Coverage for pyspark/sql/context.py : 86%
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# # 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. #
"""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.
.. 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])] """
"Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.", FutureWarning )
or SQLContext._instantiatedContext._sc._jsc is None):
def _ssql_ctx(self): """Accessor for the JVM Spark SQL context.
Subclasses can override this property to provide their own JVM Contexts. """
def _conf(self): """Accessor for the JVM SQL-specific configurations"""
def getOrCreate(cls, sc): """ 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` """ "Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.", FutureWarning )
or SQLContext._instantiatedContext._sc._jsc is None): sc._jsc.sc()).getOrCreate().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())
"""Sets the given Spark SQL configuration property.
.. versionadded:: 1.3.0 """
"""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' """
def udf(self): """Returns a :class:`UDFRegistration` for UDF registration.
.. versionadded:: 1.3.1
Returns ------- :class:`UDFRegistration` """
""" 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)] """
"""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. """ "Deprecated in 2.3.0. Use spark.udf.register instead.", FutureWarning )
"""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. """ "Deprecated in 2.3.0. Use spark.udf.registerJavaFunction instead.", FutureWarning )
# TODO(andrew): delete this once we refactor things to take in SparkSession """ Infer schema from an RDD of Row or tuple.
Parameters ---------- rdd : :class:`RDD` an RDD of Row or tuple samplingRatio : float, optional sampling ratio, or no sampling (default)
Returns ------- :class:`pyspark.sql.types.StructType` """
""" 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``.
.. 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.
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`, or :class:`pandas.DataFrame`. 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<>`` 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`. 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(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): ... Py4JJavaError: ... """
"""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") """
""" Remove the temporary table from catalog.
.. versionadded:: 1.6.0
Examples -------- >>> sqlContext.registerDataFrameAsTable(df, "table1") >>> sqlContext.dropTempTable("table1") """
"""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)
"""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')] """
"""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 """
"""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) """ else: return DataFrame(self._ssql_ctx.tables(dbName), self)
"""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 """ else:
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()
def read(self): """ 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)
def readStream(self): """ 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 """
def streams(self): """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. """
"""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.
"""
warnings.warn( "HiveContext is deprecated in Spark 2.0.0. Please use " + "SparkSession.builder.enableHiveSupport().getOrCreate() instead.", FutureWarning ) if jhiveContext is None: sparkContext._conf.set("spark.sql.catalogImplementation", "hive") sparkSession = SparkSession.builder._sparkContext(sparkContext).getOrCreate() else: sparkSession = SparkSession(sparkContext, jhiveContext.sparkSession()) SQLContext.__init__(self, sparkContext, sparkSession, jhiveContext)
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)
"""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)
[Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")] ) '{"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": []}}' ] pyspark.sql.context, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) sys.exit(-1)
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