f76d2e55b1
This PR create an alias for `registerFunction` as `udf.register`, to be consistent with Scala API. It also deprecated inferSchema() and applySchema(), show an warning for them. cc rxin Author: Davies Liu <davies@databricks.com> Closes #5273 from davies/udf and squashes the following commits: 476e947 [Davies Liu] address comments c096fdb [Davies Liu] add SQLContext.udf and deprecate inferSchema() and applySchema
690 lines
26 KiB
Python
690 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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import warnings
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import json
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from itertools import imap
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from py4j.protocol import Py4JError
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from py4j.java_collections import MapConverter
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from pyspark.rdd import RDD, _prepare_for_python_RDD
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from pyspark.serializers import AutoBatchedSerializer, PickleSerializer
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from pyspark.sql.types import Row, StringType, StructType, _verify_type, \
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_infer_schema, _has_nulltype, _merge_type, _create_converter, _python_to_sql_converter
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from pyspark.sql.dataframe import DataFrame
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try:
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import pandas
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has_pandas = True
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except ImportError:
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has_pandas = False
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__all__ = ["SQLContext", "HiveContext", "UDFRegistration"]
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def _monkey_patch_RDD(sqlCtx):
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def toDF(self, schema=None, sampleRatio=None):
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"""
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Convert current :class:`RDD` into a :class:`DataFrame`
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This is a shorthand for `sqlCtx.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 sqlCtx.createDataFrame(self, schema, sampleRatio)
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RDD.toDF = toDF
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class UDFRegistration(object):
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"""Wrapper for register UDF"""
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def __init__(self, sqlCtx):
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self.sqlCtx = sqlCtx
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def register(self, name, f, returnType=StringType()):
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"""Registers a lambda function as a UDF 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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>>> sqlCtx.udf.register("stringLengthString", lambda x: len(x))
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>>> sqlCtx.sql("SELECT stringLengthString('test')").collect()
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[Row(c0=u'4')]
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>>> from pyspark.sql.types import IntegerType
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>>> sqlCtx.udf.register("stringLengthInt", lambda x: len(x), IntegerType())
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>>> sqlCtx.sql("SELECT stringLengthInt('test')").collect()
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[Row(c0=4)]
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"""
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return self.sqlCtx.registerFunction(name, f, returnType)
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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 L{DataFrame}, register L{DataFrame} as
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tables, execute SQL over tables, cache tables, and read parquet files.
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"""
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def __init__(self, sparkContext, sqlContext=None):
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"""Create a new SQLContext.
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It will add a method called `toDF` to :class:`RDD`, which could be
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used to convert an RDD into a DataFrame, it's a shorthand for
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:func:`SQLContext.createDataFrame`.
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:param sparkContext: The SparkContext to wrap.
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:param sqlContext: An optional JVM Scala SQLContext. If set, we do not instatiate a new
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SQLContext in the JVM, instead we make all calls to this object.
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>>> from datetime import datetime
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>>> sqlCtx = SQLContext(sc)
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>>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L,
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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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>>> sqlCtx.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(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)]
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>>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time,
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... x.row.a, x.list)).collect()
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[(1, u'string', 1.0, 1, True, ...(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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@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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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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def getConf(self, key, defaultValue):
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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, returns defaultValue.
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"""
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return self._ssql_ctx.getConf(key, defaultValue)
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@property
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def udf(self):
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"""Wrapper for register Python function as UDF """
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return UDFRegistration(self)
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def registerFunction(self, name, f, returnType=StringType()):
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"""Registers a lambda function as a UDF 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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>>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x))
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>>> sqlCtx.sql("SELECT stringLengthString('test')").collect()
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[Row(c0=u'4')]
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>>> from pyspark.sql.types import IntegerType
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>>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType())
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>>> sqlCtx.sql("SELECT stringLengthInt('test')").collect()
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[Row(c0=4)]
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"""
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func = lambda _, it: imap(lambda x: f(*x), it)
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ser = AutoBatchedSerializer(PickleSerializer())
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command = (func, None, ser, ser)
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pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self._sc, command, self)
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self._ssql_ctx.udf().registerPython(name,
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bytearray(pickled_cmd),
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env,
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includes,
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self._sc.pythonExec,
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bvars,
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self._sc._javaAccumulator,
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returnType.json())
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def _inferSchema(self, rdd, samplingRatio=None):
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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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"please 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 inferSchema(self, rdd, samplingRatio=None):
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"""Infer and apply a schema to an RDD of L{Row}.
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::note:
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Deprecated in 1.3, use :func:`createDataFrame` instead
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When samplingRatio is specified, the schema is inferred by looking
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at the types of each row in the sampled dataset. Otherwise, the
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first 100 rows of the RDD are inspected. Nested collections are
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supported, which can include array, dict, list, Row, tuple,
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namedtuple, or object.
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Each row could be L{pyspark.sql.Row} object or namedtuple or objects.
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Using top level dicts is deprecated, as dict is used to represent Maps.
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If a single column has multiple distinct inferred types, it may cause
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runtime exceptions.
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>>> rdd = sc.parallelize(
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... [Row(field1=1, field2="row1"),
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... Row(field1=2, field2="row2"),
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... Row(field1=3, field2="row3")])
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>>> df = sqlCtx.inferSchema(rdd)
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>>> df.collect()[0]
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Row(field1=1, field2=u'row1')
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"""
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warnings.warn("inferSchema is deprecated, please use createDataFrame instead")
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if isinstance(rdd, DataFrame):
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raise TypeError("Cannot apply schema to DataFrame")
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return self.createDataFrame(rdd, None, samplingRatio)
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def applySchema(self, rdd, schema):
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"""
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Applies the given schema to the given RDD of L{tuple} or L{list}.
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::note:
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Deprecated in 1.3, use :func:`createDataFrame` instead
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These tuples or lists can contain complex nested structures like
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lists, maps or nested rows.
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The schema should be a StructType.
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It is important that the schema matches the types of the objects
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in each row or exceptions could be thrown at runtime.
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>>> from pyspark.sql.types import *
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>>> rdd2 = sc.parallelize([(1, "row1"), (2, "row2"), (3, "row3")])
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>>> schema = StructType([StructField("field1", IntegerType(), False),
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... StructField("field2", StringType(), False)])
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>>> df = sqlCtx.applySchema(rdd2, schema)
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>>> df.collect()
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[Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')]
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"""
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warnings.warn("applySchema is deprecated, please use createDataFrame instead")
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if isinstance(rdd, DataFrame):
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raise TypeError("Cannot apply schema to DataFrame")
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if not isinstance(schema, StructType):
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raise TypeError("schema should be StructType, but got %s" % schema)
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return self.createDataFrame(rdd, schema)
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def createDataFrame(self, data, schema=None, samplingRatio=None):
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"""
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Create a DataFrame from an RDD of tuple/list, list or pandas.DataFrame.
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`schema` could be :class:`StructType` or a list of column names.
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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 `rdd`.
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When `schema` is None, it will try to infer the column name and type
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from `rdd`, which should be an RDD of :class:`Row`, or namedtuple,
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or dict.
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If referring needed, `samplingRatio` is used to determined how many
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rows will be used to do referring. The first row will be used if
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`samplingRatio` is None.
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:param data: an RDD of Row/tuple/list/dict, list, or pandas.DataFrame
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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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>>> l = [('Alice', 1)]
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>>> sqlCtx.createDataFrame(l).collect()
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[Row(_1=u'Alice', _2=1)]
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>>> sqlCtx.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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>>> sqlCtx.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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>>> sqlCtx.createDataFrame(rdd).collect()
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[Row(_1=u'Alice', _2=1)]
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>>> df = sqlCtx.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 = sqlCtx.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 = sqlCtx.createDataFrame(rdd, schema)
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>>> df3.collect()
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[Row(name=u'Alice', age=1)]
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>>> sqlCtx.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
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[Row(name=u'Alice', age=1)]
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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 has_pandas and isinstance(data, pandas.DataFrame):
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if schema is None:
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schema = list(data.columns)
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data = [r.tolist() for r in data.to_records(index=False)]
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if not isinstance(data, RDD):
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try:
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# data could be list, tuple, generator ...
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rdd = self._sc.parallelize(data)
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except Exception:
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raise ValueError("cannot create an RDD from type: %s" % type(data))
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else:
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rdd = data
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if schema is None:
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schema = self._inferSchema(rdd, samplingRatio)
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converter = _create_converter(schema)
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rdd = rdd.map(converter)
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if isinstance(schema, (list, tuple)):
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first = rdd.first()
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if not isinstance(first, (list, tuple)):
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raise ValueError("each row in `rdd` should be list or tuple, "
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"but got %r" % type(first))
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row_cls = Row(*schema)
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schema = self._inferSchema(rdd.map(lambda r: row_cls(*r)), samplingRatio)
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# take the first few rows to verify schema
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rows = rdd.take(10)
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# Row() cannot been deserialized by Pyrolite
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if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row':
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rdd = rdd.map(tuple)
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rows = rdd.take(10)
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for row in rows:
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_verify_type(row, schema)
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# convert python objects to sql data
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converter = _python_to_sql_converter(schema)
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rdd = rdd.map(converter)
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jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
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df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
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return DataFrame(df, self)
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def registerDataFrameAsTable(self, rdd, tableName):
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"""Registers the given RDD as a temporary table in the catalog.
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Temporary tables exist only during the lifetime of this instance of
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SQLContext.
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>>> sqlCtx.registerDataFrameAsTable(df, "table1")
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"""
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if (rdd.__class__ is DataFrame):
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df = rdd._jdf
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self._ssql_ctx.registerDataFrameAsTable(df, tableName)
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else:
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raise ValueError("Can only register DataFrame as table")
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def parquetFile(self, *paths):
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"""Loads a Parquet file, returning the result as a L{DataFrame}.
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>>> import tempfile, shutil
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>>> parquetFile = tempfile.mkdtemp()
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>>> shutil.rmtree(parquetFile)
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>>> df.saveAsParquetFile(parquetFile)
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>>> df2 = sqlCtx.parquetFile(parquetFile)
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>>> sorted(df.collect()) == sorted(df2.collect())
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True
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"""
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gateway = self._sc._gateway
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jpaths = gateway.new_array(gateway.jvm.java.lang.String, len(paths))
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for i in range(0, len(paths)):
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jpaths[i] = paths[i]
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jdf = self._ssql_ctx.parquetFile(jpaths)
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return DataFrame(jdf, self)
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def jsonFile(self, path, schema=None, samplingRatio=1.0):
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"""
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Loads a text file storing one JSON object per line as a
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L{DataFrame}.
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If the schema is provided, applies the given schema to this
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JSON dataset.
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Otherwise, it samples the dataset with ratio `samplingRatio` to
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determine the schema.
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>>> import tempfile, shutil
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>>> jsonFile = tempfile.mkdtemp()
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>>> shutil.rmtree(jsonFile)
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>>> with open(jsonFile, 'w') as f:
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... f.writelines(jsonStrings)
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>>> df1 = sqlCtx.jsonFile(jsonFile)
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>>> df1.printSchema()
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root
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|-- field1: long (nullable = true)
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|-- field2: string (nullable = true)
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|-- field3: struct (nullable = true)
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| |-- field4: long (nullable = true)
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>>> from pyspark.sql.types import *
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>>> schema = StructType([
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... StructField("field2", StringType()),
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... StructField("field3",
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... StructType([StructField("field5", ArrayType(IntegerType()))]))])
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>>> df2 = sqlCtx.jsonFile(jsonFile, schema)
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>>> df2.printSchema()
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root
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|-- field2: string (nullable = true)
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|-- field3: struct (nullable = true)
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| |-- field5: array (nullable = true)
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| | |-- element: integer (containsNull = true)
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"""
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if schema is None:
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df = self._ssql_ctx.jsonFile(path, samplingRatio)
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else:
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scala_datatype = self._ssql_ctx.parseDataType(schema.json())
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df = self._ssql_ctx.jsonFile(path, scala_datatype)
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return DataFrame(df, self)
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def jsonRDD(self, rdd, schema=None, samplingRatio=1.0):
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"""Loads an RDD storing one JSON object per string as a L{DataFrame}.
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If the schema is provided, applies the given schema to this
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JSON dataset.
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Otherwise, it samples the dataset with ratio `samplingRatio` to
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determine the schema.
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>>> df1 = sqlCtx.jsonRDD(json)
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>>> df1.first()
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Row(field1=1, field2=u'row1', field3=Row(field4=11, field5=None), field6=None)
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>>> df2 = sqlCtx.jsonRDD(json, df1.schema)
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>>> df2.first()
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Row(field1=1, field2=u'row1', field3=Row(field4=11, field5=None), field6=None)
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|
|
>>> from pyspark.sql.types import *
|
|
>>> schema = StructType([
|
|
... StructField("field2", StringType()),
|
|
... StructField("field3",
|
|
... StructType([StructField("field5", ArrayType(IntegerType()))]))
|
|
... ])
|
|
>>> df3 = sqlCtx.jsonRDD(json, schema)
|
|
>>> df3.first()
|
|
Row(field2=u'row1', field3=Row(field5=None))
|
|
|
|
"""
|
|
|
|
def func(iterator):
|
|
for x in iterator:
|
|
if not isinstance(x, basestring):
|
|
x = unicode(x)
|
|
if isinstance(x, unicode):
|
|
x = x.encode("utf-8")
|
|
yield x
|
|
keyed = rdd.mapPartitions(func)
|
|
keyed._bypass_serializer = True
|
|
jrdd = keyed._jrdd.map(self._jvm.BytesToString())
|
|
if schema is None:
|
|
df = self._ssql_ctx.jsonRDD(jrdd.rdd(), samplingRatio)
|
|
else:
|
|
scala_datatype = self._ssql_ctx.parseDataType(schema.json())
|
|
df = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
|
|
return DataFrame(df, self)
|
|
|
|
def load(self, path=None, source=None, schema=None, **options):
|
|
"""Returns the dataset in a data source as a DataFrame.
|
|
|
|
The data source is specified by the `source` and a set of `options`.
|
|
If `source` is not specified, the default data source configured by
|
|
spark.sql.sources.default will be used.
|
|
|
|
Optionally, a schema can be provided as the schema of the returned DataFrame.
|
|
"""
|
|
if path is not None:
|
|
options["path"] = path
|
|
if source is None:
|
|
source = self.getConf("spark.sql.sources.default",
|
|
"org.apache.spark.sql.parquet")
|
|
joptions = MapConverter().convert(options,
|
|
self._sc._gateway._gateway_client)
|
|
if schema is None:
|
|
df = self._ssql_ctx.load(source, joptions)
|
|
else:
|
|
if not isinstance(schema, StructType):
|
|
raise TypeError("schema should be StructType")
|
|
scala_datatype = self._ssql_ctx.parseDataType(schema.json())
|
|
df = self._ssql_ctx.load(source, scala_datatype, joptions)
|
|
return DataFrame(df, self)
|
|
|
|
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 DataFrame and
|
|
created external table.
|
|
"""
|
|
if path is not None:
|
|
options["path"] = path
|
|
if source is None:
|
|
source = self.getConf("spark.sql.sources.default",
|
|
"org.apache.spark.sql.parquet")
|
|
joptions = MapConverter().convert(options,
|
|
self._sc._gateway._gateway_client)
|
|
if schema is None:
|
|
df = self._ssql_ctx.createExternalTable(tableName, source, joptions)
|
|
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,
|
|
joptions)
|
|
return DataFrame(df, self)
|
|
|
|
def sql(self, sqlQuery):
|
|
"""Return a L{DataFrame} representing the result of the given query.
|
|
|
|
>>> sqlCtx.registerDataFrameAsTable(df, "table1")
|
|
>>> df2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
|
|
>>> df2.collect()
|
|
[Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
|
|
"""
|
|
return DataFrame(self._ssql_ctx.sql(sqlQuery), self)
|
|
|
|
def table(self, tableName):
|
|
"""Returns the specified table as a L{DataFrame}.
|
|
|
|
>>> sqlCtx.registerDataFrameAsTable(df, "table1")
|
|
>>> df2 = sqlCtx.table("table1")
|
|
>>> sorted(df.collect()) == sorted(df2.collect())
|
|
True
|
|
"""
|
|
return DataFrame(self._ssql_ctx.table(tableName), self)
|
|
|
|
def tables(self, dbName=None):
|
|
"""Returns a 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 BooleanType indicating if a table is a temporary one or not).
|
|
|
|
>>> sqlCtx.registerDataFrameAsTable(df, "table1")
|
|
>>> df2 = sqlCtx.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)
|
|
|
|
def tableNames(self, dbName=None):
|
|
"""Returns a list of names of tables in the database `dbName`.
|
|
|
|
If `dbName` is not specified, the current database will be used.
|
|
|
|
>>> sqlCtx.registerDataFrameAsTable(df, "table1")
|
|
>>> "table1" in sqlCtx.tableNames()
|
|
True
|
|
>>> "table1" in sqlCtx.tableNames("db")
|
|
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)]
|
|
|
|
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)
|
|
|
|
def clearCache(self):
|
|
"""Removes all cached tables from the in-memory cache. """
|
|
self._ssql_ctx.clearCache()
|
|
|
|
|
|
class HiveContext(SQLContext):
|
|
|
|
"""A variant of Spark SQL that integrates with data stored in Hive.
|
|
|
|
Configuration for Hive is read from hive-site.xml on the classpath.
|
|
It supports running both SQL and HiveQL commands.
|
|
"""
|
|
|
|
def __init__(self, sparkContext, hiveContext=None):
|
|
"""Create a new HiveContext.
|
|
|
|
:param sparkContext: The SparkContext to wrap.
|
|
:param hiveContext: An optional JVM Scala HiveContext. If set, we do not instatiate a new
|
|
HiveContext in the JVM, instead we make all calls to this object.
|
|
"""
|
|
SQLContext.__init__(self, sparkContext)
|
|
|
|
if hiveContext:
|
|
self._scala_HiveContext = hiveContext
|
|
|
|
@property
|
|
def _ssql_ctx(self):
|
|
try:
|
|
if not hasattr(self, '_scala_HiveContext'):
|
|
self._scala_HiveContext = self._get_hive_ctx()
|
|
return self._scala_HiveContext
|
|
except Py4JError as e:
|
|
raise Exception("You must build Spark with Hive. "
|
|
"Export 'SPARK_HIVE=true' and run "
|
|
"build/sbt assembly", e)
|
|
|
|
def _get_hive_ctx(self):
|
|
return self._jvm.HiveContext(self._jsc.sc())
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import Row, SQLContext
|
|
import pyspark.sql.context
|
|
globs = pyspark.sql.context.__dict__.copy()
|
|
sc = SparkContext('local[4]', 'PythonTest')
|
|
globs['sc'] = sc
|
|
globs['sqlCtx'] = sqlCtx = SQLContext(sc)
|
|
globs['rdd'] = rdd = sc.parallelize(
|
|
[Row(field1=1, field2="row1"),
|
|
Row(field1=2, field2="row2"),
|
|
Row(field1=3, field2="row3")]
|
|
)
|
|
_monkey_patch_RDD(sqlCtx)
|
|
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()
|