pyspark.sql.SparkSession¶

class pyspark.sql.SparkSession(sparkContext: pyspark.context.SparkContext, jsparkSession: Optional[py4j.java_gateway.JavaObject] = None, options: Dict[str, Any] = {})¶

The entry point to programming Spark with the Dataset and DataFrame API.

A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. To create a SparkSession, use the following builder pattern:

builder¶

Examples

>>> spark = SparkSession.builder \
...     .master("local") \
...     .appName("Word Count") \
...     .config("spark.some.config.option", "some-value") \
...     .getOrCreate()

>>> from datetime import datetime
>>> from pyspark.sql import Row
>>> spark = SparkSession(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")
>>> spark.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])]

Methods

`createDataFrame`(data[, schema, …])	Creates a `DataFrame` from an `RDD`, a list, a `pandas.DataFrame` or a `numpy.ndarray`.
`getActiveSession`()	Returns the active `SparkSession` for the current thread, returned by the builder
`newSession`()	Returns a new `SparkSession` as new session, that has separate SQLConf, registered temporary views and UDFs, but shared `SparkContext` and table cache.
`range`(start[, end, step, numPartitions])	Create a `DataFrame` with single `pyspark.sql.types.LongType` column named `id`, containing elements in a range from `start` to `end` (exclusive) with step value `step`.
`sql`(sqlQuery, **kwargs)	Returns a `DataFrame` representing the result of the given query.
`stop`()	Stop the underlying `SparkContext`.
`table`(tableName)	Returns the specified table as a `DataFrame`.

Attributes

`builder`
`catalog`	Interface through which the user may create, drop, alter or query underlying databases, tables, functions, etc.
`conf`	Runtime configuration interface for Spark.
`read`	Returns a `DataFrameReader` that can be used to read data in as a `DataFrame`.
`readStream`	Returns a `DataStreamReader` that can be used to read data streams as a streaming `DataFrame`.
`sparkContext`	Returns the underlying `SparkContext`.
`streams`	Returns a `StreamingQueryManager` that allows managing all the `StreamingQuery` instances active on this context.
`udf`	Returns a `UDFRegistration` for UDF registration.
`version`	The version of Spark on which this application is running.

Core Classes

pyspark.sql.Catalog