PySpark

PySpark is the Python API for Apache Spark, an open-source framework for large-scale data processing. It provides Python developers with access to Spark’s distributed computing capabilities, enabling tasks such as data ingestion, transformation, analysis, and machine learning on big datasets.

PySpark runs Python code in a driver process that communicates with a JVM-based Spark cluster via Py4J. The Python side exposes APIs such as SparkSession, DataFrame, and RDD, while the actual data processing occurs on Spark executors across a cluster. Operations on DataFrames and RDDs are evaluated lazily and optimized by Spark’s Catalyst optimizer and Tungsten execution engine.

Key components include SparkSession as the entry point, DataFrames and Datasets for structured data, and Spark SQL for queries. PySpark also integrates libraries for broader capabilities, including MLlib for machine learning and Structured Streaming for real-time data processing. It can read and write data in various formats (Parquet, ORC, JSON, CSV) and works with Hadoop ecosystems and cloud storage.

Performance considerations involve features such as Arrow-based Pandas UDFs to speed up Python–JVM data exchange, and awareness of Python serialization overhead in certain workflows. PySpark supports both batch and streaming workloads and can run on local mode for development or on multi-node clusters.

Installation is commonly done with pip install pyspark or by using a Spark distribution that includes the Python API. A compatible Java Runtime, Python environment, and a cluster manager (such as YARN or Kubernetes) are typically required to deploy PySpark programs.