Sail

The mission of Sail is to unify stream processing, batch processing, and compute-intensive (AI) workloads. Currently, Sail features a drop-in replacement for Spark SQL and the Spark DataFrame API in both single-host and distributed settings.

✨Please check out our MCP server that brings data analytics in Spark to both LLM agents and humans!✨

Installation

Sail is available as a Python package on PyPI. You can install it along with PySpark in your Python environment.

bash
1pip install pysail
2pip install "pyspark[connect]"

Alternatively, you can install the lightweight client package pyspark-client since Spark 4.0. The pyspark-connect package, which is equivalent to pyspark[connect], is also available since Spark 4.0.

The Installation guide contains more information about installing Sail from source for better performance for your hardware architecture.

Getting Started

Starting the Sail Server

Option 1: Command Line Interface You can start the local Sail server using the sail command.

bash
1sail spark server --port 50051

Option 2: Python API You can start the local Sail server using the Python API.

python
1from pysail.spark import SparkConnectServer
2
3server = SparkConnectServer(port=50051)
4server.start(background=False)

Option 3: Kubernetes You can deploy Sail on Kubernetes and run Sail in cluster mode for distributed processing. Please refer to the Kubernetes Deployment Guide for instructions on building the Docker image and writing the Kubernetes manifest YAML file.

bash
1kubectl apply -f sail.yaml
2kubectl -n sail port-forward service/sail-spark-server 50051:50051

Connecting to the Sail Server

Once you have a running Sail server, you can connect to it in PySpark. No changes are needed in your PySpark code!

python
1from pyspark.sql import SparkSession
2
3spark = SparkSession.builder.remote("sc://localhost:50051").getOrCreate()
4spark.sql("SELECT 1 + 1").show()

Please refer to the Getting Started guide for further details.

Documentation

The documentation of the latest Sail version can be found here.

Further Reading

• Supercharge Spark: Quadruple Speed, Cut Costs by 94% - This post presents detailed benchmark results comparing Sail with Spark.
• Sail 0.2 and the Future of Distributed Processing - This post discusses the Sail distributed processing architecture.

Contributing

Contributions are more than welcome!

Please submit GitHub issues for bug reports and feature requests. You are also welcome to ask questions in GitHub discussions.

Feel free to create a pull request if you would like to make a code change. You can refer to the development guide to get started.

Support

LakeSail offers flexible enterprise support options for Sail. Please contact us to learn more.