apache/spark-py repository overview

⁠Apache Spark

Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/⁠

⁠Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page⁠. This README file only contains basic setup instructions.

⁠Interactive Python Shell

The easiest way to start using PySpark is through the Python shell:

docker run -it apache/spark-py /opt/spark/bin/pyspark

And run the following command, which should also return 1,000,000,000:

>>> spark.range(1000 * 1000 * 1000).count()

⁠Running Spark on Kubernetes

https://spark.apache.org/docs/latest/running-on-kubernetes.html⁠

⁠To run Spark with Scala/Java only

Use the images on https://hub.docker.com/r/apache/spark⁠

⁠To run R Spark

Use the images on https://hub.docker.com/r/apache/spark-r⁠