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Sail MCP Server for Spark SQL

@lakehq

代码仓库主页

关于 Sail MCP Server for Spark SQL

Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.

#sail#spark#sql#data-analytics

基本信息

分类

数据库

许可证

Apache-2.0

运行时

rust

传输方式

stdio

发布者

lakehq

提交者

Heran Lin

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "sail": {
      "command": "sail",
      "args": [
        "spark",
        "mcp-server",
        "--transport",
        "stdio"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Sail MCP Server for Spark SQL?

Sail MCP Server for Spark SQL is a drop-in Apache Spark replacement written in Rust, providing a Spark SQL and DataFrame API server over the Spark Connect protocol. It unifies batch processing, stream processing, and compute-intensive AI workloads on a distributed engine. Based on derived TPC-H benchmarks, it is ~4× faster and 94% cheaper than Spark.

How to use Sail MCP Server for Spark SQL?

Install pysail and pyspark-client, then start the server via the command sail spark server --port 50051 or using the Python API with SparkConnectServer(port=50051).start(). Connect a PySpark client session by setting the remote address to sc://localhost:50051.

Key features of Sail MCP Server for Spark SQL

  • Drop-in compatibility with Spark SQL and DataFrame API.
  • 100% Rust-native engine with no JVM overhead.
  • Supports Delta Lake and Apache Iceberg table formats.
  • Integrates with AWS Glue, Unity Catalog, Hive Metastore, and OneLake.
  • Lightning-fast Python UDFs with zero-copy Arrow data sharing.
  • Lightweight, stateless workers for elastic scaling.

Use cases of Sail MCP Server for Spark SQL

  • Migrating existing PySpark workloads to a faster, cheaper engine.
  • Running batch ETL and data analytics pipelines.
  • Real-time stream processing using Spark-compatible APIs.
  • Compute-intensive AI workloads requiring high performance.

FAQ from Sail MCP Server for Spark SQL

How does Sail compare to Apache Spark?

Sail is a drop-in replacement that is ~4× faster and requires 94% less hardware cost, with zero shuffle spill per derived TPC-H benchmarks.

What are the runtime dependencies?

Python and the PySpark client package (pyspark-client). The server itself is a standalone Rust binary with no JVM required.

Where does data live?

Data can be read from and written to AWS S3, Azure, Google Cloud Storage, HDFS, Cloudflare R2, HTTP/HTTPS, Hugging Face, and local filesystems.

Is my existing Spark code compatible?

Yes, existing PySpark code works out of the box when the client connects over Spark Connect. An experimental compatibility check script scans your codebase for supported functions.

How do I deploy Sail in production?

Sail can be deployed on Kubernetes in cluster mode. Refer to the Kubernetes Deployment Guide for building Docker images and writing YAML manifests.

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