Sail

Sail is a project aiming to unify stream processing, batch processing, and compute - intensive (AI) workloads. It provides an alternative to Spark SQL and Spark DataFrame APIs and supports both standalone and distributed environments.

Research and data Developer tools #Unified Stream and Batch Processing #Spark Alternative #AI Computing #Distributed Processing .Rust

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update time : 2025-04-23

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What is the Sail MCP Server?

The Sail MCP Server is an innovative data processing platform designed for Apache Spark, aiming to significantly improve data analysis performance through optimized execution plans and modern hardware acceleration technologies. It supports native SQL and DataFrame APIs and can be seamlessly integrated into existing workflows.

Why choose Sail?

Sail offers higher performance, a simpler deployment process, and lower costs than traditional Spark. Its optimized execution engine and automatic resource management features make it an ideal choice for big data processing.

Key Features

Sail supports multiple computing frameworks, including Apache Flink and Apache Beam, and integrates with major cloud providers, ensuring flexible deployment options.

Frequently Asked Questions

Is Sail compatible with Apache Spark?

How to install Sail?

🚀 Sail

Sail aims to unify stream processing, batch processing, and compute - intensive (AI) workloads. It currently offers drop - in replacements for Spark SQL and Spark DataFrame APIs, usable in both single - node and distributed environments.

✨ News ✨: Check out our newly launched MCP Server, which brings Spark data analysis to LLM agents and humans!

🚀 Quick Start

Start the Sail Server

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

sail spark server --port 50051

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

from pysail.spark import SparkConnectServer

server = SparkConnectServer(port=50051)
server.start(background=False)

Option 3: Kubernetes You can deploy Sail to Kubernetes and run it in cluster mode for distributed processing. Refer to the Kubernetes Deployment Guide to learn how to build a Docker image and write a Kubernetes extension file YAML file.

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

Connect to the Sail Server

Once you have a running Sail server, you can connect to it via PySpark. No need to change your PySpark code!

from pyspark.sql import SparkSession

spark = SparkSession.builder.remote("sc://localhost:50051").getOrCreate()
spark.sql("SELECT 1 + 1").show()

Refer to the Quick Start Guide for more details.

📦 Installation

Sail is available as a Python package on PyPI. You can install it using pip.

pip install "pysail[spark]"

Alternatively, you can install from source for better performance tailored to your hardware architecture. You can follow the Installation Guide for more details.