# # 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. # from __future__ import print_function import sys import warnings import json from functools import reduce if sys.version >= '3': basestring = unicode = str else: from itertools import imap as map from py4j.protocol import Py4JError from pyspark import since from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.serializers import AutoBatchedSerializer, PickleSerializer from pyspark.sql.types import Row, DataType, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string from pyspark.sql.dataframe import DataFrame from pyspark.sql.readwriter import DataFrameReader from pyspark.sql.utils import install_exception_handler from pyspark.sql.functions import UserDefinedFunction try: import pandas has_pandas = True except Exception: has_pandas = False __all__ = ["SQLContext", "HiveContext", "UDFRegistration"] def _monkey_patch_RDD(sqlContext): def toDF(self, schema=None, sampleRatio=None): """ Converts current :class:`RDD` into a :class:`DataFrame` This is a shorthand for ``sqlContext.createDataFrame(rdd, schema, sampleRatio)`` :param schema: a StructType or list of names of columns :param samplingRatio: the sample ratio of rows used for inferring :return: a DataFrame >>> rdd.toDF().collect() [Row(name=u'Alice', age=1)] """ return sqlContext.createDataFrame(self, schema, sampleRatio) RDD.toDF = toDF class SQLContext(object): """Main entry point for Spark SQL functionality. 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 sqlContext: 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, sqlContext=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.registerTempTable("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 self._scala_SQLContext = sqlContext _monkey_patch_RDD(self) 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. """ if self._scala_SQLContext is None: self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc()) return self._scala_SQLContext @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()) cls(sc, jsqlContext) return cls._instantiatedContext @since(1.6) def newSession(self): """ Returns a new SQLContext as new session, that has separate SQLConf, registered temporary tables and UDFs, but shared SparkContext and table cache. """ jsqlContext = self._ssql_ctx.newSession() return self.__class__(self._sc, jsqlContext) @since(1.3) def setConf(self, key, value): """Sets the given Spark SQL configuration property. """ self._ssql_ctx.setConf(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", "10") u'10' >>> sqlContext.setConf("spark.sql.shuffle.partitions", "50") >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10") u'50' """ if defaultValue is not None: return self._ssql_ctx.getConf(key, defaultValue) else: return self._ssql_ctx.getConf(key) @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 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)] """ if numPartitions is None: numPartitions = self._sc.defaultParallelism if end is None: jdf = self._ssql_ctx.range(0, int(start), int(step), int(numPartitions)) else: jdf = self._ssql_ctx.range(int(start), int(end), int(step), int(numPartitions)) return DataFrame(jdf, self) @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:`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)] """ udf = UserDefinedFunction(f, returnType, name) self._ssql_ctx.udf().registerPython(name, udf._judf) def _inferSchemaFromList(self, data): """ Infer schema from list of Row or tuple. :param data: list of Row or tuple :return: StructType """ if not data: raise ValueError("can not infer schema from empty dataset") first = data[0] if type(first) is dict: warnings.warn("inferring schema from dict is deprecated," "please use pyspark.sql.Row instead") schema = reduce(_merge_type, map(_infer_schema, data)) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") return schema 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: StructType """ first = rdd.first() if not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated. " "Use pyspark.sql.Row instead") if samplingRatio is None: schema = _infer_schema(first) if _has_nulltype(schema): for row in rdd.take(100)[1:]: schema = _merge_type(schema, _infer_schema(row)) if not _has_nulltype(schema): break else: raise ValueError("Some of types cannot be determined by the " "first 100 rows, please try again with sampling") else: if samplingRatio < 0.99: rdd = rdd.sample(False, float(samplingRatio)) schema = rdd.map(_infer_schema).reduce(_merge_type) return schema def _createFromRDD(self, rdd, schema, samplingRatio): """ Create an RDD for DataFrame from an existing RDD, returns the RDD and schema. """ if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchema(rdd, samplingRatio) converter = _create_converter(struct) rdd = rdd.map(converter) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) # convert python objects to sql data rdd = rdd.map(schema.toInternal) return rdd, schema def _createFromLocal(self, data, schema): """ Create an RDD for DataFrame from an list or pandas.DataFrame, returns the RDD and schema. """ # make sure data could consumed multiple times if not isinstance(data, list): data = list(data) if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchemaFromList(data) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif isinstance(schema, StructType): for row in data: _verify_type(row, schema) else: raise TypeError("schema should be StructType or list or None, but got: %s" % schema) # convert python objects to sql data data = [schema.toInternal(row) for row in data] return self._sc.parallelize(data), schema @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:`DataType` or datatype string, it must match the real data, or exception will be thrown at runtime. If the given schema is not StructType, it will be wrapped into a 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. row, tuple, int, boolean, etc.), or :class:`list`, or :class:`pandas.DataFrame`. :param schema: a :class:`DataType` or a datatype string or a list of column names, default is None. The data type string format equals to `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 ByteType. We can also use `int` as a short name for IntegerType. :param samplingRatio: the sample ratio of rows used for inferring :return: :class:`DataFrame` .. versionchanged:: 2.0 The schema parameter can be a DataType or a datatype string after 2.0. If it's not a StructType, it will be wrapped into a 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: ... """ if isinstance(data, DataFrame): raise TypeError("data is already a DataFrame") if isinstance(schema, basestring): schema = _parse_datatype_string(schema) if has_pandas and isinstance(data, pandas.DataFrame): if schema is None: schema = [str(x) for x in data.columns] data = [r.tolist() for r in data.to_records(index=False)] if isinstance(schema, StructType): def prepare(obj): _verify_type(obj, schema) return obj elif isinstance(schema, DataType): datatype = schema def prepare(obj): _verify_type(obj, datatype) return (obj, ) schema = StructType().add("value", datatype) else: prepare = lambda obj: obj if isinstance(data, RDD): rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio) else: rdd, schema = self._createFromLocal(map(prepare, data), schema) jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) df = DataFrame(jdf, self) df._schema = schema return df @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") """ 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) # TODO(andrew): remove this too 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.SparkSession.withHiveSupport(self._jsc.sc()).wrapped() 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()