# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys import warnings from typing import ( Optional, Union, Callable, Any, Iterable, List, Tuple, overload, Type, ClassVar, TYPE_CHECKING, cast, ) from pyspark import _NoValue from pyspark._globals import _NoValueType 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.udf import UDFRegistration from pyspark.sql.udtf import UDTFRegistration from pyspark.errors.exceptions.captured import install_exception_handler from pyspark.sql.types import AtomicType, DataType, StructType from pyspark.sql.streaming import StreamingQueryManager from pyspark.sql.streaming.query import StreamingCheckpointManager if TYPE_CHECKING: from py4j.java_gateway import JavaObject import pyarrow as pa from pyspark.core.rdd import RDD from pyspark.core.context import SparkContext from pyspark.sql._typing import ( AtomicValue, RowLike, UserDefinedFunctionLike, ) from pyspark.sql.pandas._typing import DataFrameLike as PandasDataFrameLike __all__ = ["SQLContext", "HiveContext"] class SQLContext: """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 to create :class:`DataFrame`, register :class:`DataFrame` as tables, execute SQL over tables, cache tables, and read parquet files. .. deprecated:: 3.0.0 Use :func:`SparkSession.builder.getOrCreate()` instead. Parameters ---------- sparkContext : :class:`SparkContext` The :class:`SparkContext` backing this SQLContext. sparkSession : :class:`SparkSession` The :class:`SparkSession` around which this SQLContext wraps. jsqlContext : optional 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. This is only for internal. Examples -------- >>> from datetime import datetime >>> from pyspark.sql import Row >>> 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 + 1)=2, (d + 1)=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, 'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ _instantiatedContext: ClassVar[Optional["SQLContext"]] = None def __init__( self, sparkContext: "SparkContext", sparkSession: Optional[SparkSession] = None, jsqlContext: Optional["JavaObject"] = None, ): if sparkSession is None: warnings.warn( "Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.", FutureWarning, ) self._sc = sparkContext self._jsc = self._sc._jsc self._jvm = self._sc._jvm if sparkSession is None: sparkSession = SparkSession._getActiveSessionOrCreate() if jsqlContext is None: jsqlContext = sparkSession._jsparkSession.sqlContext() self.sparkSession = sparkSession self._jsqlContext = jsqlContext _monkey_patch_RDD(self.sparkSession) install_exception_handler() if ( SQLContext._instantiatedContext is None or SQLContext._instantiatedContext._sc._jsc is None ): SQLContext._instantiatedContext = self @property def _ssql_ctx(self) -> "JavaObject": """Accessor for the JVM Spark SQL context. Subclasses can override this property to provide their own JVM Contexts. """ return self._jsqlContext @classmethod def getOrCreate(cls: Type["SQLContext"], sc: "SparkContext") -> "SQLContext": """ Get the existing SQLContext or create a new one with given SparkContext. .. versionadded:: 1.6.0 .. deprecated:: 3.0.0 Use :func:`SparkSession.builder.getOrCreate()` instead. Parameters ---------- sc : :class:`SparkContext` """ warnings.warn( "Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.", FutureWarning, ) return cls._get_or_create(sc) @classmethod def _get_or_create( cls: Type["SQLContext"], sc: "SparkContext", **static_conf: Any ) -> "SQLContext": if ( cls._instantiatedContext is None or SQLContext._instantiatedContext._sc._jsc is None # type: ignore[union-attr] ): assert sc._jvm is not None # There can be only one running Spark context. That will automatically # be used in the Spark session internally. session = SparkSession._getActiveSessionOrCreate(**static_conf) cls(sc, session, session._jsparkSession.sqlContext()) return cast(SQLContext, cls._instantiatedContext) def newSession(self) -> "SQLContext": """ Returns a new SQLContext as new session, that has separate SQLConf, registered temporary views and UDFs, but shared SparkContext and table cache. .. versionadded:: 1.6.0 """ return self.__class__(self._sc, self.sparkSession.newSession()) def setConf(self, key: str, value: Union[bool, int, str]) -> None: """Sets the given Spark SQL configuration property. .. versionadded:: 1.3.0 """ self.sparkSession.conf.set(key, value) def getConf( self, key: str, defaultValue: Union[Optional[str], _NoValueType] = _NoValue ) -> Optional[str]: """Returns the value of Spark SQL configuration property for the given key. If the key is not set and defaultValue is set, return defaultValue. If the key is not set and defaultValue is not set, return the system default value. .. versionadded:: 1.3.0 Examples -------- >>> sqlContext.getConf("spark.sql.shuffle.partitions") '200' >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10") '10' >>> sqlContext.setConf("spark.sql.shuffle.partitions", "50") >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10") '50' """ return self.sparkSession.conf.get(key, defaultValue) @property def udf(self) -> UDFRegistration: """Returns a :class:`UDFRegistration` for UDF registration. .. versionadded:: 1.3.1 Returns ------- :class:`UDFRegistration` """ return self.sparkSession.udf @property def udtf(self) -> UDTFRegistration: """Returns a :class:`UDTFRegistration` for UDTF registration. .. versionadded:: 3.5.0 Returns ------- :class:`UDTFRegistration` """ return self.sparkSession.udtf def range( self, start: int, end: Optional[int] = None, step: int = 1, numPartitions: Optional[int] = None, ) -> DataFrame: """ 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``. .. versionadded:: 1.4.0 Parameters ---------- start : int the start value end : int, optional the end value (exclusive) step : int, optional the incremental step (default: 1) numPartitions : int, optional the number of partitions of the DataFrame Returns ------- :class:`DataFrame` Examples -------- >>> 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) def registerFunction( self, name: str, f: Callable[..., Any], returnType: Optional[DataType] = None ) -> "UserDefinedFunctionLike": """An alias for :func:`spark.udf.register`. See :meth:`pyspark.sql.UDFRegistration.register`. .. versionadded:: 1.2.0 .. deprecated:: 2.3.0 Use :func:`spark.udf.register` instead. """ warnings.warn("Deprecated in 2.3.0. Use spark.udf.register instead.", FutureWarning) return self.sparkSession.udf.register(name, f, returnType) def registerJavaFunction( self, name: str, javaClassName: str, returnType: Optional[DataType] = None ) -> None: """An alias for :func:`spark.udf.registerJavaFunction`. See :meth:`pyspark.sql.UDFRegistration.registerJavaFunction`. .. versionadded:: 2.1.0 .. deprecated:: 2.3.0 Use :func:`spark.udf.registerJavaFunction` instead. """ warnings.warn( "Deprecated in 2.3.0. Use spark.udf.registerJavaFunction instead.", FutureWarning, ) return self.sparkSession.udf.registerJavaFunction(name, javaClassName, returnType) @overload def createDataFrame( self, data: Union["RDD[RowLike]", Iterable["RowLike"]], schema: Union[List[str], Tuple[str, ...]] = ..., samplingRatio: Optional[float] = ..., ) -> DataFrame: ... @overload def createDataFrame( self, data: Union["RDD[RowLike]", Iterable["RowLike"]], schema: Union[StructType, str], *, verifySchema: bool = ..., ) -> DataFrame: ... @overload def createDataFrame( self, data: Union[ "RDD[AtomicValue]", Iterable["AtomicValue"], ], schema: Union[AtomicType, str], verifySchema: bool = ..., ) -> DataFrame: ... @overload def createDataFrame( self, data: Union["PandasDataFrameLike", "pa.Table"], samplingRatio: Optional[float] = ... ) -> DataFrame: ... @overload def createDataFrame( self, data: Union["PandasDataFrameLike", "pa.Table"], schema: Union[StructType, str], verifySchema: bool = ..., ) -> DataFrame: ... def createDataFrame( # type: ignore[misc] self, data: Union["RDD[Any]", Iterable[Any], "PandasDataFrameLike", "pa.Table"], schema: Optional[Union[AtomicType, StructType, str]] = None, samplingRatio: Optional[float] = None, verifySchema: bool = True, ) -> DataFrame: """ Creates a :class:`DataFrame` from an :class:`RDD`, a list, a :class:`pandas.DataFrame`, or a :class:`pyarrow.Table`. 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 determine the ratio of rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``. .. versionadded:: 1.3.0 .. versionchanged:: 2.0.0 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. .. versionchanged:: 2.1.0 Added verifySchema. .. versionchanged:: 4.0.0 Added support for :class:`pyarrow.Table`. Parameters ---------- data : :class:`RDD` or iterable an RDD of any kind of SQL data representation (:class:`Row`, :class:`tuple`, ``int``, ``boolean``, etc.), or :class:`list`, :class:`pandas.DataFrame`, or :class:`pyarrow.Table`. schema : :class:`pyspark.sql.types.DataType`, str or list, optional 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<>``. samplingRatio : float, optional the sample ratio of rows used for inferring verifySchema : bool, optional verify data types of every row against schema. Enabled by default. Returns ------- :class:`DataFrame` Examples -------- >>> l = [('Alice', 1)] >>> sqlContext.createDataFrame(l).collect() [Row(_1='Alice', _2=1)] >>> sqlContext.createDataFrame(l, ['name', 'age']).collect() [Row(name='Alice', age=1)] >>> d = [{'name': 'Alice', 'age': 1}] >>> sqlContext.createDataFrame(d).collect() [Row(age=1, name='Alice')] >>> rdd = sc.parallelize(l) >>> sqlContext.createDataFrame(rdd).collect() [Row(_1='Alice', _2=1)] >>> df = sqlContext.createDataFrame(rdd, ['name', 'age']) >>> df.collect() [Row(name='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='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='Alice', age=1)] >>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP [Row(name='Alice', age=1)] >>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP [Row(0=1, 1=2)] >>> sqlContext.createDataFrame(df.toArrow()).collect() # doctest: +SKIP [Row(name='Alice', age=1)] >>> table = pyarrow.table({'0': [1], '1': [2]}) # doctest: +SKIP >>> sqlContext.createDataFrame(table).collect() # doctest: +SKIP [Row(0=1, 1=2)] >>> sqlContext.createDataFrame(rdd, "a: string, b: int").collect() [Row(a='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): ... pyspark.errors.exceptions.captured.PythonException: ... """ return self.sparkSession.createDataFrame( # type: ignore[call-overload] data, schema, samplingRatio, verifySchema ) def registerDataFrameAsTable(self, df: DataFrame, tableName: str) -> None: """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`. .. versionadded:: 1.3.0 Examples -------- >>> sqlContext.registerDataFrameAsTable(df, "table1") """ df.createOrReplaceTempView(tableName) def dropTempTable(self, tableName: str) -> None: """Remove the temporary table from catalog. .. versionadded:: 1.6.0 Examples -------- >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> sqlContext.dropTempTable("table1") """ self.sparkSession.catalog.dropTempView(tableName) def createExternalTable( self, tableName: str, path: Optional[str] = None, source: Optional[str] = None, schema: Optional[StructType] = None, **options: str, ) -> DataFrame: """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. .. versionadded:: 1.3.0 Returns ------- :class:`DataFrame` """ return self.sparkSession.catalog.createExternalTable( tableName, path, source, schema, **options ) def sql(self, sqlQuery: str) -> DataFrame: """Returns a :class:`DataFrame` representing the result of the given query. .. versionadded:: 1.0.0 Returns ------- :class:`DataFrame` Examples -------- >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> df2.collect() [Row(f1=1, f2='row1'), Row(f1=2, f2='row2'), Row(f1=3, f2='row3')] """ return self.sparkSession.sql(sqlQuery) def table(self, tableName: str) -> DataFrame: """Returns the specified table or view as a :class:`DataFrame`. .. versionadded:: 1.0.0 Returns ------- :class:`DataFrame` Examples -------- >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> df2 = sqlContext.table("table1") >>> sorted(df.collect()) == sorted(df2.collect()) True """ return self.sparkSession.table(tableName) def tables(self, dbName: Optional[str] = None) -> DataFrame: """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). .. versionadded:: 1.3.0 Parameters ---------- dbName: str, optional name of the database to use. Returns ------- :class:`DataFrame` Examples -------- >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> df2 = sqlContext.tables() >>> df2.filter("tableName = 'table1'").first() Row(namespace='', tableName='table1', isTemporary=True) """ if dbName is None: return DataFrame(self._ssql_ctx.tables(), self.sparkSession) else: return DataFrame(self._ssql_ctx.tables(dbName), self.sparkSession) def tableNames(self, dbName: Optional[str] = None) -> List[str]: """Returns a list of names of tables in the database ``dbName``. .. versionadded:: 1.3.0 Parameters ---------- dbName: str name of the database to use. Default to the current database. Returns ------- list 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)] def cacheTable(self, tableName: str) -> None: """ Caches the specified table in-memory. .. versionadded:: 1.0.0 """ self._ssql_ctx.cacheTable(tableName) def uncacheTable(self, tableName: str) -> None: """ Removes the specified table from the in-memory cache. .. versionadded:: 1.0.0 """ self._ssql_ctx.uncacheTable(tableName) def clearCache(self) -> None: """ Removes all cached tables from the in-memory cache. .. versionadded:: 1.3.0 """ self._ssql_ctx.clearCache() @property def read(self) -> DataFrameReader: """ Returns a :class:`DataFrameReader` that can be used to read data in as a :class:`DataFrame`. .. versionadded:: 1.4.0 Returns ------- :class:`DataFrameReader` """ return DataFrameReader(self.sparkSession) @property def readStream(self) -> DataStreamReader: """ Returns a :class:`DataStreamReader` that can be used to read data streams as a streaming :class:`DataFrame`. .. versionadded:: 2.0.0 Notes ----- This API is evolving. Returns ------- :class:`DataStreamReader` >>> text_sdf = sqlContext.readStream.text(tempfile.mkdtemp()) >>> text_sdf.isStreaming True """ return DataStreamReader(self.sparkSession) @property def streams(self) -> StreamingQueryManager: """Returns a :class:`StreamingQueryManager` that allows managing all the :class:`StreamingQuery` StreamingQueries active on `this` context. .. versionadded:: 2.0.0 Notes ----- This API is evolving. """ from pyspark.sql.streaming import StreamingQueryManager return StreamingQueryManager(self._ssql_ctx.streams()) @property def _streamingCheckpointManager(self) -> StreamingCheckpointManager: """Returns a :class:`StreamingCheckpointManager` to manage streaming checkpoints. .. versionadded:: 4.2.0 Notes ----- This API is evolving. """ return StreamingCheckpointManager(self._ssql_ctx.streamingCheckpointManager()) 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. .. deprecated:: 2.0.0 Use SparkSession.builder.enableHiveSupport().getOrCreate(). Parameters ---------- sparkContext : :class:`SparkContext` The SparkContext to wrap. jhiveContext : optional 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. This is only for internal use. """ _static_conf = {"spark.sql.catalogImplementation": "hive"} def __init__( self, sparkContext: "SparkContext", sparkSession: Optional[SparkSession] = None, jhiveContext: Optional["JavaObject"] = None, ): warnings.warn( "HiveContext is deprecated in Spark 2.0.0. Please use " + "SparkSession.builder.enableHiveSupport().getOrCreate() instead.", FutureWarning, ) static_conf = {} if jhiveContext is None: static_conf = HiveContext._static_conf # There can be only one running Spark context. That will automatically # be used in the Spark session internally. if sparkSession is not None: sparkSession = SparkSession._getActiveSessionOrCreate(**static_conf) SQLContext.__init__(self, sparkContext, sparkSession, jhiveContext) @classmethod def _get_or_create( cls: Type["SQLContext"], sc: "SparkContext", **static_conf: Any ) -> "SQLContext": return SQLContext._get_or_create(sc, **HiveContext._static_conf) @classmethod def _createForTesting(cls, sparkContext: "SparkContext") -> "HiveContext": """(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() assert sparkContext._jvm is not None jtestHive = sparkContext._jvm.org.apache.spark.sql.hive.test.TestHiveContext(jsc, False) return cls(sparkContext, jtestHive) def refreshTable(self, tableName: str) -> None: """Invalidate and refresh all the cached 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) def _test() -> None: import os import doctest import tempfile from pyspark.core.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: sys.exit(-1) if __name__ == "__main__": _test()