PySpark reference

This page provides an overview of reference available for PySpark, a Python API for Spark. For more information about PySpark, see PySpark on Azure Databricks.

Data types

For a complete list of PySpark data types, see PySpark data types.

Classes

Reference	Description
Avro	Support for reading and writing data in Apache Avro format.
Catalog	Interface for managing databases, tables, functions, and other catalog metadata.
Column	Operations for working with DataFrame columns, including transformations and expressions.
Data Types	Available data types in PySpark SQL, including primitive types, complex types, and user-defined types.
DataFrame	Distributed collection of data organized into named columns, similar to a table in a relational database.
DataFrameNaFunctions	Functionality for working with missing data in a DataFrame.
DataFrameReader	Interface used to load a DataFrame from external storage systems.
DataFrameStatFunctions	Functionality for statistical functions with a DataFrame.
DataFrameWriter	Interface used to write a DataFrame to external storage systems.
DataFrameWriterV2	Interface used to write a DataFrame to external storage (version 2).
DataSource	APIs for implementing custom data sources to read from external systems. For information about custom data sources, see PySpark custom data sources.
DataSourceArrowWriter	A base class for data source writers that process data using PyArrow's RecordBatch.
DataSourceRegistration	A wrapper for data source registration.
DataSourceReader	A base class for data source readers.
DataSourceStreamArrowWriter	A base class for data stream writers that process data using PyArrow's RecordBatch.
DataSourceStreamReader	A base class for streaming data source readers.
DataSourceStreamWriter	A base class for data stream writers.
GroupedData	Methods for grouping data and performing aggregation operations on grouped DataFrames.
Observation	Collects metrics and observes DataFrames during query execution for monitoring and debugging.
PlotAccessor	Accessor for DataFrame plotting functionality in PySpark.
ProtoBuf	Support for serializing and deserializing data using Protocol Buffers format.
Row	Represents a row of data in a DataFrame, providing access to individual field values.
RuntimeConfig	Runtime configuration options for Spark SQL, including execution and optimizer settings. For information on configuration that is only available on Databricks, see Set Spark configuration properties on Azure Databricks.
SparkSession	The entry point for reading data and executing SQL queries in PySpark applications.
Stateful Processor	Manages state across streaming batches for complex stateful operations in structured streaming.
UserDefinedFunction (UDF)	User-defined functions for applying custom Python logic to DataFrame columns.
UDFRegistration	Wrapper for user-defined function registration. This instance can be accessed by spark.udf.
UserDefinedTableFunction (UDTF)	User-defined table functions that return multiple rows for each input row.
UDTFRegistration	Wrapper for user-defined table function registration. This instance can be accessed by spark.udtf.
VariantVal	Represents semi-structured data with flexible schema, which supports dynamic types and nested structures.
Window	Window functions for performing calculations across a set of table rows related to the current row.
WindowSpec	Window functions for performing calculations across a set of table rows related to the current row.

Functions

For a complete list of available built-in functions, see PySpark functions.