880eabec37
Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes. This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance. root |-- field1: integer (nullable = true) |-- field2: string (nullable = true) |-- field3: struct (nullable = true) | |-- field4: integer (nullable = true) | |-- field5: array (nullable = true) | | |-- element: integer (containsNull = false) |-- field6: array (nullable = true) | |-- element: struct (containsNull = false) | | |-- field7: string (nullable = true) Then we can access them by row.field3.field5[0] or row.field6[5].field7 It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType. You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as: ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1])) Or you could use Row to create a class just like namedtuple, for example: Person = Row("name", "age") ctx.inferSchema(rdd.map(lambda x: Person(*x))) Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details. schema = StructType([StructField("name, StringType, True), StructType("age", IntegerType, True)]) ctx.applySchema(rdd, schema) PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable. Author: Davies Liu <davies.liu@gmail.com> Closes #1598 from davies/nested and squashes the following commits: f1d15b6 [Davies Liu] verify schema with the first few rows 8852aaf [Davies Liu] check type of schema abe9e6e [Davies Liu] address comments 61b2292 [Davies Liu] add @deprecated to pythonToJavaMap 1e5b801 [Davies Liu] improve cache of classes 51aa135 [Davies Liu] use Row to infer schema e9c0d5c [Davies Liu] remove string typed schema 353a3f2 [Davies Liu] fix code style 63de8f8 [Davies Liu] fix typo c79ca67 [Davies Liu] fix serialization of nested data 6b258b5 [Davies Liu] fix pep8 9d8447c [Davies Liu] apply schema provided by string of names f5df97f [Davies Liu] refactor, address comments 9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python 84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested 0eaaf56 [Davies Liu] fix doc tests b3559b4 [Davies Liu] use generated Row instead of namedtuple c4ddc30 [Davies Liu] fix conflict between name of fields and variables 7f6f251 [Davies Liu] address all comments d69d397 [Davies Liu] refactor 2cc2d45 [Davies Liu] refactor 182fb46 [Davies Liu] refactor bc6e9e1 [Davies Liu] switch to new Schema API 547bf3e [Davies Liu] Merge branch 'master' into nested a435b5a [Davies Liu] add docs and code refactor 2c8debc [Davies Liu] Merge branch 'master' into nested 644665a [Davies Liu] use tuple and namedtuple for schemardd
1641 lines
54 KiB
Python
1641 lines
54 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import types
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import itertools
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import warnings
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import decimal
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import datetime
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import keyword
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import warnings
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from array import array
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from operator import itemgetter
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from pyspark.rdd import RDD, PipelinedRDD
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from pyspark.serializers import BatchedSerializer, PickleSerializer
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from py4j.protocol import Py4JError
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__all__ = [
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"StringType", "BinaryType", "BooleanType", "TimestampType", "DecimalType",
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"DoubleType", "FloatType", "ByteType", "IntegerType", "LongType",
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"ShortType", "ArrayType", "MapType", "StructField", "StructType",
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"SQLContext", "HiveContext", "LocalHiveContext", "TestHiveContext",
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"SchemaRDD", "Row"]
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class DataType(object):
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"""Spark SQL DataType"""
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def __repr__(self):
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return self.__class__.__name__
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def __hash__(self):
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return hash(str(self))
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def __eq__(self, other):
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return (isinstance(other, self.__class__) and
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self.__dict__ == other.__dict__)
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def __ne__(self, other):
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return not self.__eq__(other)
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class PrimitiveTypeSingleton(type):
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"""Metaclass for PrimitiveType"""
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_instances = {}
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def __call__(cls):
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if cls not in cls._instances:
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cls._instances[cls] = super(PrimitiveTypeSingleton, cls).__call__()
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return cls._instances[cls]
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class PrimitiveType(DataType):
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"""Spark SQL PrimitiveType"""
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__metaclass__ = PrimitiveTypeSingleton
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def __eq__(self, other):
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# because they should be the same object
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return self is other
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class StringType(PrimitiveType):
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"""Spark SQL StringType
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The data type representing string values.
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"""
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class BinaryType(PrimitiveType):
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"""Spark SQL BinaryType
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The data type representing bytearray values.
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"""
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class BooleanType(PrimitiveType):
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"""Spark SQL BooleanType
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The data type representing bool values.
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"""
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class TimestampType(PrimitiveType):
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"""Spark SQL TimestampType
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The data type representing datetime.datetime values.
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"""
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class DecimalType(PrimitiveType):
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"""Spark SQL DecimalType
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The data type representing decimal.Decimal values.
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"""
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class DoubleType(PrimitiveType):
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"""Spark SQL DoubleType
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The data type representing float values.
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"""
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class FloatType(PrimitiveType):
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"""Spark SQL FloatType
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The data type representing single precision floating-point values.
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"""
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class ByteType(PrimitiveType):
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"""Spark SQL ByteType
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The data type representing int values with 1 singed byte.
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"""
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class IntegerType(PrimitiveType):
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"""Spark SQL IntegerType
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The data type representing int values.
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"""
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class LongType(PrimitiveType):
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"""Spark SQL LongType
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The data type representing long values. If the any value is
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beyond the range of [-9223372036854775808, 9223372036854775807],
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please use DecimalType.
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"""
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class ShortType(PrimitiveType):
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"""Spark SQL ShortType
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The data type representing int values with 2 signed bytes.
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"""
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class ArrayType(DataType):
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"""Spark SQL ArrayType
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The data type representing list values. An ArrayType object
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comprises two fields, elementType (a DataType) and containsNull (a bool).
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The field of elementType is used to specify the type of array elements.
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The field of containsNull is used to specify if the array has None values.
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"""
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def __init__(self, elementType, containsNull=False):
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"""Creates an ArrayType
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:param elementType: the data type of elements.
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:param containsNull: indicates whether the list contains None values.
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>>> ArrayType(StringType) == ArrayType(StringType, False)
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True
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>>> ArrayType(StringType, True) == ArrayType(StringType)
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False
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"""
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self.elementType = elementType
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self.containsNull = containsNull
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def __str__(self):
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return "ArrayType(%s,%s)" % (self.elementType,
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str(self.containsNull).lower())
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class MapType(DataType):
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"""Spark SQL MapType
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The data type representing dict values. A MapType object comprises
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three fields, keyType (a DataType), valueType (a DataType) and
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valueContainsNull (a bool).
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The field of keyType is used to specify the type of keys in the map.
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The field of valueType is used to specify the type of values in the map.
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The field of valueContainsNull is used to specify if values of this
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map has None values.
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For values of a MapType column, keys are not allowed to have None values.
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"""
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def __init__(self, keyType, valueType, valueContainsNull=True):
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"""Creates a MapType
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:param keyType: the data type of keys.
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:param valueType: the data type of values.
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:param valueContainsNull: indicates whether values contains
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null values.
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>>> (MapType(StringType, IntegerType)
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... == MapType(StringType, IntegerType, True))
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True
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>>> (MapType(StringType, IntegerType, False)
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... == MapType(StringType, FloatType))
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False
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"""
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self.keyType = keyType
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self.valueType = valueType
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self.valueContainsNull = valueContainsNull
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def __repr__(self):
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return "MapType(%s,%s,%s)" % (self.keyType, self.valueType,
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str(self.valueContainsNull).lower())
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class StructField(DataType):
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"""Spark SQL StructField
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Represents a field in a StructType.
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A StructField object comprises three fields, name (a string),
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dataType (a DataType) and nullable (a bool). The field of name
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is the name of a StructField. The field of dataType specifies
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the data type of a StructField.
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The field of nullable specifies if values of a StructField can
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contain None values.
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"""
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def __init__(self, name, dataType, nullable):
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"""Creates a StructField
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:param name: the name of this field.
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:param dataType: the data type of this field.
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:param nullable: indicates whether values of this field
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can be null.
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>>> (StructField("f1", StringType, True)
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... == StructField("f1", StringType, True))
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True
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>>> (StructField("f1", StringType, True)
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... == StructField("f2", StringType, True))
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False
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"""
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self.name = name
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self.dataType = dataType
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self.nullable = nullable
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def __repr__(self):
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return "StructField(%s,%s,%s)" % (self.name, self.dataType,
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str(self.nullable).lower())
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class StructType(DataType):
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"""Spark SQL StructType
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The data type representing rows.
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A StructType object comprises a list of L{StructField}s.
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"""
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def __init__(self, fields):
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"""Creates a StructType
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>>> struct1 = StructType([StructField("f1", StringType, True)])
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>>> struct2 = StructType([StructField("f1", StringType, True)])
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>>> struct1 == struct2
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True
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>>> struct1 = StructType([StructField("f1", StringType, True)])
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>>> struct2 = StructType([StructField("f1", StringType, True),
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... [StructField("f2", IntegerType, False)]])
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>>> struct1 == struct2
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False
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"""
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self.fields = fields
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def __repr__(self):
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return ("StructType(List(%s))" %
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",".join(str(field) for field in self.fields))
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def _parse_datatype_list(datatype_list_string):
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"""Parses a list of comma separated data types."""
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index = 0
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datatype_list = []
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start = 0
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depth = 0
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while index < len(datatype_list_string):
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if depth == 0 and datatype_list_string[index] == ",":
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datatype_string = datatype_list_string[start:index].strip()
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datatype_list.append(_parse_datatype_string(datatype_string))
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start = index + 1
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elif datatype_list_string[index] == "(":
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depth += 1
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elif datatype_list_string[index] == ")":
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depth -= 1
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index += 1
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# Handle the last data type
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datatype_string = datatype_list_string[start:index].strip()
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datatype_list.append(_parse_datatype_string(datatype_string))
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return datatype_list
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_all_primitive_types = dict((k, v) for k, v in globals().iteritems()
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if type(v) is PrimitiveTypeSingleton and v.__base__ == PrimitiveType)
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def _parse_datatype_string(datatype_string):
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"""Parses the given data type string.
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>>> def check_datatype(datatype):
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... scala_datatype = sqlCtx._ssql_ctx.parseDataType(str(datatype))
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... python_datatype = _parse_datatype_string(
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... scala_datatype.toString())
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... return datatype == python_datatype
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>>> all(check_datatype(cls()) for cls in _all_primitive_types.values())
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True
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>>> # Simple ArrayType.
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>>> simple_arraytype = ArrayType(StringType(), True)
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>>> check_datatype(simple_arraytype)
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True
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>>> # Simple MapType.
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>>> simple_maptype = MapType(StringType(), LongType())
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>>> check_datatype(simple_maptype)
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True
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>>> # Simple StructType.
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>>> simple_structtype = StructType([
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... StructField("a", DecimalType(), False),
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... StructField("b", BooleanType(), True),
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... StructField("c", LongType(), True),
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... StructField("d", BinaryType(), False)])
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>>> check_datatype(simple_structtype)
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True
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>>> # Complex StructType.
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>>> complex_structtype = StructType([
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... StructField("simpleArray", simple_arraytype, True),
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... StructField("simpleMap", simple_maptype, True),
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... StructField("simpleStruct", simple_structtype, True),
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... StructField("boolean", BooleanType(), False)])
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>>> check_datatype(complex_structtype)
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True
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>>> # Complex ArrayType.
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>>> complex_arraytype = ArrayType(complex_structtype, True)
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>>> check_datatype(complex_arraytype)
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True
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>>> # Complex MapType.
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>>> complex_maptype = MapType(complex_structtype,
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... complex_arraytype, False)
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>>> check_datatype(complex_maptype)
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True
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"""
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index = datatype_string.find("(")
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if index == -1:
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# It is a primitive type.
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index = len(datatype_string)
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type_or_field = datatype_string[:index]
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rest_part = datatype_string[index + 1:len(datatype_string) - 1].strip()
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if type_or_field in _all_primitive_types:
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return _all_primitive_types[type_or_field]()
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elif type_or_field == "ArrayType":
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last_comma_index = rest_part.rfind(",")
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containsNull = True
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if rest_part[last_comma_index + 1:].strip().lower() == "false":
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containsNull = False
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elementType = _parse_datatype_string(
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rest_part[:last_comma_index].strip())
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return ArrayType(elementType, containsNull)
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elif type_or_field == "MapType":
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last_comma_index = rest_part.rfind(",")
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valueContainsNull = True
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if rest_part[last_comma_index + 1:].strip().lower() == "false":
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valueContainsNull = False
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keyType, valueType = _parse_datatype_list(
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rest_part[:last_comma_index].strip())
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return MapType(keyType, valueType, valueContainsNull)
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elif type_or_field == "StructField":
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first_comma_index = rest_part.find(",")
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name = rest_part[:first_comma_index].strip()
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last_comma_index = rest_part.rfind(",")
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nullable = True
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if rest_part[last_comma_index + 1:].strip().lower() == "false":
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nullable = False
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dataType = _parse_datatype_string(
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rest_part[first_comma_index + 1:last_comma_index].strip())
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return StructField(name, dataType, nullable)
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elif type_or_field == "StructType":
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# rest_part should be in the format like
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# List(StructField(field1,IntegerType,false)).
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field_list_string = rest_part[rest_part.find("(") + 1:-1]
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fields = _parse_datatype_list(field_list_string)
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return StructType(fields)
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# Mapping Python types to Spark SQL DateType
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_type_mappings = {
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bool: BooleanType,
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int: IntegerType,
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long: LongType,
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float: DoubleType,
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str: StringType,
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unicode: StringType,
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decimal.Decimal: DecimalType,
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datetime.datetime: TimestampType,
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datetime.date: TimestampType,
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datetime.time: TimestampType,
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}
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def _infer_type(obj):
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"""Infer the DataType from obj"""
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if obj is None:
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raise ValueError("Can not infer type for None")
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dataType = _type_mappings.get(type(obj))
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if dataType is not None:
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return dataType()
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if isinstance(obj, dict):
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if not obj:
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raise ValueError("Can not infer type for empty dict")
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key, value = obj.iteritems().next()
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return MapType(_infer_type(key), _infer_type(value), True)
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elif isinstance(obj, (list, array)):
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if not obj:
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raise ValueError("Can not infer type for empty list/array")
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return ArrayType(_infer_type(obj[0]), True)
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else:
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try:
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return _infer_schema(obj)
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except ValueError:
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raise ValueError("not supported type: %s" % type(obj))
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def _infer_schema(row):
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"""Infer the schema from dict/namedtuple/object"""
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if isinstance(row, dict):
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items = sorted(row.items())
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elif isinstance(row, tuple):
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if hasattr(row, "_fields"): # namedtuple
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items = zip(row._fields, tuple(row))
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elif hasattr(row, "__FIELDS__"): # Row
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items = zip(row.__FIELDS__, tuple(row))
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elif all(isinstance(x, tuple) and len(x) == 2 for x in row):
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items = row
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else:
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raise ValueError("Can't infer schema from tuple")
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elif hasattr(row, "__dict__"): # object
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items = sorted(row.__dict__.items())
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else:
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raise ValueError("Can not infer schema for type: %s" % type(row))
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fields = [StructField(k, _infer_type(v), True) for k, v in items]
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return StructType(fields)
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def _create_converter(obj, dataType):
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"""Create an converter to drop the names of fields in obj """
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if not _has_struct(dataType):
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return lambda x: x
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elif isinstance(dataType, ArrayType):
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conv = _create_converter(obj[0], dataType.elementType)
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return lambda row: map(conv, row)
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elif isinstance(dataType, MapType):
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value = obj.values()[0]
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conv = _create_converter(value, dataType.valueType)
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return lambda row: dict((k, conv(v)) for k, v in row.iteritems())
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|
|
# dataType must be StructType
|
|
names = [f.name for f in dataType.fields]
|
|
|
|
if isinstance(obj, dict):
|
|
conv = lambda o: tuple(o.get(n) for n in names)
|
|
|
|
elif isinstance(obj, tuple):
|
|
if hasattr(obj, "_fields"): # namedtuple
|
|
conv = tuple
|
|
elif hasattr(obj, "__FIELDS__"):
|
|
conv = tuple
|
|
elif all(isinstance(x, tuple) and len(x) == 2 for x in obj):
|
|
conv = lambda o: tuple(v for k, v in o)
|
|
else:
|
|
raise ValueError("unexpected tuple")
|
|
|
|
elif hasattr(obj, "__dict__"): # object
|
|
conv = lambda o: [o.__dict__.get(n, None) for n in names]
|
|
|
|
nested = any(_has_struct(f.dataType) for f in dataType.fields)
|
|
if not nested:
|
|
return conv
|
|
|
|
row = conv(obj)
|
|
convs = [_create_converter(v, f.dataType)
|
|
for v, f in zip(row, dataType.fields)]
|
|
|
|
def nested_conv(row):
|
|
return tuple(f(v) for f, v in zip(convs, conv(row)))
|
|
|
|
return nested_conv
|
|
|
|
|
|
def _drop_schema(rows, schema):
|
|
""" all the names of fields, becoming tuples"""
|
|
iterator = iter(rows)
|
|
row = iterator.next()
|
|
converter = _create_converter(row, schema)
|
|
yield converter(row)
|
|
for i in iterator:
|
|
yield converter(i)
|
|
|
|
|
|
_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 infered from obj
|
|
|
|
>>> schema = _parse_schema_abstract("a b c")
|
|
>>> row = (1, 1.0, "str")
|
|
>>> _infer_schema_type(row, schema)
|
|
StructType...IntegerType...DoubleType...StringType...
|
|
>>> 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:
|
|
raise ValueError("Can not infer type from empty value")
|
|
|
|
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_type(k),
|
|
_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),
|
|
TimestampType: (datetime.datetime, datetime.time, datetime.date),
|
|
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:...
|
|
"""
|
|
# all objects are nullable
|
|
if obj is None:
|
|
return
|
|
|
|
_type = type(dataType)
|
|
if _type not in _acceptable_types:
|
|
return
|
|
|
|
if type(obj) not in _acceptable_types[_type]:
|
|
raise TypeError("%s can not accept abject 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 mose 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`. """
|
|
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(dt):
|
|
"""Return whether `dt` is or has StructType in it"""
|
|
if isinstance(dt, StructType):
|
|
return True
|
|
elif isinstance(dt, ArrayType):
|
|
return _has_struct(dt.elementType)
|
|
elif isinstance(dt, MapType):
|
|
return _has_struct(dt.valueType)
|
|
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(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)})
|
|
"""
|
|
|
|
if isinstance(dataType, ArrayType):
|
|
cls = _create_cls(dataType.elementType)
|
|
|
|
class List(list):
|
|
|
|
def __getitem__(self, i):
|
|
# create object with datetype
|
|
return _create_object(cls, list.__getitem__(self, i))
|
|
|
|
def __repr__(self):
|
|
# call collect __repr__ for nested objects
|
|
return "[%s]" % (", ".join(repr(self[i])
|
|
for i in range(len(self))))
|
|
|
|
def __reduce__(self):
|
|
return list.__reduce__(self)
|
|
|
|
return List
|
|
|
|
elif isinstance(dataType, MapType):
|
|
vcls = _create_cls(dataType.valueType)
|
|
|
|
class Dict(dict):
|
|
|
|
def __getitem__(self, k):
|
|
# create object with datetype
|
|
return _create_object(vcls, dict.__getitem__(self, k))
|
|
|
|
def __repr__(self):
|
|
# call collect __repr__ for nested objects
|
|
return "{%s}" % (", ".join("%r: %r" % (k, self[k])
|
|
for k in self))
|
|
|
|
def __reduce__(self):
|
|
return dict.__reduce__(self)
|
|
|
|
return Dict
|
|
|
|
elif not isinstance(dataType, StructType):
|
|
raise Exception("unexpected data type: %s" % dataType)
|
|
|
|
class Row(tuple):
|
|
""" Row in SchemaRDD """
|
|
__DATATYPE__ = dataType
|
|
__FIELDS__ = tuple(f.name for f in dataType.fields)
|
|
__slots__ = ()
|
|
|
|
# create property for fast access
|
|
locals().update(_create_properties(dataType.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:
|
|
"""Main entry point for SparkSQL functionality.
|
|
|
|
A SQLContext can be used create L{SchemaRDD}s, register L{SchemaRDD}s 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.
|
|
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> sqlCtx.inferSchema(srdd) # 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))])
|
|
>>> srdd = sqlCtx.inferSchema(allTypes)
|
|
>>> srdd.registerAsTable("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)]
|
|
>>> srdd.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._pythonToJava = self._jvm.PythonRDD.pythonToJavaArray
|
|
|
|
if sqlContext:
|
|
self._scala_SQLContext = sqlContext
|
|
|
|
@property
|
|
def _ssql_ctx(self):
|
|
"""Accessor for the JVM SparkSQL context.
|
|
|
|
Subclasses can override this property to provide their own
|
|
JVM Contexts.
|
|
"""
|
|
if not hasattr(self, '_scala_SQLContext'):
|
|
self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc())
|
|
return self._scala_SQLContext
|
|
|
|
def inferSchema(self, rdd):
|
|
"""Infer and apply a schema to an RDD of L{Row}s.
|
|
|
|
We peek at the first row of the RDD to determine the fields' names
|
|
and types. Nested collections are supported, which include array,
|
|
dict, list, Row, tuple, namedtuple, or object.
|
|
|
|
All the rows in `rdd` should have the same type with the first one,
|
|
or it will cause runtime exceptions.
|
|
|
|
Each row could be L{pyspark.sql.Row} object or namedtuple or objects,
|
|
using dict is deprecated.
|
|
|
|
>>> rdd = sc.parallelize(
|
|
... [Row(field1=1, field2="row1"),
|
|
... Row(field1=2, field2="row2"),
|
|
... Row(field1=3, field2="row3")])
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> srdd.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})])
|
|
>>> srdd = sqlCtx.inferSchema(nestedRdd1)
|
|
>>> srdd.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])])
|
|
>>> srdd = sqlCtx.inferSchema(nestedRdd2)
|
|
>>> srdd.collect()
|
|
[Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), ..., f2=[2, 3])]
|
|
"""
|
|
|
|
if isinstance(rdd, SchemaRDD):
|
|
raise TypeError("Cannot apply schema to SchemaRDD")
|
|
|
|
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")
|
|
|
|
schema = _infer_schema(first)
|
|
rdd = rdd.mapPartitions(lambda rows: _drop_schema(rows, schema))
|
|
return self.applySchema(rdd, schema)
|
|
|
|
def applySchema(self, rdd, schema):
|
|
"""
|
|
Applies the given schema to the given RDD of L{tuple} or L{list}s.
|
|
|
|
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)])
|
|
>>> srdd = sqlCtx.applySchema(rdd2, schema)
|
|
>>> sqlCtx.registerRDDAsTable(srdd, "table1")
|
|
>>> srdd2 = sqlCtx.sql("SELECT * from table1")
|
|
>>> srdd2.collect()
|
|
[Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')]
|
|
|
|
>>> from datetime import datetime
|
|
>>> rdd = sc.parallelize([(127, -32768, 1.0,
|
|
... datetime(2010, 1, 1, 1, 1, 1),
|
|
... {"a": 1}, (2,), [1, 2, 3], None)])
|
|
>>> schema = StructType([
|
|
... StructField("byte", ByteType(), False),
|
|
... StructField("short", ShortType(), False),
|
|
... StructField("float", FloatType(), 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)])
|
|
>>> srdd = sqlCtx.applySchema(rdd, schema).map(
|
|
... lambda x: (x.byte, x.short, x.float, x.time,
|
|
... x.map["a"], x.struct.b, x.list, x.null))
|
|
>>> srdd.collect()[0]
|
|
(127, -32768, 1.0, ...(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None)
|
|
|
|
>>> 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)
|
|
>>> srdd = sqlCtx.applySchema(rdd, typedSchema)
|
|
>>> srdd.collect()
|
|
[Row(byte=127, short=-32768, float=1.0, time=..., list=[1, 2, 3])]
|
|
"""
|
|
|
|
if isinstance(rdd, SchemaRDD):
|
|
raise TypeError("Cannot apply schema to SchemaRDD")
|
|
|
|
if not isinstance(schema, StructType):
|
|
raise TypeError("schema should be StructType")
|
|
|
|
# take the first few rows to verify schema
|
|
rows = rdd.take(10)
|
|
for row in rows:
|
|
_verify_type(row, schema)
|
|
|
|
batched = isinstance(rdd._jrdd_deserializer, BatchedSerializer)
|
|
jrdd = self._pythonToJava(rdd._jrdd, batched)
|
|
srdd = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), str(schema))
|
|
return SchemaRDD(srdd, 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.
|
|
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> sqlCtx.registerRDDAsTable(srdd, "table1")
|
|
"""
|
|
if (rdd.__class__ is SchemaRDD):
|
|
jschema_rdd = rdd._jschema_rdd
|
|
self._ssql_ctx.registerRDDAsTable(jschema_rdd, tableName)
|
|
else:
|
|
raise ValueError("Can only register SchemaRDD as table")
|
|
|
|
def parquetFile(self, path):
|
|
"""Loads a Parquet file, returning the result as a L{SchemaRDD}.
|
|
|
|
>>> import tempfile, shutil
|
|
>>> parquetFile = tempfile.mkdtemp()
|
|
>>> shutil.rmtree(parquetFile)
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> srdd.saveAsParquetFile(parquetFile)
|
|
>>> srdd2 = sqlCtx.parquetFile(parquetFile)
|
|
>>> sorted(srdd.collect()) == sorted(srdd2.collect())
|
|
True
|
|
"""
|
|
jschema_rdd = self._ssql_ctx.parquetFile(path)
|
|
return SchemaRDD(jschema_rdd, self)
|
|
|
|
def jsonFile(self, path, schema=None):
|
|
"""
|
|
Loads a text file storing one JSON object per line as a
|
|
L{SchemaRDD}.
|
|
|
|
If the schema is provided, applies the given schema to this
|
|
JSON dataset.
|
|
|
|
Otherwise, it goes through the entire dataset once 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()
|
|
>>> srdd1 = sqlCtx.jsonFile(jsonFile)
|
|
>>> sqlCtx.registerRDDAsTable(srdd1, "table1")
|
|
>>> srdd2 = sqlCtx.sql(
|
|
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
|
|
... "field6 as f4 from table1")
|
|
>>> for r in srdd2.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)
|
|
>>> srdd3 = sqlCtx.jsonFile(jsonFile, srdd1.schema())
|
|
>>> sqlCtx.registerRDDAsTable(srdd3, "table2")
|
|
>>> srdd4 = sqlCtx.sql(
|
|
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
|
|
... "field6 as f4 from table2")
|
|
>>> for r in srdd4.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)])
|
|
>>> srdd5 = sqlCtx.jsonFile(jsonFile, schema)
|
|
>>> sqlCtx.registerRDDAsTable(srdd5, "table3")
|
|
>>> srdd6 = sqlCtx.sql(
|
|
... "SELECT field2 AS f1, field3.field5 as f2, "
|
|
... "field3.field5[0] as f3 from table3")
|
|
>>> srdd6.collect()
|
|
[Row(f1=u'row1', f2=None, f3=None)...Row(f1=u'row3', f2=[], f3=None)]
|
|
"""
|
|
if schema is None:
|
|
jschema_rdd = self._ssql_ctx.jsonFile(path)
|
|
else:
|
|
scala_datatype = self._ssql_ctx.parseDataType(str(schema))
|
|
jschema_rdd = self._ssql_ctx.jsonFile(path, scala_datatype)
|
|
return SchemaRDD(jschema_rdd, self)
|
|
|
|
def jsonRDD(self, rdd, schema=None):
|
|
"""Loads an RDD storing one JSON object per string as a L{SchemaRDD}.
|
|
|
|
If the schema is provided, applies the given schema to this
|
|
JSON dataset.
|
|
|
|
Otherwise, it goes through the entire dataset once to determine
|
|
the schema.
|
|
|
|
>>> srdd1 = sqlCtx.jsonRDD(json)
|
|
>>> sqlCtx.registerRDDAsTable(srdd1, "table1")
|
|
>>> srdd2 = sqlCtx.sql(
|
|
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
|
|
... "field6 as f4 from table1")
|
|
>>> for r in srdd2.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)
|
|
>>> srdd3 = sqlCtx.jsonRDD(json, srdd1.schema())
|
|
>>> sqlCtx.registerRDDAsTable(srdd3, "table2")
|
|
>>> srdd4 = sqlCtx.sql(
|
|
... "SELECT field1 AS f1, field2 as f2, field3 as f3, "
|
|
... "field6 as f4 from table2")
|
|
>>> for r in srdd4.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)])
|
|
>>> srdd5 = sqlCtx.jsonRDD(json, schema)
|
|
>>> sqlCtx.registerRDDAsTable(srdd5, "table3")
|
|
>>> srdd6 = sqlCtx.sql(
|
|
... "SELECT field2 AS f1, field3.field5 as f2, "
|
|
... "field3.field5[0] as f3 from table3")
|
|
>>> srdd6.collect()
|
|
[Row(f1=u'row1', f2=None,...Row(f1=u'row3', f2=[], f3=None)]
|
|
"""
|
|
|
|
def func(iterator):
|
|
for x in iterator:
|
|
if not isinstance(x, basestring):
|
|
x = unicode(x)
|
|
yield x.encode("utf-8")
|
|
keyed = rdd.mapPartitions(func)
|
|
keyed._bypass_serializer = True
|
|
jrdd = keyed._jrdd.map(self._jvm.BytesToString())
|
|
if schema is None:
|
|
jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd())
|
|
else:
|
|
scala_datatype = self._ssql_ctx.parseDataType(str(schema))
|
|
jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
|
|
return SchemaRDD(jschema_rdd, self)
|
|
|
|
def sql(self, sqlQuery):
|
|
"""Return a L{SchemaRDD} representing the result of the given query.
|
|
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> sqlCtx.registerRDDAsTable(srdd, "table1")
|
|
>>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
|
|
>>> srdd2.collect()
|
|
[Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
|
|
"""
|
|
return SchemaRDD(self._ssql_ctx.sql(sqlQuery), self)
|
|
|
|
def table(self, tableName):
|
|
"""Returns the specified table as a L{SchemaRDD}.
|
|
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> sqlCtx.registerRDDAsTable(srdd, "table1")
|
|
>>> srdd2 = sqlCtx.table("table1")
|
|
>>> sorted(srdd.collect()) == sorted(srdd2.collect())
|
|
True
|
|
"""
|
|
return SchemaRDD(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.
|
|
"""
|
|
|
|
@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 "
|
|
"sbt/sbt assembly", e)
|
|
|
|
def _get_hive_ctx(self):
|
|
return self._jvm.HiveContext(self._jsc.sc())
|
|
|
|
def hiveql(self, hqlQuery):
|
|
"""
|
|
Runs a query expressed in HiveQL, returning the result as
|
|
a L{SchemaRDD}.
|
|
"""
|
|
return SchemaRDD(self._ssql_ctx.hiveql(hqlQuery), self)
|
|
|
|
def hql(self, hqlQuery):
|
|
"""
|
|
Runs a query expressed in HiveQL, returning the result as
|
|
a L{SchemaRDD}.
|
|
"""
|
|
return self.hiveql(hqlQuery)
|
|
|
|
|
|
class LocalHiveContext(HiveContext):
|
|
"""Starts up an instance of hive where metadata is stored locally.
|
|
|
|
An in-process metadata data is created with data stored in ./metadata.
|
|
Warehouse data is stored in in ./warehouse.
|
|
|
|
>>> import os
|
|
>>> hiveCtx = LocalHiveContext(sc)
|
|
>>> try:
|
|
... supress = hiveCtx.hql("DROP TABLE src")
|
|
... except Exception:
|
|
... pass
|
|
>>> kv1 = os.path.join(os.environ["SPARK_HOME"],
|
|
... 'examples/src/main/resources/kv1.txt')
|
|
>>> supress = hiveCtx.hql(
|
|
... "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
|
|
>>> supress = hiveCtx.hql("LOAD DATA LOCAL INPATH '%s' INTO TABLE src"
|
|
... % kv1)
|
|
>>> results = hiveCtx.hql("FROM src SELECT value"
|
|
... ).map(lambda r: int(r.value.split('_')[1]))
|
|
>>> num = results.count()
|
|
>>> reduce_sum = results.reduce(lambda x, y: x + y)
|
|
>>> num
|
|
500
|
|
>>> reduce_sum
|
|
130091
|
|
"""
|
|
|
|
def __init__(self, sparkContext, sqlContext=None):
|
|
HiveContext.__init__(self, sparkContext, sqlContext)
|
|
warnings.warn("LocalHiveContext is deprecated. "
|
|
"Use HiveContext instead.", DeprecationWarning)
|
|
|
|
def _get_hive_ctx(self):
|
|
return self._jvm.LocalHiveContext(self._jsc.sc())
|
|
|
|
|
|
class TestHiveContext(HiveContext):
|
|
|
|
def _get_hive_ctx(self):
|
|
return self._jvm.TestHiveContext(self._jsc.sc())
|
|
|
|
|
|
def _create_row(fields, values):
|
|
row = Row(*values)
|
|
row.__FIELDS__ = fields
|
|
return row
|
|
|
|
|
|
class Row(tuple):
|
|
"""
|
|
A row in L{SchemaRDD}. 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")
|
|
|
|
|
|
# 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 SchemaRDD(RDD):
|
|
"""An RDD of L{Row} objects that has an associated schema.
|
|
|
|
The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can
|
|
utilize the relational query api exposed by SparkSQL.
|
|
|
|
For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the
|
|
L{SchemaRDD} is not operated on directly, as it's underlying
|
|
implementation is an RDD composed of Java objects. Instead it is
|
|
converted to a PythonRDD in the JVM, on which Python operations can
|
|
be done.
|
|
|
|
This class receives raw tuples from Java but assigns a class to it in
|
|
all its data-collection methods (mapPartitionsWithIndex, collect, take,
|
|
etc) so that PySpark sees them as Row objects with named fields.
|
|
"""
|
|
|
|
def __init__(self, jschema_rdd, sql_ctx):
|
|
self.sql_ctx = sql_ctx
|
|
self._sc = sql_ctx._sc
|
|
self._jschema_rdd = jschema_rdd
|
|
|
|
self.is_cached = False
|
|
self.is_checkpointed = False
|
|
self.ctx = self.sql_ctx._sc
|
|
# the _jrdd is created by javaToPython(), serialized by pickle
|
|
self._jrdd_deserializer = BatchedSerializer(PickleSerializer())
|
|
|
|
@property
|
|
def _jrdd(self):
|
|
"""Lazy evaluation of PythonRDD object.
|
|
|
|
Only done when a user calls methods defined by the
|
|
L{pyspark.rdd.RDD} super class (map, filter, etc.).
|
|
"""
|
|
if not hasattr(self, '_lazy_jrdd'):
|
|
self._lazy_jrdd = self._jschema_rdd.javaToPython()
|
|
return self._lazy_jrdd
|
|
|
|
@property
|
|
def _id(self):
|
|
return self._jrdd.id()
|
|
|
|
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 SchemaRDD using the L{SQLContext.parquetFile} method.
|
|
|
|
>>> import tempfile, shutil
|
|
>>> parquetFile = tempfile.mkdtemp()
|
|
>>> shutil.rmtree(parquetFile)
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> srdd.saveAsParquetFile(parquetFile)
|
|
>>> srdd2 = sqlCtx.parquetFile(parquetFile)
|
|
>>> sorted(srdd2.collect()) == sorted(srdd.collect())
|
|
True
|
|
"""
|
|
self._jschema_rdd.saveAsParquetFile(path)
|
|
|
|
def registerAsTable(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 SchemaRDD.
|
|
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
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|
>>> srdd.registerAsTable("test")
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|
>>> srdd2 = sqlCtx.sql("select * from test")
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|
>>> sorted(srdd.collect()) == sorted(srdd2.collect())
|
|
True
|
|
"""
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|
self._jschema_rdd.registerAsTable(name)
|
|
|
|
def insertInto(self, tableName, overwrite=False):
|
|
"""Inserts the contents of this SchemaRDD into the specified table.
|
|
|
|
Optionally overwriting any existing data.
|
|
"""
|
|
self._jschema_rdd.insertInto(tableName, overwrite)
|
|
|
|
def saveAsTable(self, tableName):
|
|
"""Creates a new table with the contents of this SchemaRDD."""
|
|
self._jschema_rdd.saveAsTable(tableName)
|
|
|
|
def schema(self):
|
|
"""Returns the schema of this SchemaRDD (represented by
|
|
a L{StructType})."""
|
|
return _parse_datatype_string(self._jschema_rdd.schema().toString())
|
|
|
|
def schemaString(self):
|
|
"""Returns the output schema in the tree format."""
|
|
return self._jschema_rdd.schemaString()
|
|
|
|
def printSchema(self):
|
|
"""Prints out the schema in the tree format."""
|
|
print self.schemaString()
|
|
|
|
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 SchemaRDD,
|
|
which supports features such as filter pushdown.
|
|
|
|
>>> srdd = sqlCtx.inferSchema(rdd)
|
|
>>> srdd.count()
|
|
3L
|
|
>>> srdd.count() == srdd.map(lambda x: x).count()
|
|
True
|
|
"""
|
|
return self._jschema_rdd.count()
|
|
|
|
def collect(self):
|
|
"""
|
|
Return a list that contains all of the rows in this RDD.
|
|
|
|
Each object in the list is on Row, the fields can be accessed as
|
|
attributes.
|
|
"""
|
|
rows = RDD.collect(self)
|
|
cls = _create_cls(self.schema())
|
|
return map(cls, rows)
|
|
|
|
# Convert each object in the RDD to a Row with the right class
|
|
# for this SchemaRDD, so that fields can be accessed as attributes.
|
|
def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
|
|
"""
|
|
Return a new RDD by applying a function to each partition of this RDD,
|
|
while tracking the index of the original partition.
|
|
|
|
>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
|
|
>>> def f(splitIndex, iterator): yield splitIndex
|
|
>>> rdd.mapPartitionsWithIndex(f).sum()
|
|
6
|
|
"""
|
|
rdd = RDD(self._jrdd, self._sc, self._jrdd_deserializer)
|
|
|
|
schema = self.schema()
|
|
import pickle
|
|
pickle.loads(pickle.dumps(schema))
|
|
|
|
def applySchema(_, it):
|
|
cls = _create_cls(schema)
|
|
return itertools.imap(cls, it)
|
|
|
|
objrdd = rdd.mapPartitionsWithIndex(applySchema, preservesPartitioning)
|
|
return objrdd.mapPartitionsWithIndex(f, preservesPartitioning)
|
|
|
|
# We override the default cache/persist/checkpoint behavior
|
|
# as we want to cache the underlying SchemaRDD object in the JVM,
|
|
# not the PythonRDD checkpointed by the super class
|
|
def cache(self):
|
|
self.is_cached = True
|
|
self._jschema_rdd.cache()
|
|
return self
|
|
|
|
def persist(self, storageLevel):
|
|
self.is_cached = True
|
|
javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
|
|
self._jschema_rdd.persist(javaStorageLevel)
|
|
return self
|
|
|
|
def unpersist(self):
|
|
self.is_cached = False
|
|
self._jschema_rdd.unpersist()
|
|
return self
|
|
|
|
def checkpoint(self):
|
|
self.is_checkpointed = True
|
|
self._jschema_rdd.checkpoint()
|
|
|
|
def isCheckpointed(self):
|
|
return self._jschema_rdd.isCheckpointed()
|
|
|
|
def getCheckpointFile(self):
|
|
checkpointFile = self._jschema_rdd.getCheckpointFile()
|
|
if checkpointFile.isDefined():
|
|
return checkpointFile.get()
|
|
else:
|
|
return None
|
|
|
|
def coalesce(self, numPartitions, shuffle=False):
|
|
rdd = self._jschema_rdd.coalesce(numPartitions, shuffle)
|
|
return SchemaRDD(rdd, self.sql_ctx)
|
|
|
|
def distinct(self):
|
|
rdd = self._jschema_rdd.distinct()
|
|
return SchemaRDD(rdd, self.sql_ctx)
|
|
|
|
def intersection(self, other):
|
|
if (other.__class__ is SchemaRDD):
|
|
rdd = self._jschema_rdd.intersection(other._jschema_rdd)
|
|
return SchemaRDD(rdd, self.sql_ctx)
|
|
else:
|
|
raise ValueError("Can only intersect with another SchemaRDD")
|
|
|
|
def repartition(self, numPartitions):
|
|
rdd = self._jschema_rdd.repartition(numPartitions)
|
|
return SchemaRDD(rdd, self.sql_ctx)
|
|
|
|
def subtract(self, other, numPartitions=None):
|
|
if (other.__class__ is SchemaRDD):
|
|
if numPartitions is None:
|
|
rdd = self._jschema_rdd.subtract(other._jschema_rdd)
|
|
else:
|
|
rdd = self._jschema_rdd.subtract(other._jschema_rdd,
|
|
numPartitions)
|
|
return SchemaRDD(rdd, self.sql_ctx)
|
|
else:
|
|
raise ValueError("Can only subtract another SchemaRDD")
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
from array import array
|
|
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
|
|
globs = pyspark.sql.__dict__.copy()
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
sc = SparkContext('local[4]', 'PythonTest', batchSize=2)
|
|
globs['sc'] = sc
|
|
globs['sqlCtx'] = SQLContext(sc)
|
|
globs['rdd'] = sc.parallelize(
|
|
[Row(field1=1, field2="row1"),
|
|
Row(field1=2, field2="row2"),
|
|
Row(field1=3, field2="row3")]
|
|
)
|
|
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()
|