902c15c5e6
## What changes were proposed in this pull request? In Python, sqlContext.getConf didn't allow getting the system default (getConf with one parameter). Now the following are supported: ``` sqlContext.getConf(confName) # System default if not locally set, this is new sqlContext.getConf(confName, myDefault) # myDefault if not locally set, old behavior ``` I also added doctests to this function. The original behavior does not change. ## How was this patch tested? Manually, but doctests were added. Author: mathieu longtin <mathieu.longtin@nuance.com> Closes #12488 from mathieulongtin/pyfixgetconf3.
697 lines
26 KiB
Python
697 lines
26 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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from __future__ import print_function
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import sys
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import warnings
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import json
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from functools import reduce
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if sys.version >= '3':
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basestring = unicode = str
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else:
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from itertools import imap as map
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from py4j.protocol import Py4JError
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from pyspark import since
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from pyspark.rdd import RDD, ignore_unicode_prefix
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from pyspark.serializers import AutoBatchedSerializer, PickleSerializer
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from pyspark.sql.types import Row, DataType, StringType, StructType, _verify_type, \
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_infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string
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from pyspark.sql.dataframe import DataFrame
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from pyspark.sql.readwriter import DataFrameReader
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from pyspark.sql.utils import install_exception_handler
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from pyspark.sql.functions import UserDefinedFunction
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try:
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import pandas
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has_pandas = True
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except Exception:
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has_pandas = False
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__all__ = ["SQLContext", "HiveContext", "UDFRegistration"]
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def _monkey_patch_RDD(sqlContext):
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def toDF(self, schema=None, sampleRatio=None):
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"""
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Converts current :class:`RDD` into a :class:`DataFrame`
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This is a shorthand for ``sqlContext.createDataFrame(rdd, schema, sampleRatio)``
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:param schema: a StructType or list of names of columns
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:param samplingRatio: the sample ratio of rows used for inferring
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:return: a DataFrame
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>>> rdd.toDF().collect()
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[Row(name=u'Alice', age=1)]
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"""
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return sqlContext.createDataFrame(self, schema, sampleRatio)
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RDD.toDF = toDF
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class SQLContext(object):
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"""Main entry point for Spark SQL functionality.
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A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as
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tables, execute SQL over tables, cache tables, and read parquet files.
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:param sparkContext: The :class:`SparkContext` backing this SQLContext.
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:param sqlContext: An optional JVM Scala SQLContext. If set, we do not instantiate a new
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SQLContext in the JVM, instead we make all calls to this object.
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"""
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_instantiatedContext = None
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@ignore_unicode_prefix
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def __init__(self, sparkContext, sqlContext=None):
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"""Creates a new SQLContext.
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>>> from datetime import datetime
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>>> sqlContext = SQLContext(sc)
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>>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1,
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... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
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... time=datetime(2014, 8, 1, 14, 1, 5))])
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>>> df = allTypes.toDF()
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>>> df.registerTempTable("allTypes")
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>>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
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... 'from allTypes where b and i > 0').collect()
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[Row((i + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \
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dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
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>>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect()
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[(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
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"""
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self._sc = sparkContext
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self._jsc = self._sc._jsc
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self._jvm = self._sc._jvm
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self._scala_SQLContext = sqlContext
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_monkey_patch_RDD(self)
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install_exception_handler()
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if SQLContext._instantiatedContext is None:
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SQLContext._instantiatedContext = self
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@property
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def _ssql_ctx(self):
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"""Accessor for the JVM Spark SQL context.
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Subclasses can override this property to provide their own
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JVM Contexts.
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"""
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if self._scala_SQLContext is None:
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self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc())
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return self._scala_SQLContext
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@classmethod
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@since(1.6)
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def getOrCreate(cls, sc):
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"""
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Get the existing SQLContext or create a new one with given SparkContext.
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:param sc: SparkContext
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"""
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if cls._instantiatedContext is None:
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jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
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cls(sc, jsqlContext)
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return cls._instantiatedContext
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@since(1.6)
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def newSession(self):
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"""
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Returns a new SQLContext as new session, that has separate SQLConf,
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registered temporary tables and UDFs, but shared SparkContext and
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table cache.
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"""
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jsqlContext = self._ssql_ctx.newSession()
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return self.__class__(self._sc, jsqlContext)
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@since(1.3)
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def setConf(self, key, value):
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"""Sets the given Spark SQL configuration property.
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"""
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self._ssql_ctx.setConf(key, value)
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@ignore_unicode_prefix
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@since(1.3)
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def getConf(self, key, defaultValue=None):
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"""Returns the value of Spark SQL configuration property for the given key.
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If the key is not set and defaultValue is not None, return
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defaultValue. If the key is not set and defaultValue is None, return
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the system default value.
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>>> sqlContext.getConf("spark.sql.shuffle.partitions")
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u'200'
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>>> sqlContext.getConf("spark.sql.shuffle.partitions", "10")
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u'10'
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>>> sqlContext.setConf("spark.sql.shuffle.partitions", "50")
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>>> sqlContext.getConf("spark.sql.shuffle.partitions", "10")
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u'50'
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"""
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if defaultValue is not None:
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return self._ssql_ctx.getConf(key, defaultValue)
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else:
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return self._ssql_ctx.getConf(key)
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@property
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@since("1.3.1")
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def udf(self):
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"""Returns a :class:`UDFRegistration` for UDF registration.
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:return: :class:`UDFRegistration`
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"""
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return UDFRegistration(self)
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@since(1.4)
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def range(self, start, end=None, step=1, numPartitions=None):
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"""
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Create a :class:`DataFrame` with single LongType column named `id`,
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containing elements in a range from `start` to `end` (exclusive) with
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step value `step`.
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:param start: the start value
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:param end: the end value (exclusive)
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:param step: the incremental step (default: 1)
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:param numPartitions: the number of partitions of the DataFrame
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:return: :class:`DataFrame`
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>>> sqlContext.range(1, 7, 2).collect()
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[Row(id=1), Row(id=3), Row(id=5)]
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If only one argument is specified, it will be used as the end value.
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>>> sqlContext.range(3).collect()
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[Row(id=0), Row(id=1), Row(id=2)]
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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if end is None:
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jdf = self._ssql_ctx.range(0, int(start), int(step), int(numPartitions))
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else:
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jdf = self._ssql_ctx.range(int(start), int(end), int(step), int(numPartitions))
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return DataFrame(jdf, self)
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@ignore_unicode_prefix
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@since(1.2)
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def registerFunction(self, name, f, returnType=StringType()):
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"""Registers a python function (including lambda function) as a UDF
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so it can be used in SQL statements.
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In addition to a name and the function itself, the return type can be optionally specified.
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When the return type is not given it default to a string and conversion will automatically
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be done. For any other return type, the produced object must match the specified type.
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:param name: name of the UDF
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:param f: python function
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:param returnType: a :class:`DataType` object
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>>> sqlContext.registerFunction("stringLengthString", lambda x: len(x))
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>>> sqlContext.sql("SELECT stringLengthString('test')").collect()
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[Row(stringLengthString(test)=u'4')]
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>>> from pyspark.sql.types import IntegerType
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>>> sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType())
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>>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
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[Row(stringLengthInt(test)=4)]
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>>> from pyspark.sql.types import IntegerType
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>>> sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType())
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>>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
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[Row(stringLengthInt(test)=4)]
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"""
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udf = UserDefinedFunction(f, returnType, name)
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self._ssql_ctx.udf().registerPython(name, udf._judf)
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def _inferSchemaFromList(self, data):
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"""
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Infer schema from list of Row or tuple.
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:param data: list of Row or tuple
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:return: StructType
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"""
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if not data:
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raise ValueError("can not infer schema from empty dataset")
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first = data[0]
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if type(first) is dict:
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warnings.warn("inferring schema from dict is deprecated,"
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"please use pyspark.sql.Row instead")
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schema = reduce(_merge_type, map(_infer_schema, data))
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if _has_nulltype(schema):
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raise ValueError("Some of types cannot be determined after inferring")
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return schema
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def _inferSchema(self, rdd, samplingRatio=None):
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"""
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Infer schema from an RDD of Row or tuple.
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:param rdd: an RDD of Row or tuple
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:param samplingRatio: sampling ratio, or no sampling (default)
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:return: StructType
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"""
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first = rdd.first()
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if not first:
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raise ValueError("The first row in RDD is empty, "
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"can not infer schema")
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if type(first) is dict:
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warnings.warn("Using RDD of dict to inferSchema is deprecated. "
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"Use pyspark.sql.Row instead")
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if samplingRatio is None:
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schema = _infer_schema(first)
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if _has_nulltype(schema):
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for row in rdd.take(100)[1:]:
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schema = _merge_type(schema, _infer_schema(row))
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if not _has_nulltype(schema):
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break
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else:
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raise ValueError("Some of types cannot be determined by the "
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"first 100 rows, please try again with sampling")
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else:
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if samplingRatio < 0.99:
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rdd = rdd.sample(False, float(samplingRatio))
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schema = rdd.map(_infer_schema).reduce(_merge_type)
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return schema
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def _createFromRDD(self, rdd, schema, samplingRatio):
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"""
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Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
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"""
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if schema is None or isinstance(schema, (list, tuple)):
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struct = self._inferSchema(rdd, samplingRatio)
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converter = _create_converter(struct)
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rdd = rdd.map(converter)
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if isinstance(schema, (list, tuple)):
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for i, name in enumerate(schema):
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struct.fields[i].name = name
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struct.names[i] = name
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schema = struct
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elif not isinstance(schema, StructType):
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raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
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# convert python objects to sql data
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rdd = rdd.map(schema.toInternal)
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return rdd, schema
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def _createFromLocal(self, data, schema):
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"""
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Create an RDD for DataFrame from an list or pandas.DataFrame, returns
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the RDD and schema.
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"""
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# make sure data could consumed multiple times
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if not isinstance(data, list):
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data = list(data)
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if schema is None or isinstance(schema, (list, tuple)):
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struct = self._inferSchemaFromList(data)
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if isinstance(schema, (list, tuple)):
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for i, name in enumerate(schema):
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struct.fields[i].name = name
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struct.names[i] = name
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schema = struct
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elif isinstance(schema, StructType):
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for row in data:
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_verify_type(row, schema)
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else:
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raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
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# convert python objects to sql data
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data = [schema.toInternal(row) for row in data]
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return self._sc.parallelize(data), schema
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@since(1.3)
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@ignore_unicode_prefix
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def createDataFrame(self, data, schema=None, samplingRatio=None):
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"""
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Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
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When ``schema`` is a list of column names, the type of each column
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will be inferred from ``data``.
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When ``schema`` is ``None``, it will try to infer the schema (column names and types)
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from ``data``, which should be an RDD of :class:`Row`,
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or :class:`namedtuple`, or :class:`dict`.
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When ``schema`` is :class:`DataType` or datatype string, it must match the real data, or
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exception will be thrown at runtime. If the given schema is not StructType, it will be
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wrapped into a StructType as its only field, and the field name will be "value", each record
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will also be wrapped into a tuple, which can be converted to row later.
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If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
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rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
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:param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean,
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etc.), or :class:`list`, or :class:`pandas.DataFrame`.
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:param schema: a :class:`DataType` or a datatype string or a list of column names, default
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is None. The data type string format equals to `DataType.simpleString`, except that
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top level struct type can omit the `struct<>` and atomic types use `typeName()` as
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their format, e.g. use `byte` instead of `tinyint` for ByteType. We can also use `int`
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as a short name for IntegerType.
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:param samplingRatio: the sample ratio of rows used for inferring
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:return: :class:`DataFrame`
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.. versionchanged:: 2.0
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The schema parameter can be a DataType or a datatype string after 2.0. If it's not a
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StructType, it will be wrapped into a StructType and each record will also be wrapped
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into a tuple.
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>>> l = [('Alice', 1)]
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>>> sqlContext.createDataFrame(l).collect()
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[Row(_1=u'Alice', _2=1)]
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>>> sqlContext.createDataFrame(l, ['name', 'age']).collect()
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[Row(name=u'Alice', age=1)]
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>>> d = [{'name': 'Alice', 'age': 1}]
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>>> sqlContext.createDataFrame(d).collect()
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[Row(age=1, name=u'Alice')]
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>>> rdd = sc.parallelize(l)
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>>> sqlContext.createDataFrame(rdd).collect()
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[Row(_1=u'Alice', _2=1)]
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>>> df = sqlContext.createDataFrame(rdd, ['name', 'age'])
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>>> df.collect()
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[Row(name=u'Alice', age=1)]
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>>> from pyspark.sql import Row
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>>> Person = Row('name', 'age')
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>>> person = rdd.map(lambda r: Person(*r))
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>>> df2 = sqlContext.createDataFrame(person)
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>>> df2.collect()
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[Row(name=u'Alice', age=1)]
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>>> from pyspark.sql.types import *
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>>> schema = StructType([
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... StructField("name", StringType(), True),
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... StructField("age", IntegerType(), True)])
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>>> df3 = sqlContext.createDataFrame(rdd, schema)
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>>> df3.collect()
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[Row(name=u'Alice', age=1)]
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>>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
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[Row(name=u'Alice', age=1)]
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>>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
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[Row(0=1, 1=2)]
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>>> sqlContext.createDataFrame(rdd, "a: string, b: int").collect()
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[Row(a=u'Alice', b=1)]
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>>> rdd = rdd.map(lambda row: row[1])
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>>> sqlContext.createDataFrame(rdd, "int").collect()
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[Row(value=1)]
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>>> sqlContext.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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Py4JJavaError: ...
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"""
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if isinstance(data, DataFrame):
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raise TypeError("data is already a DataFrame")
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if isinstance(schema, basestring):
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schema = _parse_datatype_string(schema)
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if has_pandas and isinstance(data, pandas.DataFrame):
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if schema is None:
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schema = [str(x) for x in data.columns]
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data = [r.tolist() for r in data.to_records(index=False)]
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if isinstance(schema, StructType):
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def prepare(obj):
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_verify_type(obj, schema)
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return obj
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elif isinstance(schema, DataType):
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datatype = schema
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def prepare(obj):
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_verify_type(obj, datatype)
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return (obj, )
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schema = StructType().add("value", datatype)
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else:
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prepare = lambda obj: obj
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if isinstance(data, RDD):
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rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
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else:
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rdd, schema = self._createFromLocal(map(prepare, data), schema)
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jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
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jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
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df = DataFrame(jdf, self)
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df._schema = schema
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return df
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@since(1.3)
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def registerDataFrameAsTable(self, df, tableName):
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"""Registers the given :class:`DataFrame` as a temporary table in the catalog.
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Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`.
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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"""
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if (df.__class__ is DataFrame):
|
|
self._ssql_ctx.registerDataFrameAsTable(df._jdf, tableName)
|
|
else:
|
|
raise ValueError("Can only register DataFrame as table")
|
|
|
|
@since(1.6)
|
|
def dropTempTable(self, tableName):
|
|
""" Remove the temp table from catalog.
|
|
|
|
>>> sqlContext.registerDataFrameAsTable(df, "table1")
|
|
>>> sqlContext.dropTempTable("table1")
|
|
"""
|
|
self._ssql_ctx.dropTempTable(tableName)
|
|
|
|
@since(1.3)
|
|
def createExternalTable(self, tableName, path=None, source=None, schema=None, **options):
|
|
"""Creates an external table based on the dataset in a data source.
|
|
|
|
It returns the DataFrame associated with the external table.
|
|
|
|
The data source is specified by the ``source`` and a set of ``options``.
|
|
If ``source`` is not specified, the default data source configured by
|
|
``spark.sql.sources.default`` will be used.
|
|
|
|
Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and
|
|
created external table.
|
|
|
|
:return: :class:`DataFrame`
|
|
"""
|
|
if path is not None:
|
|
options["path"] = path
|
|
if source is None:
|
|
source = self.getConf("spark.sql.sources.default",
|
|
"org.apache.spark.sql.parquet")
|
|
if schema is None:
|
|
df = self._ssql_ctx.createExternalTable(tableName, source, options)
|
|
else:
|
|
if not isinstance(schema, StructType):
|
|
raise TypeError("schema should be StructType")
|
|
scala_datatype = self._ssql_ctx.parseDataType(schema.json())
|
|
df = self._ssql_ctx.createExternalTable(tableName, source, scala_datatype,
|
|
options)
|
|
return DataFrame(df, self)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.0)
|
|
def sql(self, sqlQuery):
|
|
"""Returns a :class:`DataFrame` representing the result of the given query.
|
|
|
|
:return: :class:`DataFrame`
|
|
|
|
>>> sqlContext.registerDataFrameAsTable(df, "table1")
|
|
>>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1")
|
|
>>> df2.collect()
|
|
[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)
|
|
|
|
@since(1.0)
|
|
def table(self, tableName):
|
|
"""Returns the specified table as a :class:`DataFrame`.
|
|
|
|
:return: :class:`DataFrame`
|
|
|
|
>>> sqlContext.registerDataFrameAsTable(df, "table1")
|
|
>>> df2 = sqlContext.table("table1")
|
|
>>> sorted(df.collect()) == sorted(df2.collect())
|
|
True
|
|
"""
|
|
return DataFrame(self._ssql_ctx.table(tableName), self)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def tables(self, dbName=None):
|
|
"""Returns a :class:`DataFrame` containing names of tables in the given database.
|
|
|
|
If ``dbName`` is not specified, the current database will be used.
|
|
|
|
The returned DataFrame has two columns: ``tableName`` and ``isTemporary``
|
|
(a column with :class:`BooleanType` indicating if a table is a temporary one or not).
|
|
|
|
:param dbName: string, name of the database to use.
|
|
:return: :class:`DataFrame`
|
|
|
|
>>> sqlContext.registerDataFrameAsTable(df, "table1")
|
|
>>> df2 = sqlContext.tables()
|
|
>>> df2.filter("tableName = 'table1'").first()
|
|
Row(tableName=u'table1', isTemporary=True)
|
|
"""
|
|
if dbName is None:
|
|
return DataFrame(self._ssql_ctx.tables(), self)
|
|
else:
|
|
return DataFrame(self._ssql_ctx.tables(dbName), self)
|
|
|
|
@since(1.3)
|
|
def tableNames(self, dbName=None):
|
|
"""Returns a list of names of tables in the database ``dbName``.
|
|
|
|
:param dbName: string, name of the database to use. Default to the current database.
|
|
:return: list of table names, in string
|
|
|
|
>>> sqlContext.registerDataFrameAsTable(df, "table1")
|
|
>>> "table1" in sqlContext.tableNames()
|
|
True
|
|
>>> "table1" in sqlContext.tableNames("default")
|
|
True
|
|
"""
|
|
if dbName is None:
|
|
return [name for name in self._ssql_ctx.tableNames()]
|
|
else:
|
|
return [name for name in self._ssql_ctx.tableNames(dbName)]
|
|
|
|
@since(1.0)
|
|
def cacheTable(self, tableName):
|
|
"""Caches the specified table in-memory."""
|
|
self._ssql_ctx.cacheTable(tableName)
|
|
|
|
@since(1.0)
|
|
def uncacheTable(self, tableName):
|
|
"""Removes the specified table from the in-memory cache."""
|
|
self._ssql_ctx.uncacheTable(tableName)
|
|
|
|
@since(1.3)
|
|
def clearCache(self):
|
|
"""Removes all cached tables from the in-memory cache. """
|
|
self._ssql_ctx.clearCache()
|
|
|
|
@property
|
|
@since(1.4)
|
|
def read(self):
|
|
"""
|
|
Returns a :class:`DataFrameReader` that can be used to read data
|
|
in as a :class:`DataFrame`.
|
|
|
|
:return: :class:`DataFrameReader`
|
|
"""
|
|
return DataFrameReader(self)
|
|
|
|
|
|
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.
|
|
|
|
:param sparkContext: The SparkContext to wrap.
|
|
:param hiveContext: An optional JVM Scala HiveContext. If set, we do not instantiate a new
|
|
:class:`HiveContext` in the JVM, instead we make all calls to this object.
|
|
"""
|
|
|
|
def __init__(self, sparkContext, hiveContext=None):
|
|
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:
|
|
print("You must build Spark with Hive. "
|
|
"Export 'SPARK_HIVE=true' and run "
|
|
"build/sbt assembly", file=sys.stderr)
|
|
raise
|
|
|
|
def _get_hive_ctx(self):
|
|
return self._jvm.HiveContext(self._jsc.sc())
|
|
|
|
def refreshTable(self, tableName):
|
|
"""Invalidate and refresh all the cached the metadata of the given
|
|
table. For performance reasons, Spark SQL or the external data source
|
|
library it uses might cache certain metadata about a table, such as the
|
|
location of blocks. When those change outside of Spark SQL, users should
|
|
call this function to invalidate the cache.
|
|
"""
|
|
self._ssql_ctx.refreshTable(tableName)
|
|
|
|
|
|
class UDFRegistration(object):
|
|
"""Wrapper for user-defined function registration."""
|
|
|
|
def __init__(self, sqlContext):
|
|
self.sqlContext = sqlContext
|
|
|
|
def register(self, name, f, returnType=StringType()):
|
|
return self.sqlContext.registerFunction(name, f, returnType)
|
|
|
|
register.__doc__ = SQLContext.registerFunction.__doc__
|
|
|
|
|
|
def _test():
|
|
import os
|
|
import doctest
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import Row, SQLContext
|
|
import pyspark.sql.context
|
|
|
|
os.chdir(os.environ["SPARK_HOME"])
|
|
|
|
globs = pyspark.sql.context.__dict__.copy()
|
|
sc = SparkContext('local[4]', 'PythonTest')
|
|
globs['sc'] = sc
|
|
globs['sqlContext'] = SQLContext(sc)
|
|
globs['rdd'] = rdd = sc.parallelize(
|
|
[Row(field1=1, field2="row1"),
|
|
Row(field1=2, field2="row2"),
|
|
Row(field1=3, field2="row3")]
|
|
)
|
|
globs['df'] = rdd.toDF()
|
|
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.context, globs=globs,
|
|
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|