text: #
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  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  from __future__ import print_function
  import sys
  import warnings
  
  if sys.version >= '3':
      basestring = unicode = str
  
  from pyspark import since
  from pyspark.rdd import ignore_unicode_prefix
  from pyspark.sql.session import _monkey_patch_RDD, SparkSession
  from pyspark.sql.dataframe import DataFrame
  from pyspark.sql.readwriter import DataFrameReader
  from pyspark.sql.streaming import DataStreamReader
  from pyspark.sql.types import Row, StringType
  from pyspark.sql.utils import install_exception_handler
  
  __all__ = ["SQLContext", "HiveContext", "UDFRegistration"]
  
  
  class SQLContext(object):
      """The entry point for working with structured data (rows and columns) in Spark, in Spark 1.x.
  
      As of Spark 2.0, this is replaced by :class:`SparkSession`. However, we are keeping the class
      here for backward compatibility.
  
      A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as
      tables, execute SQL over tables, cache tables, and read parquet files.
  
      :param sparkContext: The :class:`SparkContext` backing this SQLContext.
      :param sparkSession: The :class:`SparkSession` around which this SQLContext wraps.
      :param jsqlContext: An optional JVM Scala SQLContext. If set, we do not instantiate a new
          SQLContext in the JVM, instead we make all calls to this object.
      """
  
      _instantiatedContext = None
  
      @ignore_unicode_prefix
      def __init__(self, sparkContext, sparkSession=None, jsqlContext=None):
          """Creates a new SQLContext.
  
          >>> from datetime import datetime
          >>> sqlContext = SQLContext(sc)
          >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1,
          ...     b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
          ...     time=datetime(2014, 8, 1, 14, 1, 5))])
          >>> df = allTypes.toDF()
          >>> df.createOrReplaceTempView("allTypes")
          >>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
          ...            'from allTypes where b and i > 0').collect()
          [Row((i + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \
              dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
          >>> df.rdd.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, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
          """
          self._sc = sparkContext
          self._jsc = self._sc._jsc
          self._jvm = self._sc._jvm
          if sparkSession is None:
              sparkSession = SparkSession(sparkContext)
          if jsqlContext is None:
              jsqlContext = sparkSession._jwrapped
          self.sparkSession = sparkSession
          self._jsqlContext = jsqlContext
          _monkey_patch_RDD(self.sparkSession)
          install_exception_handler()
          if SQLContext._instantiatedContext is None:
              SQLContext._instantiatedContext = self
  
      @property
      def _ssql_ctx(self):
          """Accessor for the JVM Spark SQL context.
  
          Subclasses can override this property to provide their own
          JVM Contexts.
          """
          return self._jsqlContext
  
      @classmethod
      @since(1.6)
      def getOrCreate(cls, sc):
          """
          Get the existing SQLContext or create a new one with given SparkContext.
  
          :param sc: SparkContext
          """
          if cls._instantiatedContext is None:
              jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
              sparkSession = SparkSession(sc, jsqlContext.sparkSession())
              cls(sc, sparkSession, jsqlContext)
          return cls._instantiatedContext
  
      @since(1.6)
      def newSession(self):
          """
          Returns a new SQLContext as new session, that has separate SQLConf,
          registered temporary views and UDFs, but shared SparkContext and
          table cache.
          """
          return self.__class__(self._sc, self.sparkSession.newSession())
  
      @since(1.3)
      def setConf(self, key, value):
          """Sets the given Spark SQL configuration property.
          """
          self.sparkSession.conf.set(key, value)
  
      @ignore_unicode_prefix
      @since(1.3)
      def getConf(self, key, defaultValue=None):
          """Returns the value of Spark SQL configuration property for the given key.
  
          If the key is not set and defaultValue is not None, return
          defaultValue. If the key is not set and defaultValue is None, return
          the system default value.
  
          >>> sqlContext.getConf("spark.sql.shuffle.partitions")
          u'200'
          >>> sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
          u'10'
          >>> sqlContext.setConf("spark.sql.shuffle.partitions", u"50")
          >>> sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
          u'50'
          """
          return self.sparkSession.conf.get(key, defaultValue)
  
      @property
      @since("1.3.1")
      def udf(self):
          """Returns a :class:`UDFRegistration` for UDF registration.
  
          :return: :class:`UDFRegistration`
          """
          return UDFRegistration(self)
  
      @since(1.4)
      def range(self, start, end=None, step=1, numPartitions=None):
          """
          Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
          ``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
          step value ``step``.
  
          :param start: the start value
          :param end: the end value (exclusive)
          :param step: the incremental step (default: 1)
          :param numPartitions: the number of partitions of the DataFrame
          :return: :class:`DataFrame`
  
          >>> sqlContext.range(1, 7, 2).collect()
          [Row(id=1), Row(id=3), Row(id=5)]
  
          If only one argument is specified, it will be used as the end value.
  
          >>> sqlContext.range(3).collect()
          [Row(id=0), Row(id=1), Row(id=2)]
          """
          return self.sparkSession.range(start, end, step, numPartitions)
  
      @ignore_unicode_prefix
      @since(1.2)
      def registerFunction(self, name, f, returnType=StringType()):
          """Registers a python function (including lambda function) as a UDF
          so it can be used in SQL statements.
  
          In addition to a name and the function itself, the return type can be optionally specified.
          When the return type is not given it default to a string and conversion will automatically
          be done.  For any other return type, the produced object must match the specified type.
  
          :param name: name of the UDF
          :param f: python function
          :param returnType: a :class:`pyspark.sql.types.DataType` object
  
          >>> sqlContext.registerFunction("stringLengthString", lambda x: len(x))
          >>> sqlContext.sql("SELECT stringLengthString('test')").collect()
          [Row(stringLengthString(test)=u'4')]
  
          >>> from pyspark.sql.types import IntegerType
          >>> sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType())
          >>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
          [Row(stringLengthInt(test)=4)]
  
          >>> from pyspark.sql.types import IntegerType
          >>> sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType())
          >>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
          [Row(stringLengthInt(test)=4)]
          """
          self.sparkSession.catalog.registerFunction(name, f, returnType)
  
      # TODO(andrew): delete this once we refactor things to take in SparkSession
      def _inferSchema(self, rdd, samplingRatio=None):
          """
          Infer schema from an RDD of Row or tuple.
  
          :param rdd: an RDD of Row or tuple
          :param samplingRatio: sampling ratio, or no sampling (default)
          :return: :class:`pyspark.sql.types.StructType`
          """
          return self.sparkSession._inferSchema(rdd, samplingRatio)
  
      @since(1.3)
      @ignore_unicode_prefix
      def createDataFrame(self, data, schema=None, samplingRatio=None):
          """
          Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
  
          When ``schema`` is a list of column names, the type of each column
          will be inferred from ``data``.
  
          When ``schema`` is ``None``, it will try to infer the schema (column names and types)
          from ``data``, which should be an RDD of :class:`Row`,
          or :class:`namedtuple`, or :class:`dict`.
  
          When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string it must match
          the real data, or an exception will be thrown at runtime. If the given schema is not
          :class:`pyspark.sql.types.StructType`, it will be wrapped into a
          :class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
          each record will also be wrapped into a tuple, which can be converted to row later.
  
          If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
          rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
  
          :param data: an RDD of any kind of SQL data representation(e.g. :class:`Row`,
              :class:`tuple`, ``int``, ``boolean``, etc.), or :class:`list`, or
              :class:`pandas.DataFrame`.
          :param schema: a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
              column names, default is None.  The data type string format equals to
              :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
              omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
              ``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
              We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
          :param samplingRatio: the sample ratio of rows used for inferring
          :return: :class:`DataFrame`
  
          .. versionchanged:: 2.0
             The ``schema`` parameter can be a :class:`pyspark.sql.types.DataType` or a
             datatype string after 2.0.
             If it's not a :class:`pyspark.sql.types.StructType`, it will be wrapped into a
             :class:`pyspark.sql.types.StructType` and each record will also be wrapped into a tuple.
  
          >>> l = [('Alice', 1)]
          >>> sqlContext.createDataFrame(l).collect()
          [Row(_1=u'Alice', _2=1)]
          >>> sqlContext.createDataFrame(l, ['name', 'age']).collect()
          [Row(name=u'Alice', age=1)]
  
          >>> d = [{'name': 'Alice', 'age': 1}]
          >>> sqlContext.createDataFrame(d).collect()
          [Row(age=1, name=u'Alice')]
  
          >>> rdd = sc.parallelize(l)
          >>> sqlContext.createDataFrame(rdd).collect()
          [Row(_1=u'Alice', _2=1)]
          >>> df = sqlContext.createDataFrame(rdd, ['name', 'age'])
          >>> df.collect()
          [Row(name=u'Alice', age=1)]
  
          >>> from pyspark.sql import Row
          >>> Person = Row('name', 'age')
          >>> person = rdd.map(lambda r: Person(*r))
          >>> df2 = sqlContext.createDataFrame(person)
          >>> df2.collect()
          [Row(name=u'Alice', age=1)]
  
          >>> from pyspark.sql.types import *
          >>> schema = StructType([
          ...    StructField("name", StringType(), True),
          ...    StructField("age", IntegerType(), True)])
          >>> df3 = sqlContext.createDataFrame(rdd, schema)
          >>> df3.collect()
          [Row(name=u'Alice', age=1)]
  
          >>> sqlContext.createDataFrame(df.toPandas()).collect()  # doctest: +SKIP
          [Row(name=u'Alice', age=1)]
          >>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]])).collect()  # doctest: +SKIP
          [Row(0=1, 1=2)]
  
          >>> sqlContext.createDataFrame(rdd, "a: string, b: int").collect()
          [Row(a=u'Alice', b=1)]
          >>> rdd = rdd.map(lambda row: row[1])
          >>> sqlContext.createDataFrame(rdd, "int").collect()
          [Row(value=1)]
          >>> sqlContext.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
          Traceback (most recent call last):
              ...
          Py4JJavaError: ...
          """
          return self.sparkSession.createDataFrame(data, schema, samplingRatio)
  
      @since(1.3)
      def registerDataFrameAsTable(self, df, tableName):
          """Registers the given :class:`DataFrame` as a temporary table in the catalog.
  
          Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`.
  
          >>> sqlContext.registerDataFrameAsTable(df, "table1")
          """
          df.createOrReplaceTempView(tableName)
  
      @since(1.6)
      def dropTempTable(self, tableName):
          """ Remove the temp table from catalog.
  
          >>> sqlContext.registerDataFrameAsTable(df, "table1")
          >>> sqlContext.dropTempTable("table1")
          """
          self.sparkSession.catalog.dropTempView(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`
          """
          return self.sparkSession.catalog.createExternalTable(
              tableName, path, source, schema, **options)
  
      @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 self.sparkSession.sql(sqlQuery)
  
      @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 self.sparkSession.table(tableName)
  
      @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)
  
      @property
      @since(2.0)
      def readStream(self):
          """
          Returns a :class:`DataStreamReader` that can be used to read data streams
          as a streaming :class:`DataFrame`.
  
          .. note:: Experimental.
  
          :return: :class:`DataStreamReader`
  
          >>> text_sdf = sqlContext.readStream.text(tempfile.mkdtemp())
          >>> text_sdf.isStreaming
          True
          """
          return DataStreamReader(self)
  
      @property
      @since(2.0)
      def streams(self):
          """Returns a :class:`StreamingQueryManager` that allows managing all the
          :class:`StreamingQuery` StreamingQueries active on `this` context.
  
          .. note:: Experimental.
          """
          from pyspark.sql.streaming import StreamingQueryManager
          return StreamingQueryManager(self._ssql_ctx.streams())
  
  
  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 jhiveContext: 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.
  
      .. note:: Deprecated in 2.0.0. Use SparkSession.builder.enableHiveSupport().getOrCreate().
      """
  
      def __init__(self, sparkContext, jhiveContext=None):
          warnings.warn(
              "HiveContext is deprecated in Spark 2.0.0. Please use " +
              "SparkSession.builder.enableHiveSupport().getOrCreate() instead.",
              DeprecationWarning)
          if jhiveContext is None:
              sparkSession = SparkSession.builder.enableHiveSupport().getOrCreate()
          else:
              sparkSession = SparkSession(sparkContext, jhiveContext.sparkSession())
          SQLContext.__init__(self, sparkContext, sparkSession, jhiveContext)
  
      @classmethod
      def _createForTesting(cls, sparkContext):
          """(Internal use only) Create a new HiveContext for testing.
  
          All test code that touches HiveContext *must* go through this method. Otherwise,
          you may end up launching multiple derby instances and encounter with incredibly
          confusing error messages.
          """
          jsc = sparkContext._jsc.sc()
          jtestHive = sparkContext._jvm.org.apache.spark.sql.hive.test.TestHiveContext(jsc, False)
          return cls(sparkContext, jtestHive)
  
      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
      import tempfile
      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['tempfile'] = tempfile
      globs['os'] = os
      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()
  