_value) is unicode:
if json_value in _all_primitive_types.keys():
return _all_primitive_types[json_value]()
elif json_value == u'decimal':
return DecimalType()
elif _FIXED_DECIMAL.match(json_value):
m = _FIXED_DECIMAL.match(json_value)
return DecimalType(int(m.group(1)), int(m.group(2)))
else:
raise ValueError("Could not parse datatype: %s" % json_value)
else:
tpe = json_value["type"]
if tpe in _all_complex_types:
return _all_complex_types[tpe].fromJson(json_value)
elif tpe == 'udt':
return UserDefinedType.fromJson(json_value)
else:
raise ValueError("not supported type: %s" % tpe)
# Mapping Python types to Spark SQL DataType
_type_mappings = {
type(None): NullType,
bool: BooleanType,
int: IntegerType,
long: LongType,
float: DoubleType,
str: StringType,
unicode: StringType,
bytearray: BinaryType,
decimal.Decimal: DecimalType,
datetime.date: DateType,
datetime.datetime: TimestampType,
datetime.time: TimestampType,
}
def _infer_type(obj):
"""Infer the DataType from obj
>>> p = ExamplePoint(1.0, 2.0)
>>> _infer_type(p)
ExamplePointUDT
"""
if obj is None:
raise ValueError("Can not infer type for None")
if hasattr(obj, '__UDT__'):
return obj.__UDT__
dataType = _type_mappings.get(type(obj))
if dataType is not None:
return dataType()
if isinstance(obj, dict):
for key, value in obj.iteritems():
if key is not None and value is not None:
return MapType(_infer_type(key), _infer_type(value), True)
else:
return MapType(NullType(), NullType(), True)
elif isinstance(obj, (list, array)):
for v in obj:
if v is not None:
return ArrayType(_infer_type(obj[0]), True)
else:
return ArrayType(NullType(), True)
else:
try:
return _infer_schema(obj)
except ValueError:
raise ValueError("not supported type: %s" % type(obj))
def _infer_schema(row):
"""Infer the schema from dict/namedtuple/object"""
if isinstance(row, dict):
items = sorted(row.items())
elif isinstance(row, tuple):
if hasattr(row, "_fields"): # namedtuple
items = zip(row._fields, tuple(row))
elif hasattr(row, "__FIELDS__"): # Row
items = zip(row.__FIELDS__, tuple(row))
elif all(isinstance(x, tuple) and len(x) == 2 for x in row):
items = row
else:
raise ValueError("Can't infer schema from tuple")
elif hasattr(row, "__dict__"): # object
items = sorted(row.__dict__.items())
else:
raise ValueError("Can not infer schema for type: %s" % type(row))
fields = [StructField(k, _infer_type(v), True) for k, v in items]
return StructType(fields)
def _need_python_to_sql_conversion(dataType):
"""
Checks whether we need python to sql conversion for the given type.
For now, only UDTs need this conversion.
>>> _need_python_to_sql_conversion(DoubleType())
False
>>> schema0 = StructType([StructField("indices", ArrayType(IntegerType(), False), False),
... StructField("values", ArrayType(DoubleType(), False), False)])
>>> _need_python_to_sql_conversion(schema0)
False
>>> _need_python_to_sql_conversion(ExamplePointUDT())
True
>>> schema1 = ArrayType(ExamplePointUDT(), False)
>>> _need_python_to_sql_conversion(schema1)
True
>>> schema2 = StructType([StructField("label", DoubleType(), False),
... StructField("point", ExamplePointUDT(), False)])
>>> _need_python_to_sql_conversion(schema2)
True
"""
if isinstance(dataType, StructType):
return any([_need_python_to_sql_conversion(f.dataType) for f in dataType.fields])
elif isinstance(dataType, ArrayType):
return _need_python_to_sql_conversion(dataType.elementType)
elif isinstance(dataType, MapType):
return _need_python_to_sql_conversion(dataType.keyType) or \
_need_python_to_sql_conversion(dataType.valueType)
elif isinstance(dataType, UserDefinedType):
return True
else:
return False
def _python_to_sql_converter(dataType):
"""
Returns a converter that converts a Python object into a SQL datum for the given type.
>>> conv = _python_to_sql_converter(DoubleType())
>>> conv(1.0)
1.0
>>> conv = _python_to_sql_converter(ArrayType(DoubleType(), False))
>>> conv([1.0, 2.0])
[1.0, 2.0]
>>> conv = _python_to_sql_converter(ExamplePointUDT())
>>> conv(ExamplePoint(1.0, 2.0))
[1.0, 2.0]
>>> schema = StructType([StructField("label", DoubleType(), False),
... StructField("point", ExamplePointUDT(), False)])
>>> conv = _python_to_sql_converter(schema)
>>> conv((1.0, ExamplePoint(1.0, 2.0)))
(1.0, [1.0, 2.0])
"""
if not _need_python_to_sql_conversion(dataType):
return lambda x: x
if isinstance(dataType, StructType):
names, types = zip(*[(f.name, f.dataType) for f in dataType.fields])
converters = map(_python_to_sql_converter, types)
def converter(obj):
if isinstance(obj, dict):
return tuple(c(obj.get(n)) for n, c in zip(names, converters))
elif isinstance(obj, tuple):
if hasattr(obj, "_fields") or hasattr(obj, "__FIELDS__"):
return tuple(c(v) for c, v in zip(converters, obj))
elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): # k-v pairs
d = dict(obj)
return tuple(c(d.get(n)) for n, c in zip(names, converters))
else:
return tuple(c(v) for c, v in zip(converters, obj))
else:
raise ValueError("Unexpected tuple %r with type %r" % (obj, dataType))
return converter
elif isinstance(dataType, ArrayType):
element_converter = _python_to_sql_converter(dataType.elementType)
return lambda a: [element_converter(v) for v in a]
elif isinstance(dataType, MapType):
key_converter = _python_to_sql_converter(dataType.keyType)
value_converter = _python_to_sql_converter(dataType.valueType)
return lambda m: dict([(key_converter(k), value_converter(v)) for k, v in m.items()])
elif isinstance(dataType, UserDefinedType):
return lambda obj: dataType.serialize(obj)
else:
raise ValueError("Unexpected type %r" % dataType)
def _has_nulltype(dt):
""" Return whether there is NullType in `dt` or not """
if isinstance(dt, StructType):
return any(_has_nulltype(f.dataType) for f in dt.fields)
elif isinstance(dt, ArrayType):
return _has_nulltype((dt.elementType))
elif isinstance(dt, MapType):
return _has_nulltype(dt.keyType) or _has_nulltype(dt.valueType)
else:
return isinstance(dt, NullType)
def _merge_type(a, b):
if isinstance(a, NullType):
return b
elif isinstance(b, NullType):
return a
elif type(a) is not type(b):
# TODO: type cast (such as int -> long)
raise TypeError("Can not merge type %s and %s" % (a, b))
# same type
if isinstance(a, StructType):
nfs = dict((f.name, f.dataType) for f in b.fields)
fields = [StructField(f.name, _merge_type(f.dataType, nfs.get(f.name, NullType())))
for f in a.fields]
names = set([f.name for f in fields])
for n in nfs:
if n not in names:
fields.append(StructField(n, nfs[n]))
return StructType(fields)
elif isinstance(a, ArrayType):
return ArrayType(_merge_type(a.elementType, b.elementType), True)
elif isinstance(a, MapType):
return MapType(_merge_type(a.keyType, b.keyType),
_merge_type(a.valueType, b.valueType),
True)
else:
return a
def _create_converter(dataType):
"""Create an converter to drop the names of fields in obj """
if isinstance(dataType, ArrayType):
conv = _create_converter(dataType.elementType)
return lambda row: map(conv, row)
elif isinstance(dataType, MapType):
kconv = _create_converter(dataType.keyType)
vconv = _create_converter(dataType.valueType)
return lambda row: dict((kconv(k), vconv(v)) for k, v in row.iteritems())
elif isinstance(dataType, NullType):
return lambda x: None
elif not isinstance(dataType, StructType):
return lambda x: x
# dataType must be StructType
names = [f.name for f in dataType.fields]
converters = [_create_converter(f.dataType) for f in dataType.fields]
def convert_struct(obj):
if obj is None:
return
if isinstance(obj, tuple):
if hasattr(obj, "_fields"):
d = dict(zip(obj._fields, obj))
elif hasattr(obj, "__FIELDS__"):
d = dict(zip(obj.__FIELDS__, obj))
elif all(isinstance(x, tuple) and len(x) == 2 for x in obj):
d = dict(obj)
else:
raise ValueError("unexpected tuple: %s" % str(obj))
elif isinstance(obj, dict):
d = obj
elif hasattr(obj, "__dict__"): # object
d = obj.__dict__
else:
raise ValueError("Unexpected obj: %s" % obj)
return tuple([conv(d.get(name)) for name, conv in zip(names, converters)])
return convert_struct
_BRACKETS = {'(': ')', '[': ']', '{': '}'}
def _split_schema_abstract(s):
"""
split the schema abstract into fields
>>> _split_schema_abstract("a b c")
['a', 'b', 'c']
>>> _split_schema_abstract("a(a b)")
['a(a b)']
>>> _split_schema_abstract("a b[] c{a b}")
['a', 'b[]', 'c{a b}']
>>> _split_schema_abstract(" ")
[]
"""
r = []
w = ''
brackets = []
for c in s:
if c == ' ' and not brackets:
if w:
r.append(w)
w = ''
else:
w += c
if c in _BRACKETS:
brackets.append(c)
elif c in _BRACKETS.values():
if not brackets or c != _BRACKETS[brackets.pop()]:
raise ValueError("unexpected " + c)
if brackets:
raise ValueError("brackets not closed: %s" % brackets)
if w:
r.append(w)
return r
def _parse_field_abstract(s):
"""
Parse a field in schema abstract
>>> _parse_field_abstract("a")
StructField(a,None,true)
>>> _parse_field_abstract("b(c d)")
StructField(b,StructType(...c,None,true),StructField(d...
>>> _parse_field_abstract("a[]")
StructField(a,ArrayType(None,true),true)
>>> _parse_field_abstract("a{[]}")
StructField(a,MapType(None,ArrayType(None,true),true),true)
"""
if set(_BRACKETS.keys()) & set(s):
idx = min((s.index(c) for c in _BRACKETS if c in s))
name = s[:idx]
return StructField(name, _parse_schema_abstract(s[idx:]), True)
else:
return StructField(s, None, True)
def _parse_schema_abstract(s):
"""
parse abstract into schema
>>> _parse_schema_abstract("a b c")
StructType...a...b...c...
>>> _parse_schema_abstract("a[b c] b{}")
StructType...a,ArrayType...b...c...b,MapType...
>>> _parse_schema_abstract("c{} d{a b}")
StructType...c,MapType...d,MapType...a...b...
>>> _parse_schema_abstract("a b(t)").fields[1]
StructField(b,StructType(List(StructField(t,None,true))),true)
"""
s = s.strip()
if not s:
return
elif s.startswith('('):
return _parse_schema_abstract(s[1:-1])
elif s.startswith('['):
return ArrayType(_parse_schema_abstract(s[1:-1]), True)
elif s.startswith('{'):
return MapType(None, _parse_schema_abstract(s[1:-1]))
parts = _split_schema_abstract(s)
fields = [_parse_field_abstract(p) for p in parts]
return StructType(fields)
def _infer_schema_type(obj, dataType):
"""
Fill the dataType with types inferred from obj
>>> schema = _parse_schema_abstract("a b c d")
>>> row = (1, 1.0, "str", datetime.date(2014, 10, 10))
>>> _infer_schema_type(row, schema)
StructType...IntegerType...DoubleType...StringType...DateType...
>>> row = [[1], {"key": (1, 2.0)}]
>>> schema = _parse_schema_abstract("a[] b{c d}")
>>> _infer_schema_type(row, schema)
StructType...a,ArrayType...b,MapType(StringType,...c,IntegerType...
"""
if dataType is None:
return _infer_type(obj)
if not obj:
return NullType()
if isinstance(dataType, ArrayType):
eType = _infer_schema_type(obj[0], dataType.elementType)
return ArrayType(eType, True)
elif isinstance(dataType, MapType):
k, v = obj.iteritems().next()
return MapType(_infer_schema_type(k, dataType.keyType),
_infer_schema_type(v, dataType.valueType))
elif isinstance(dataType, StructType):
fs = dataType.fields
assert len(fs) == len(obj), \
"Obj(%s) have different length with fields(%s)" % (obj, fs)
fields = [StructField(f.name, _infer_schema_type(o, f.dataType), True)
for o, f in zip(obj, fs)]
return StructType(fields)
else:
raise ValueError("Unexpected dataType: %s" % dataType)
_acceptable_types = {
BooleanType: (bool,),
ByteType: (int, long),
ShortType: (int, long),
IntegerType: (int, long),
LongType: (int, long),
FloatType: (float,),
DoubleType: (float,),
DecimalType: (decimal.Decimal,),
StringType: (str, unicode),
BinaryType: (bytearray,),
DateType: (datetime.date,),
TimestampType: (datetime.datetime,),
ArrayType: (list, tuple, array),
MapType: (dict,),
StructType: (tuple, list),
}
def _verify_type(obj, dataType):
"""
Verify the type of obj against dataType, raise an exception if
they do not match.
>>> _verify_type(None, StructType([]))
>>> _verify_type("", StringType())
>>> _verify_type(0, IntegerType())
>>> _verify_type(range(3), ArrayType(ShortType()))
>>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TypeError:...
>>> _verify_type({}, MapType(StringType(), IntegerType()))
>>> _verify_type((), StructType([]))
>>> _verify_type([], StructType([]))
>>> _verify_type([1], StructType([])) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
ValueError:...
>>> _verify_type(ExamplePoint(1.0, 2.0), ExamplePointUDT())
>>> _verify_type([1.0, 2.0], ExamplePointUDT()) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
ValueError:...
"""
# all objects are nullable
if obj is None:
return
if isinstance(dataType, UserDefinedType):
if not (hasattr(obj, '__UDT__') and obj.__UDT__ == dataType):
raise ValueError("%r is not an instance of type %r" % (obj, dataType))
_verify_type(dataType.serialize(obj), dataType.sqlType())
return
_type = type(dataType)
assert _type in _acceptable_types, "unkown datatype: %s" % dataType
# subclass of them can not be deserialized in JVM
if type(obj) not in _acceptable_types[_type]:
raise TypeError("%s can not accept object in type %s"
% (dataType, type(obj)))
if isinstance(dataType, ArrayType):
for i in obj:
_verify_type(i, dataType.elementType)
elif isinstance(dataType, MapType):
for k, v in obj.iteritems():
_verify_type(k, dataType.keyType)
_verify_type(v, dataType.valueType)
elif isinstance(dataType, StructType):
if len(obj) != len(dataType.fields):
raise ValueError("Length of object (%d) does not match with"
"length of fields (%d)" % (len(obj), len(dataType.fields)))
for v, f in zip(obj, dataType.fields):
_verify_type(v, f.dataType)
_cached_cls = {}
def _restore_object(dataType, obj):
""" Restore object during unpickling. """
# use id(dataType) as key to speed up lookup in dict
# Because of batched pickling, dataType will be the
# same object in most cases.
k = id(dataType)
cls = _cached_cls.get(k)
if cls is None:
# use dataType as key to avoid create multiple class
cls = _cached_cls.get(dataType)
if cls is None:
cls = _create_cls(dataType)
_cached_cls[dataType] = cls
_cached_cls[k] = cls
return cls(obj)
def _create_object(cls, v):
""" Create an customized object with class `cls`. """
# datetime.date would be deserialized as datetime.datetime
# from java type, so we need to set it back.
if cls is datetime.date and isinstance(v, datetime.datetime):
return v.date()
return cls(v) if v is not None else v
def _create_getter(dt, i):
""" Create a getter for item `i` with schema """
cls = _create_cls(dt)
def getter(self):
return _create_object(cls, self[i])
return getter
def _has_struct_or_date(dt):
"""Return whether `dt` is or has StructType/DateType in it"""
if isinstance(dt, StructType):
return True
elif isinstance(dt, ArrayType):
return _has_struct_or_date(dt.elementType)
elif isinstance(dt, MapType):
return _has_struct_or_date(dt.keyType) or _has_struct_or_date(dt.valueType)
elif isinstance(dt, DateType):
return True
elif isinstance(dt, UserDefinedType):
return True
return False
def _create_properties(fields):
"""Create properties according to fields"""
ps = {}
for i, f in enumerate(fields):
name = f.name
if (name.startswith("__") and name.endswith("__")
or keyword.iskeyword(name)):
warnings.warn("field name %s can not be accessed in Python,"
"use position to access it instead" % name)
if _has_struct_or_date(f.dataType):
# delay creating object until accessing it
getter = _create_getter(f.dataType, i)
else:
getter = itemgetter(i)
ps[name] = property(getter)
return ps
def _create_cls(dataType):
"""
Create an class by dataType
The created class is similar to namedtuple, but can have nested schema.
>>> schema = _parse_schema_abstract("a b c")
>>> row = (1, 1.0, "str")
>>> schema = _infer_schema_type(row, schema)
>>> obj = _create_cls(schema)(row)
>>> import pickle
>>> pickle.loads(pickle.dumps(obj))
Row(a=1, b=1.0, c='str')
>>> row = [[1], {"key": (1, 2.0)}]
>>> schema = _parse_schema_abstract("a[] b{c d}")
>>> schema = _infer_schema_type(row, schema)
>>> obj = _create_cls(schema)(row)
>>> pickle.loads(pickle.dumps(obj))
Row(a=[1], b={'key': Row(c=1, d=2.0)})
>>> pickle.loads(pickle.dumps(obj.a))
[1]
>>> pickle.loads(pickle.dumps(obj.b))
{'key': Row(c=1, d=2.0)}
"""
if isinstance(dataType, ArrayType):
cls = _create_cls(dataType.elementType)
def List(l):
if l is None:
return
return [_create_object(cls, v) for v in l]
return List
elif isinstance(dataType, MapType):
kcls = _create_cls(dataType.keyType)
vcls = _create_cls(dataType.valueType)
def Dict(d):
if d is None:
return
return dict((_create_object(kcls, k), _create_object(vcls, v)) for k, v in d.items())
return Dict
elif isinstance(dataType, DateType):
return datetime.date
elif isinstance(dataType, UserDefinedType):
return lambda datum: dataType.deserialize(datum)
elif not isinstance(dataType, StructType):
# no wrapper for primitive types
return lambda x: x
class Row(tuple):
""" Row in DataFrame """
__DATATYPE__ = dataType
__FIELDS__ = tuple(f.name for f in dataType.fields)
__slots__ = ()
# create property for fast access
locals().update(_create_properties(dataType.fields))
def asDict(self):
""" Return as a dict """
return dict((n, getattr(self, n)) for n in self.__FIELDS__)
def __repr__(self):
# call collect __repr__ for nested objects
return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n))
for n in self.__FIELDS__))
def __reduce__(self):
return (_restore_object, (self.__DATATYPE__, tuple(self)))
return Row
class SQLContext(object):
"""Main entry point for Spark SQL functionality.
A SQLContext can be used create L{DataFrame}, register L{DataFrame} as
tables, execute SQL over tables, cache tables, and read parquet files.
"""
def __init__(self, sparkContext, sqlContext=None):
"""Create a new SQLContext.
:param sparkContext: The SparkContext to wrap.
:param sqlContext: An optional JVM Scala SQLContext. If set, we do not instatiate a new
SQLContext in the JVM, instead we make all calls to this object.
>>> df = sqlCtx.inferSchema(rdd)
>>> sqlCtx.inferSchema(df) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
TypeError:...
>>> bad_rdd = sc.parallelize([1,2,3])
>>> sqlCtx.inferSchema(bad_rdd) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
ValueError:...
>>> from datetime import datetime
>>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L,
... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
... time=datetime(2014, 8, 1, 14, 1, 5))])
>>> df = sqlCtx.inferSchema(allTypes)
>>> df.registerTempTable("allTypes")
>>> sqlCtx.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
... 'from allTypes where b and i > 0').collect()
[Row(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)]
>>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time,
... x.row.a, x.list)).collect()
[(1, u'string', 1.0, 1, True, ...(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
"""
self._sc = sparkContext
self._jsc = self._sc._jsc
self._jvm = self._sc._jvm
self._scala_SQLContext = sqlContext
@property
def _ssql_ctx(self):
"""Accessor for the JVM Spark SQL context.
Subclasses can override this property to provide their own
JVM Contexts.
"""
if self._scala_SQLContext is None:
self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc())
return self._scala_SQLContext
def registerFunction(self, name, f, returnType=StringType()):
"""Registers a lambda function as a UDF so it can be used in SQL statements.
In addition to a name and the function itself, the return type can be optionally specified.
When the return type is not given it default to a string and conversion will automatically
be done. For any other return type, the produced object must match the specified type.
>>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x))
>>> sqlCtx.sql("SELECT stringLengthString('test')").collect()
[Row(c0=u'4')]
>>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType())
>>> sqlCtx.sql("SELECT stringLengthInt('test')").collect()
[Row(c0=4)]
"""
func = lambda _, it: imap(lambda x: f(*x), it)
ser = AutoBatchedSerializer(PickleSerializer())
command = (func, None, ser, ser)
pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self._sc, command, self)
self._ssql_ctx.udf().registerPython(name,
bytearray(pickled_cmd),
env,
includes,
self._sc.pythonExec,
bvars,
self._sc._javaAccumulator,
returnType.json())
def inferSchema(self, rdd, samplingRatio=None):
"""Infer and apply a schema to an RDD of L{Row}.
When samplingRatio is specified, the schema is inferred by looking
at the types of each row in the sampled dataset. Otherwise, the
first 100 rows of the RDD are inspected. Nested collections are
supported, which can include array, dict, list, Row, tuple,
namedtuple, or object.
Each row could be L{pyspark.sql.Row} object or namedtuple or objects.
Using top level dicts is deprecated, as dict is used to represent Maps.
If a single column has multiple distinct inferred types, it may cause
runtime exceptions.
>>> rdd = sc.parallelize(
... [Row(field1=1, field2="row1"),
... Row(field1=2, field2="row2"),
... Row(field1=3, field2="row3")])
>>> df = sqlCtx.inferSchema(rdd)
>>> df.collect()[0]
Row(field1=1, field2=u'row1')
>>> NestedRow = Row("f1", "f2")
>>> nestedRdd1 = sc.parallelize([
... NestedRow(array('i', [1, 2]), {"row1": 1.0}),
... NestedRow(array('i', [2, 3]), {"row2": 2.0})])
>>> df = sqlCtx.inferSchema(nestedRdd1)
>>> df.collect()
[Row(f1=[1, 2], f2={u'row1': 1.0}), ..., f2={u'row2': 2.0})]
>>> nestedRdd2 = sc.parallelize([
... NestedRow([[1, 2], [2, 3]], [1, 2]),
... NestedRow([[2, 3], [3, 4]], [2, 3])])
>>> df = sqlCtx.inferSchema(nestedRdd2)
>>> df.collect()
[Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), ..., f2=[2, 3])]
>>> from collections import namedtuple
>>> CustomRow = namedtuple('CustomRow', 'field1 field2')
>>> rdd = sc.parallelize(
... [CustomRow(field1=1, field2="row1"),
... CustomRow(field1=2, field2="row2"),
... CustomRow(field1=3, field2="row3")])
>>> df = sqlCtx.inferSchema(rdd)
>>> df.collect()[0]
Row(field1=1, field2=u'row1')
"""
if isinstance(rdd, DataFrame):
raise TypeError("Cannot apply schema to DataFrame")
first = rdd.first()
if not first:
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")
if samplingRatio is None:
schema = _infer_schema(first)
if _has_nulltype(schema):
for row in rdd.take(100)[1:]:
schema = _merge_type(schema, _infer_schema(row))
if not _has_nulltype(schema):
break
else:
warnings.warn("Some of types cannot be determined by the "
"first 100 rows, please try again with sampling")
else:
if samplingRatio > 0.99:
rdd = rdd.sample(False, float(samplingRatio))
schema = rdd.map(_infer_schema).reduce(_merge_type)
converter = _create_converter(schema)
rdd = rdd.map(converter)
return self.applySchema(rdd, schema)
def applySchema(self, rdd, schema):
"""
Applies the given schema to the given RDD of L{tuple} or L{list}.
These tuples or lists can contain complex nested structures like
lists, maps or nested rows.
The schema should be a StructType.
It is important that the schema matches the types of the objects
in each row or exceptions could be thrown at runtime.
>>> rdd2 = sc.parallelize([(1, "row1"), (2, "row2"), (3, "row3")])
>>> schema = StructType([StructField("field1", IntegerType(), False),
... StructField("field2", StringType(), False)])
>>> df = sqlCtx.applySchema(rdd2, schema)
>>> sqlCtx.registerRDDAsTable(df, "table1")
>>> df2 = sqlCtx.sql("SELECT * from table1")
>>> df2.collect()
[Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')]
>>> from datetime import date, datetime
>>> rdd = sc.parallelize([(127, -128L, -32768, 32767, 2147483647L, 1.0,
... date(2010, 1, 1),
... datetime(2010, 1, 1, 1, 1, 1),
... {"a": 1}, (2,), [1, 2, 3], None)])
>>> schema = StructType([
... StructField("byte1", ByteType(), False),
... StructField("byte2", ByteType(), False),
... StructField("short1", ShortType(), False),
... StructField("short2", ShortType(), False),
... StructField("int", IntegerType(), False),
... StructField("float", FloatType(), False),
... StructField("date", DateType(), False),
... StructField("time", TimestampType(), False),
... StructField("map",
... MapType(StringType(), IntegerType(), False), False),
... StructField("struct",
... StructType([StructField("b", ShortType(), False)]), False),
... StructField("list", ArrayType(ByteType(), False), False),
... StructField("null", DoubleType(), True)])
>>> df = sqlCtx.applySchema(rdd, schema)
>>> results = df.map(
... lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int, x.float, x.date,
... x.time, x.map["a"], x.struct.b, x.list, x.null))
>>> results.collect()[0] # doctest: +NORMALIZE_WHITESPACE
(127, -128, -32768, 32767, 2147483647, 1.0, datetime.date(2010, 1, 1),
datetime.datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None)
>>> df.registerTempTable("table2")
>>> sqlCtx.sql(
... "SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " +
... "short1 + 1 AS short1, short2 - 1 AS short2, int - 1 AS int, " +
... "float + 1.5 as float FROM table2").collect()
[Row(byte1=126, byte2=-127, short1=-32767, short2=32766, int=2147483646, float=2.5)]
>>> rdd = sc.parallelize([(127, -32768, 1.0,
... datetime(2010, 1, 1, 1, 1, 1),
... {"a": 1}, (2,), [1, 2, 3])])
>>> abstract = "byte short float time map{} struct(b) list[]"
>>> schema = _parse_schema_abstract(abstract)
>>> typedSchema = _infer_schema_type(rdd.first(), schema)
>>> df = sqlCtx.applySchema(rdd, typedSchema)
>>> df.collect()
[Row(byte=127, short=-32768, float=1.0, time=..., list=[1, 2, 3])]
"""
if isinstance(rdd, DataFrame):
raise TypeError("Cannot apply schema to DataFrame")
if not isinstance(schema, StructType):
raise TypeError("schema should be StructType")
# take the first few rows to verify schema
rows = rdd.take(10)
# Row() cannot been deserialized by Pyrolite
if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row':
rdd = rdd.map(tuple)
rows = rdd.take(10)
for row in rows:
_verify_type(row, schema)
# convert python objects to sql data
converter = _python_to_sql_converter(schema)
rdd = rdd.map(converter)
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
return DataFrame(df, self)
def registerRDDAsTable(self, rdd, tableName):
"""Registers the given RDD as a temporary table in the catalog.
Temporary tables exist only during the lifetime of this instance of
SQLContext.
>>> df = sqlCtx.inferSchema(rdd)
>>> sqlCtx.registerRDDAsTable(df, "table1")
"""
if (rdd.__class__ is DataFrame):
df = rdd._jdf
self._ssql_ctx.registerRDDAsTable(df, tableName)
else:
raise ValueError("Can only register DataFrame as table")
def parquetFile(self, *paths):
"""Loads a Parquet file, returning the result as a L{DataFrame}.
>>> import tempfile, shutil
>>> parquetFile = tempfile.mkdtemp()
>>> shutil.rmtree(parquetFile)
>>> df = sqlCtx.inferSchema(rdd)
>>> df.saveAsParquetFile(parquetFile)
>>> df2 = sqlCtx.parquetFile(parquetFile)
>>> sorted(df.collect()) == sorted(df2.collect())
True
"""
gateway = self._sc._gateway
jpath = paths[0]
jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths) - 1)
for i in range(1, len(paths)):
jpaths[i] = paths[i]
jdf = self._ssql_ctx.parquetFile(jpath, jpaths)
return DataFrame(jdf, self)
def jsonFile(self, path, schema=None, samplingRatio=1.0):
"""
Loads a text file storing one JSON object per line as a
L{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)
>>> ofn = open(jsonFile, 'w')
>>> for json in jsonStrings:
... print>>ofn, json
>>> ofn.close()
>>> df1 = sqlCtx.jsonFile(jsonFile)
>>> sqlCtx.registerRDDAsTable(df1, "table1")
>>> df2 = sqlCtx.sql(
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
... "field6 as f4 from table1")
>>> for r in df2.collect():
... print r
Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')])
Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
>>> df3 = sqlCtx.jsonFile(jsonFile, df1.schema())
>>> sqlCtx.registerRDDAsTable(df3, "table2")
>>> df4 = sqlCtx.sql(
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
... "field6 as f4 from table2")
>>> for r in df4.collect():
... print r
Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')])
Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
>>> schema = StructType([
... StructField("field2", StringType(), True),
... StructField("field3",
... StructType([
... StructField("field5",
... ArrayType(IntegerType(), False), True)]), False)])
>>> df5 = sqlCtx.jsonFile(jsonFile, schema)
>>> sqlCtx.registerRDDAsTable(df5, "table3")
>>> df6 = sqlCtx.sql(
... "SELECT field2 AS f1, field3.field5 as f2, "
... "field3.field5[0] as f3 from table3")
>>> df6.collect()
[Row(f1=u'row1', f2=None, f3=None)...Row(f1=u'row3', f2=[], f3=None)]
"""
if schema is None:
df = self._ssql_ctx.jsonFile(path, samplingRatio)
else:
scala_datatype = self._ssql_ctx.parseDataType(schema.json())
df = self._ssql_ctx.jsonFile(path, scala_datatype)
return DataFrame(df, self)
def jsonRDD(self, rdd, schema=None, samplingRatio=1.0):
"""Loads an RDD storing one JSON object per string as a L{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.
>>> df1 = sqlCtx.jsonRDD(json)
>>> sqlCtx.registerRDDAsTable(df1, "table1")
>>> df2 = sqlCtx.sql(
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
... "field6 as f4 from table1")
>>> for r in df2.collect():
... print r
Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')])
Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
>>> df3 = sqlCtx.jsonRDD(json, df1.schema())
>>> sqlCtx.registerRDDAsTable(df3, "table2")
>>> df4 = sqlCtx.sql(
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
... "field6 as f4 from table2")
>>> for r in df4.collect():
... print r
Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')])
Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
>>> schema = StructType([
... StructField("field2", StringType(), True),
... StructField("field3",
... StructType([
... StructField("field5",
... ArrayType(IntegerType(), False), True)]), False)])
>>> df5 = sqlCtx.jsonRDD(json, schema)
>>> sqlCtx.registerRDDAsTable(df5, "table3")
>>> df6 = sqlCtx.sql(
... "SELECT field2 AS f1, field3.field5 as f2, "
... "field3.field5[0] as f3 from table3")
>>> df6.collect()
[Row(f1=u'row1', f2=None,...Row(f1=u'row3', f2=[], f3=None)]
>>> sqlCtx.jsonRDD(sc.parallelize(['{}',
... '{"key0": {"key1": "value1"}}'])).collect()
[Row(key0=None), Row(key0=Row(key1=u'value1'))]
>>> sqlCtx.jsonRDD(sc.parallelize(['{"key0": null}',
... '{"key0": {"key1": "value1"}}'])).collect()
[Row(key0=None), Row(key0=Row(key1=u'value1'))]
"""
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 = rdd.mapPartitions(func)
keyed._bypass_serializer = True
jrdd = keyed._jrdd.map(self._jvm.BytesToString())
if schema is None:
df = self._ssql_ctx.jsonRDD(jrdd.rdd(), samplingRatio)
else:
scala_datatype = self._ssql_ctx.parseDataType(schema.json())
df = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
return DataFrame(df, self)
def sql(self, sqlQuery):
"""Return a L{DataFrame} representing the result of the given query.
>>> df = sqlCtx.inferSchema(rdd)
>>> sqlCtx.registerRDDAsTable(df, "table1")
>>> df2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
>>> df2.collect()
[Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
"""
return DataFrame(self._ssql_ctx.sql(sqlQuery), self)
def table(self, tableName):
"""Returns the specified table as a L{DataFrame}.
>>> df = sqlCtx.inferSchema(rdd)
>>> sqlCtx.registerRDDAsTable(df, "table1")
>>> df2 = sqlCtx.table("table1")
>>> sorted(df.collect()) == sorted(df2.collect())
True
"""
return DataFrame(self._ssql_ctx.table(tableName), self)
def cacheTable(self, tableName):
"""Caches the specified table in-memory."""
self._ssql_ctx.cacheTable(tableName)
def uncacheTable(self, tableName):
"""Removes the specified table from the in-memory cache."""
self._ssql_ctx.uncacheTable(tableName)
class HiveContext(SQLContext):
"""A variant of Spark SQL that integrates with data stored in Hive.
Configuration for Hive is read from hive-site.xml on the classpath.
It supports running both SQL and HiveQL commands.
"""
def __init__(self, sparkContext, hiveContext=None):
"""Create a new HiveContext.
:param sparkContext: The SparkContext to wrap.
:param hiveContext: An optional JVM Scala HiveContext. If set, we do not instatiate a new
HiveContext in the JVM, instead we make all calls to this object.
"""
SQLContext.__init__(self, sparkContext)
if hiveContext:
self._scala_HiveContext = hiveContext
@property
def _ssql_ctx(self):
try:
if not hasattr(self, '_scala_HiveContext'):
self._scala_HiveContext = self._get_hive_ctx()
return self._scala_HiveContext
except Py4JError as e:
raise Exception("You must build Spark with Hive. "
"Export 'SPARK_HIVE=true' and run "
"build/sbt assembly", e)
def _get_hive_ctx(self):
return self._jvm.HiveContext(self._jsc.sc())
def _create_row(fields, values):
row = Row(*values)
row.__FIELDS__ = fields
return row
class Row(tuple):
"""
A row in L{DataFrame}. The fields in it can be accessed like attributes.
Row can be used to create a row object by using named arguments,
the fields will be sorted by names.
>>> row = Row(name="Alice", age=11)
>>> row
Row(age=11, name='Alice')
>>> row.name, row.age
('Alice', 11)
Row also can be used to create another Row like class, then it
could be used to create Row objects, such as
>>> Person = Row("name", "age")
>>> Person
<Row(name, age)>
>>> Person("Alice", 11)
Row(name='Alice', age=11)
"""
def __new__(self, *args, **kwargs):
if args and kwargs:
raise ValueError("Can not use both args "
"and kwargs to create Row")
if args:
# create row class or objects
return tuple.__new__(self, args)
elif kwargs:
# create row objects
names = sorted(kwargs.keys())
values = tuple(kwargs[n] for n in names)
row = tuple.__new__(self, values)
row.__FIELDS__ = names
return row
else:
raise ValueError("No args or kwargs")
def asDict(self):
"""
Return as an dict
"""
if not hasattr(self, "__FIELDS__"):
raise TypeError("Cannot convert a Row class into dict")
return dict(zip(self.__FIELDS__, self))
# let obect acs like class
def __call__(self, *args):
"""create new Row object"""
return _create_row(self, args)
def __getattr__(self, item):
if item.startswith("__"):
raise AttributeError(item)
try:
# it will be slow when it has many fields,
# but this will not be used in normal cases
idx = self.__FIELDS__.index(item)
return self[idx]
except IndexError:
raise AttributeError(item)
def __reduce__(self):
if hasattr(self, "__FIELDS__"):
return (_create_row, (self.__FIELDS__, tuple(self)))
else:
return tuple.__reduce__(self)
def __repr__(self):
if hasattr(self, "__FIELDS__"):
return "Row(%s)" % ", ".join("%s=%r" % (k, v)
for k, v in zip(self.__FIELDS__, self))
else:
return "<Row(%s)>" % ", ".join(self)
class DataFrame(object):
"""A collection of rows that have the same columns.
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("...")
Once created, it can be manipulated using the various domain-specific-language
(DSL) functions defined in: :class:`DataFrame`, :class:`Column`.
To select a column from the data frame, use the apply method::
ageCol = people.age
Note that the :class:`Column` type can also be manipulated
through its various functions::
# The following creates a new column that increases everybody's age by 10.
people.age + 10
A more concrete example::
# To create DataFrame using SQLContext
people = sqlContext.parquetFile("...")
department = sqlContext.parquetFile("...")
people.filter(people.age > 30).join(department, people.deptId == department.id)) \
.groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"})
"""
def __init__(self, jdf, sql_ctx):
self._jdf = jdf
self.sql_ctx = sql_ctx
self._sc = sql_ctx and sql_ctx._sc
self.is_cached = False
@property
def rdd(self):
"""
Return the content of the :class:`DataFrame` as an :class:`RDD`
of :class:`Row` s.
"""
if not hasattr(self, '_lazy_rdd'):
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema()
def applySchema(it):
cls = _create_cls(schema)
return itertools.imap(cls, it)
self._lazy_rdd = rdd.mapPartitions(applySchema)
return self._lazy_rdd
def toJSON(self, use_unicode=False):
"""Convert a DataFrame into a MappedRDD of JSON documents; one document per row.
>>> df1 = sqlCtx.jsonRDD(json)
>>> sqlCtx.registerRDDAsTable(df1, "table1")
>>> df2 = sqlCtx.sql( "SELECT * from table1")
>>> df2.toJSON().take(1)[0] == '{"field1":1,"field2":"row1","field3":{"field4":11}}'
True
>>> df3 = sqlCtx.sql( "SELECT field3.field4 from table1")
>>> df3.toJSON().collect() == ['{"field4":11}', '{"field4":22}', '{"field4":33}']
True
"""
rdd = self._jdf.toJSON()
return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))
def saveAsParquetFile(self, path):
"""Save the contents as a Parquet file, preserving the schema.
Files that are written out using this method can be read back in as
a DataFrame using the L{SQLContext.parquetFile} method.
>>> import tempfile, shutil
>>> parquetFile = tempfile.mkdtemp()
>>> shutil.rmtree(parquetFile)
>>> df.saveAsParquetFile(parquetFile)
>>> df2 = sqlCtx.parquetFile(parquetFile)
>>> sorted(df2.collect()) == sorted(df.collect())
True
"""
self._jdf.saveAsParquetFile(path)
def registerTempTable(self, name):
"""Registers this RDD as a temporary table using the given name.
The lifetime of this temporary table is tied to the L{SQLContext}
that was used to create this DataFrame.
>>> df.registerTempTable("people")
>>> df2 = sqlCtx.sql("select * from people")
>>> sorted(df.collect()) == sorted(df2.collect())
True
"""
self._jdf.registerTempTable(name)
def registerAsTable(self, name):
"""DEPRECATED: use registerTempTable() instead"""
warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning)
self.registerTempTable(name)
def insertInto(self, tableName, overwrite=False):
"""Inserts the contents of this DataFrame into the specified table.
Optionally overwriting any existing data.
"""
self._jdf.insertInto(tableName, overwrite)
def saveAsTable(self, tableName):
"""Creates a new table with the contents of this DataFrame."""
self._jdf.saveAsTable(tableName)
def schema(self):
"""Returns the schema of this DataFrame (represented by
a L{StructType}).
>>> df.schema()
StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true)))
"""
return _parse_datatype_json_string(self._jdf.schema().json())
def printSchema(self):
"""Prints out the schema in the tree format.
>>> df.printSchema()
root
|-- age: integer (nullable = true)
|-- name: string (nullable = true)
<BLANKLINE>
"""
print (self._jdf.schema().treeString())
def count(self):
"""Return the number of elements in this RDD.
Unlike the base RDD implementation of count, this implementation
leverages the query optimizer to compute the count on the DataFrame,
which supports features such as filter pushdown.
>>> df.count()
2L
"""
return self._jdf.count()
def collect(self):
"""Return a list that contains all of the rows.
Each object in the list is a Row, the fields can be accessed as
attributes.
>>> df.collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
"""
with SCCallSiteSync(self._sc) as css:
bytesInJava = self._jdf.javaToPython().collect().iterator()
tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
tempFile.close()
self._sc._writeToFile(bytesInJava, tempFile.name)
# Read the data into Python and deserialize it:
with open(tempFile.name, 'rb') as tempFile:
rs = list(BatchedSerializer(PickleSerializer()).load_stream(tempFile))
os.unlink(tempFile.name)
cls = _create_cls(self.schema())
return [cls(r) for r in rs]
def limit(self, num):
"""Limit the result count to the number specified.
>>> df.limit(1).collect()
[Row(age=2, name=u'Alice')]
>>> df.limit(0).collect()
[]
"""
jdf = self._jdf.limit(num)
return DataFrame(jdf, self.sql_ctx)
def take(self, num):
"""Take the first num rows of the RDD.
Each object in the list is a Row, the fields can be accessed as
attributes.
>>> df.take(2)
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
"""
return self.limit(num).collect()
def map(self, f):
""" Return a new RDD by applying a function to each Row, it's a
shorthand for df.rdd.map()
>>> df.map(lambda p: p.name).collect()
[u'Alice', u'Bob']
"""
return self.rdd.map(f)
def mapPartitions(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each partition.
>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
>>> def f(iterator): yield 1
>>> rdd.mapPartitions(f).sum()
4
"""
return self.rdd.mapPartitions(f, preservesPartitioning)
def cache(self):
""" Persist with the default storage level (C{MEMORY_ONLY_SER}).
"""
self.is_cached = True
self._jdf.cache()
return self
def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
""" Set the storage level to persist its values across operations
after the first time it is computed. This can only be used to assign
a new storage level if the RDD does not have a storage level set yet.
If no storage level is specified defaults to (C{MEMORY_ONLY_SER}).
"""
self.is_cached = True
javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
self._jdf.persist(javaStorageLevel)
return self
def unpersist(self, blocking=True):
""" Mark it as non-persistent, and remove all blocks for it from
memory and disk.
"""
self.is_cached = False
self._jdf.unpersist(blocking)
return self
# def coalesce(self, numPartitions, shuffle=False):
# rdd = self._jdf.coalesce(numPartitions, shuffle, None)
# return DataFrame(rdd, self.sql_ctx)
def repartition(self, numPartitions):
""" Return a new :class:`DataFrame` that has exactly `numPartitions`
partitions.
"""
rdd = self._jdf.repartition(numPartitions, None)
return DataFrame(rdd, self.sql_ctx)
def sample(self, withReplacement, fraction, seed=None):
"""
Return a sampled subset of this DataFrame.
>>> df = sqlCtx.inferSchema(rdd)
>>> df.sample(False, 0.5, 97).count()
2L
"""
assert fraction >= 0.0, "Negative fraction value: %s" % fraction
seed = seed if seed is not None else random.randint(0, sys.maxint)
rdd = self._jdf.sample(withReplacement, fraction, long(seed))
return DataFrame(rdd, self.sql_ctx)
# def takeSample(self, withReplacement, num, seed=None):
# """Return a fixed-size sampled subset of this DataFrame.
#
# >>> df = sqlCtx.inferSchema(rdd)
# >>> df.takeSample(False, 2, 97)
# [Row(field1=3, field2=u'row3'), Row(field1=1, field2=u'row1')]
# """
# seed = seed if seed is not None else random.randint(0, sys.maxint)
# with SCCallSiteSync(self.context) as css:
# bytesInJava = self._jdf \
# .takeSampleToPython(withReplacement, num, long(seed)) \
# .iterator()
# cls = _create_cls(self.schema())
# return map(cls, self._collect_iterator_through_file(bytesInJava))
@property
def dtypes(self):
"""Return all column names and their data types as a list.
>>> df.dtypes
[('age', 'integer'), ('name', 'string')]
"""
return [(str(f.name), f.dataType.jsonValue()) for f in self.schema().fields]
@property
def columns(self):
""" Return all column names as a list.
>>> df.columns
[u'age', u'name']
"""
return [f.name for f in self.schema().fields]
def join(self, other, joinExprs=None, joinType=None):
"""
Join with another DataFrame, using the given join expression.
The following performs a full outer join between `df1` and `df2`::
:param other: Right side of the join
:param joinExprs: Join expression
:param joinType: One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`.
>>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect()
[Row(name=None, height=80), Row(name=u'Bob', height=85), Row(name=u'Alice', height=None)]
"""
if joinExprs is None:
jdf = self._jdf.join(other._jdf)
else:
assert isinstance(joinExprs, Column), "joinExprs should be Column"
if joinType is None:
jdf = self._jdf.join(other._jdf, joinExprs._jc)
else:
assert isinstance(joinType, basestring), "joinType should be basestring"
jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType)
return DataFrame(jdf, self.sql_ctx)
def sort(self, *cols):
""" Return a new :class:`DataFrame` sorted by the specified column.
:param cols: The columns or expressions used for sorting
>>> df.sort(df.age.desc()).collect()
[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
>>> df.sortBy(df.age.desc()).collect()
[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
"""
if not cols:
raise ValueError("should sort by at least one column")
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
self._sc._gateway._gateway_client)
jdf = self._jdf.sort(self._sc._jvm.PythonUtils.toSeq(jcols))
return DataFrame(jdf, self.sql_ctx)
sortBy = sort
def head(self, n=None):
""" Return the first `n` rows or the first row if n is None.
>>> df.head()
Row(age=2, name=u'Alice')
>>> df.head(1)
[Row(age=2, name=u'Alice')]
"""
if n is None:
rs = self.head(1)
return rs[0] if rs else None
return self.take(n)
def first(self):
""" Return the first row.
>>> df.first()
Row(age=2, name=u'Alice')
"""
return self.head()
def __getitem__(self, item):
""" Return the column by given name
>>> df['age'].collect()
[Row(age=2), Row(age=5)]
>>> df[ ["name", "age"]].collect()
[Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)]
>>> df[ df.age > 3 ].collect()
[Row(age=5, name=u'Bob')]
"""
if isinstance(item, basestring):
jc = self._jdf.apply(item)
return Column(jc, self.sql_ctx)
elif isinstance(item, Column):
return self.filter(item)
elif isinstance(item, list):
return self.select(*item)
else:
raise IndexError("unexpected index: %s" % item)
def __getattr__(self, name):
""" Return the column by given name
>>> df.age.collect()
[Row(age=2), Row(age=5)]
"""
if name.startswith("__"):
raise AttributeError(name)
jc = self._jdf.apply(name)
return Column(jc, self.sql_ctx)
def select(self, *cols):
""" Selecting a set of expressions.
>>> df.select().collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
>>> df.select('*').collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
>>> df.select('name', 'age').collect()
[Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)]
>>> df.select(df.name, (df.age + 10).alias('age')).collect()
[Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)]
"""
if not cols:
cols = ["*"]
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
self._sc._gateway._gateway_client)
jdf = self._jdf.select(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
return DataFrame(jdf, self.sql_ctx)
def selectExpr(self, *expr):
"""
Selects a set of SQL expressions. This is a variant of
`select` that accepts SQL expressions.
>>> df.selectExpr("age * 2", "abs(age)").collect()
[Row(('age * 2)=4, Abs('age)=2), Row(('age * 2)=10, Abs('age)=5)]
"""
jexpr = ListConverter().convert(expr, self._sc._gateway._gateway_client)
jdf = self._jdf.selectExpr(self._sc._jvm.PythonUtils.toSeq(jexpr))
return DataFrame(jdf, self.sql_ctx)
def filter(self, condition):
""" Filtering rows using the given condition, which could be
Column expression or string of SQL expression.
where() is an alias for filter().
>>> df.filter(df.age > 3).collect()
[Row(age=5, name=u'Bob')]
>>> df.where(df.age == 2).collect()
[Row(age=2, name=u'Alice')]
>>> df.filter("age > 3").collect()
[Row(age=5, name=u'Bob')]
>>> df.where("age = 2").collect()
[Row(age=2, name=u'Alice')]
"""
if isinstance(condition, basestring):
jdf = self._jdf.filter(condition)
elif isinstance(condition, Column):
jdf = self._jdf.filter(condition._jc)
else:
raise TypeError("condition should be string or Column")
return DataFrame(jdf, self.sql_ctx)
where = filter
def groupBy(self, *cols):
""" Group the :class:`DataFrame` using the specified columns,
so we can run aggregation on them. See :class:`GroupedData`
for all the available aggregate functions.
>>> df.groupBy().avg().collect()
[Row(AVG(age#0)=3.5)]
>>> df.groupBy('name').agg({'age': 'mean'}).collect()
[Row(name=u'Bob', AVG(age#0)=5.0), Row(name=u'Alice', AVG(age#0)=2.0)]
>>> df.groupBy(df.name).avg().collect()
[Row(name=u'Bob', AVG(age#0)=5.0), Row(name=u'Alice', AVG(age#0)=2.0)]
"""
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
self._sc._gateway._gateway_client)
jdf = self._jdf.groupBy(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
return GroupedData(jdf, self.sql_ctx)
def agg(self, *exprs):
""" Aggregate on the entire :class:`DataFrame` without groups
(shorthand for df.groupBy.agg()).
>>> df.agg({"age": "max"}).collect()
[Row(MAX(age#0)=5)]
>>> from pyspark.sql import Dsl
>>> df.agg(Dsl.min(df.age)).collect()
[Row(MIN(age#0)=2)]
"""
return self.groupBy().agg(*exprs)
def unionAll(self, other):
""" Return a new DataFrame containing union of rows in this
frame and another frame.
This is equivalent to `UNION ALL` in SQL.
"""
return DataFrame(self._jdf.unionAll(other._jdf), self.sql_ctx)
def intersect(self, other):
""" Return a new :class:`DataFrame` containing rows only in
both this frame and another frame.
This is equivalent to `INTERSECT` in SQL.
"""
return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx)
def subtract(self, other):
""" Return a new :class:`DataFrame` containing rows in this frame
but not in another frame.
This is equivalent to `EXCEPT` in SQL.
"""
return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx)
def addColumn(self, colName, col):
""" Return a new :class:`DataFrame` by adding a column.
>>> df.addColumn('age2', df.age + 2).collect()
[Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)]
"""
return self.select('*', col.alias(colName))
def to_pandas(self):
"""
Collect all the rows and return a `pandas.DataFrame`.
>>> df.to_pandas() # doctest: +SKIP
age name
0 2 Alice
1 5 Bob
"""
import pandas as pd
return pd.DataFrame.from_records(self.collect(), columns=self.columns)
# Having SchemaRDD for backward compatibility (for docs)
class SchemaRDD(DataFrame):
"""
SchemaRDD is deprecated, please use DataFrame
"""
def dfapi(f):
def _api(self):
name = f.__name__
jdf = getattr(self._jdf, name)()
return DataFrame(jdf, self.sql_ctx)
_api.__name__ = f.__name__
_api.__doc__ = f.__doc__
return _api
class GroupedData(object):
"""
A set of methods for aggregations on a :class:`DataFrame`,
created by DataFrame.groupBy().
"""
def __init__(self, jdf, sql_ctx):
self._jdf = jdf
self.sql_ctx = sql_ctx
def agg(self, *exprs):
""" Compute aggregates by specifying a map from column name
to aggregate methods.
The available aggregate methods are `avg`, `max`, `min`,
`sum`, `count`.
:param exprs: list or aggregate columns or a map from column
name to aggregate methods.
>>> gdf = df.groupBy(df.name)
>>> gdf.agg({"age": "max"}).collect()
[Row(name=u'Bob', MAX(age#0)=5), Row(name=u'Alice', MAX(age#0)=2)]
>>> from pyspark.sql import Dsl
>>> gdf.agg(Dsl.min(df.age)).collect()
[Row(MIN(age#0)=5), Row(MIN(age#0)=2)]
"""
assert exprs, "exprs should not be empty"
if len(exprs) == 1 and isinstance(exprs[0], dict):
jmap = MapConverter().convert(exprs[0],
self.sql_ctx._sc._gateway._gateway_client)
jdf = self._jdf.agg(jmap)
else:
# Columns
assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
jcols = ListConverter().convert([c._jc for c in exprs[1:]],
self.sql_ctx._sc._gateway._gateway_client)
jdf = self._jdf.agg(exprs[0]._jc, self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
return DataFrame(jdf, self.sql_ctx)
@dfapi
def count(self):
""" Count the number of rows for each group.
>>> df.groupBy(df.age).count().collect()
[Row(age=2, count=1), Row(age=5, count=1)]
"""
@dfapi
def mean(self):
"""Compute the average value for each numeric columns
for each group. This is an alias for `avg`."""
@dfapi
def avg(self):
"""Compute the average value for each numeric columns
for each group."""
@dfapi
def max(self):
"""Compute the max value for each numeric columns for
each group. """
@dfapi
def min(self):
"""Compute the min value for each numeric column for
each group."""
@dfapi
def sum(self):
"""Compute the sum for each numeric columns for each
group."""
def _create_column_from_literal(literal):
sc = SparkContext._active_spark_context
return sc._jvm.Dsl.lit(literal)
def _create_column_from_name(name):
sc = SparkContext._active_spark_context
return sc._jvm.Dsl.col(name)
def _to_java_column(col):
if isinstance(col, Column):
jcol = col._jc
else:
jcol = _create_column_from_name(col)
return jcol
def _unary_op(name, doc="unary operator"):
""" Create a method for given unary operator """
def _(self):
jc = getattr(self._jc, name)()
return Column(jc, self.sql_ctx)
_.__doc__ = doc
return _
def _dsl_op(name, doc=''):
def _(self):
jc = getattr(self._sc._jvm.Dsl, name)(self._jc)
return Column(jc, self.sql_ctx)
_.__doc__ = doc
return _
def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
def _(self, other):
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc, self.sql_ctx)
_.__doc__ = doc
return _
def _reverse_op(name, doc="binary operator"):
""" Create a method for binary operator (this object is on right side)
"""
def _(self, other):
jother = _create_column_from_literal(other)
jc = getattr(jother, name)(self._jc)
return Column(jc, self.sql_ctx)
_.__doc__ = doc
return _
class Column(DataFrame):
"""
A column in a DataFrame.
`Column` instances can be created by::
# 1. Select a column out of a DataFrame
df.colName
df["colName"]
# 2. Create from an expression
df.colName + 1
1 / df.colName
"""
def __init__(self, jc, sql_ctx=None):
self._jc = jc
super(Column, self).__init__(jc, sql_ctx)
# arithmetic operators
__neg__ = _dsl_op("negate")
__add__ = _bin_op("plus")
__sub__ = _bin_op("minus")
__mul__ = _bin_op("multiply")
__div__ = _bin_op("divide")
__mod__ = _bin_op("mod")
__radd__ = _bin_op("plus")
__rsub__ = _reverse_op("minus")
__rmul__ = _bin_op("multiply")
__rdiv__ = _reverse_op("divide")
__rmod__ = _reverse_op("mod")
# logistic operators
__eq__ = _bin_op("equalTo")
__ne__ = _bin_op("notEqual")
__lt__ = _bin_op("lt")
__le__ = _bin_op("leq")
__ge__ = _bin_op("geq")
__gt__ = _bin_op("gt")
# `and`, `or`, `not` cannot be overloaded in Python,
# so use bitwise operators as boolean operators
__and__ = _bin_op('and')
__or__ = _bin_op('or')
__invert__ = _dsl_op('not')
__rand__ = _bin_op("and")
__ror__ = _bin_op("or")
# container operators
__contains__ = _bin_op("contains")
__getitem__ = _bin_op("getItem")
getField = _bin_op("getField", "An expression that gets a field by name in a StructField.")
# string methods
rlike = _bin_op("rlike")
like = _bin_op("like")
startswith = _bin_op("startsWith")
endswith = _bin_op("endsWith")
def substr(self, startPos, length):
"""
Return a Column which is a substring of the column
:param startPos: start position (int or Column)
:param length: length of the substring (int or Column)
>>> df.name.substr(1, 3).collect()
[Row(col=u'Ali'), Row(col=u'Bob')]
"""
if type(startPos) != type(length):
raise TypeError("Can not mix the type")
if isinstance(startPos, (int, long)):
jc = self._jc.substr(startPos, length)
elif isinstance(startPos, Column):
jc = self._jc.substr(startPos._jc, length._jc)
else:
raise TypeError("Unexpected type: %s" % type(startPos))
return Column(jc, self.sql_ctx)
__getslice__ = substr
# order
asc = _unary_op("asc")
desc = _unary_op("desc")
isNull = _unary_op("isNull", "True if the current expression is null.")
isNotNull = _unary_op("isNotNull", "True if the current expression is not null.")
def alias(self, alias):
"""Return a alias for this column
>>> df.age.alias("age2").collect()
[Row(age2=2), Row(age2=5)]
"""
return Column(getattr(self._jc, "as")(alias), self.sql_ctx)
def cast(self, dataType):
""" Convert the column into type `dataType`
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
"""
if self.sql_ctx is None:
sc = SparkContext._active_spark_context
ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
else:
ssql_ctx = self.sql_ctx._ssql_ctx
if isinstance(dataType, basestring):
jc = self._jc.cast(dataType)
elif isinstance(dataType, DataType):
jdt = ssql_ctx.parseDataType(dataType.json())
jc = self._jc.cast(jdt)
return Column(jc, self.sql_ctx)
def to_pandas(self):
"""
Return a pandas.Series from the column
>>> df.age.to_pandas() # doctest: +SKIP
0 2
1 5
dtype: int64
"""
import pandas as pd
data = [c for c, in self.collect()]
return pd.Series(data)
def _aggregate_func(name, doc=""):
""" Create a function for aggregator by name"""
def _(col):
sc = SparkContext._active_spark_context
jc = getattr(sc._jvm.Dsl, name)(_to_java_column(col))
return Column(jc)
_.__name__ = name
_.__doc__ = doc
return staticmethod(_)
class UserDefinedFunction(object):
def __init__(self, func, returnType):
self.func = func
self.returnType = returnType
self._broadcast = None
self._judf = self._create_judf()
def _create_judf(self):
f = self.func # put it in closure `func`
func = lambda _, it: imap(lambda x: f(*x), it)
ser = AutoBatchedSerializer(PickleSerializer())
command = (func, None, ser, ser)
sc = SparkContext._active_spark_context
pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
jdt = ssql_ctx.parseDataType(self.returnType.json())
judf = sc._jvm.UserDefinedPythonFunction(f.__name__, bytearray(pickled_command), env,
includes, sc.pythonExec, broadcast_vars,
sc._javaAccumulator, jdt)
return judf
def __del__(self):
if self._broadcast is not None:
self._broadcast.unpersist()
self._broadcast = None
def __call__(self, *cols):
sc = SparkContext._active_spark_context
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
sc._gateway._gateway_client)
jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols))
return Column(jc)
class Dsl(object):
"""
A collections of builtin aggregators
"""
DSLS = {
'lit': 'Creates a :class:`Column` of literal value.',
'col': 'Returns a :class:`Column` based on the given column name.',
'column': 'Returns a :class:`Column` based on the given column name.',
'upper': 'Converts a string expression to upper case.',
'lower': 'Converts a string expression to upper case.',
'sqrt': 'Computes the square root of the specified float value.',
'abs': 'Computes the absolutle value.',
'max': 'Aggregate function: returns the maximum value of the expression in a group.',
'min': 'Aggregate function: returns the minimum value of the expression in a group.',
'first': 'Aggregate function: returns the first value in a group.',
'last': 'Aggregate function: returns the last value in a group.',
'count': 'Aggregate function: returns the number of items in a group.',
'sum': 'Aggregate function: returns the sum of all values in the expression.',
'avg': 'Aggregate function: returns the average of the values in a group.',
'mean': 'Aggregate function: returns the average of the values in a group.',
'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.',
}
for _name, _doc in DSLS.items():
locals()[_name] = _aggregate_func(_name, _doc)
del _name, _doc
@staticmethod
def countDistinct(col, *cols):
""" Return a new Column for distinct count of (col, *cols)
>>> from pyspark.sql import Dsl
>>> df.agg(Dsl.countDistinct(df.age, df.name).alias('c')).collect()
[Row(c=2)]
>>> df.agg(Dsl.countDistinct("age", "name").alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
sc._gateway._gateway_client)
jc = sc._jvm.Dsl.countDistinct(_to_java_column(col),
sc._jvm.PythonUtils.toSeq(jcols))
return Column(jc)
@staticmethod
def approxCountDistinct(col, rsd=None):
""" Return a new Column for approxiate distinct count of (col, *cols)
>>> from pyspark.sql import Dsl
>>> df.agg(Dsl.approxCountDistinct(df.age).alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
if rsd is None:
jc = sc._jvm.Dsl.approxCountDistinct(_to_java_column(col))
else:
jc = sc._jvm.Dsl.approxCountDistinct(_to_java_column(col), rsd)
return Column(jc)
@staticmethod
def udf(f, returnType=StringType()):
"""Create a user defined function (UDF)
>>> slen = Dsl.udf(lambda s: len(s), IntegerType())
>>> df.select(slen(df.name).alias('slen')).collect()
[Row(slen=5), Row(slen=3)]
"""
return UserDefinedFunction(f, returnType)
def _test():
import doctest
from pyspark.context import SparkContext
# let doctest run in pyspark.sql, so DataTypes can be picklable
import pyspark.sql
from pyspark.sql import Row, SQLContext
from pyspark.sql_tests import ExamplePoint, ExamplePointUDT
globs = pyspark.sql.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
globs['sqlCtx'] = sqlCtx = SQLContext(sc)
globs['rdd'] = sc.parallelize(
[Row(field1=1, field2="row1"),
Row(field1=2, field2="row2"),
Row(field1=3, field2="row3")]
)
rdd2 = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)])
rdd3 = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)])
globs['df'] = sqlCtx.inferSchema(rdd2)
globs['df2'] = sqlCtx.inferSchema(rdd3)
globs['ExamplePoint'] = ExamplePoint
globs['ExamplePointUDT'] = ExamplePointUDT
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['json'] = sc.parallelize(jsonStrings)
(failure_count, test_count) = doctest.testmod(
pyspark.sql, globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
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