lakehq/sail

• highreadme#1

  Reposition README's opening to clarify core identity

  原因：

  当前

  Sail is a **drop-in Apache Spark replacement** written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads on a distributed, multimodal compute engine.

  复制粘贴的修复

  复制

  Sail is a **high-performance distributed data processing engine** and **drop-in Apache Spark replacement** written in Rust. It unifies batch processing, stream processing, and compute-intensive AI workloads, offering a memory-safe alternative to JVM-based systems.

  待办完成忽略
• mediumabout#2

  Update the repository description for clarity

  原因：

  当前

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

  复制粘贴的修复

  复制

  High-performance, Rust-native **distributed data processing engine** and **drop-in Apache Spark replacement**. Unifies batch, stream, and AI workloads, offering a memory-safe alternative to JVM-based systems.

  待办完成忽略
• lowreadme#3

  Add a dedicated 'Comparison' section to the README

  原因：

  复制粘贴的修复

  复制

  ## Comparison
  
  Sail is engineered to be a superior alternative to existing distributed data processing frameworks. While compatible with the Spark Connect protocol, Sail offers significant advantages over Apache Spark and other JVM-based systems, including up to 8x faster performance and 94% lower infrastructure costs. Unlike general-purpose distributed frameworks like Ray, Sail is specifically optimized for unified batch, stream, and AI data workloads, providing a memory-safe and predictable Rust-native experience. For detailed benchmarks, see [Benchmark Results](#benchmark-results).

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Need a distributed data processing framework in Rust, aiming for better performance than JVM-based systems.

  你：未被推荐
  AI 推荐顺序：

  1. Ballista (ballista-rs/ballista)
  2. DataFusion (apache/arrow-datafusion)
  3. Ray (ray-project/ray)
  4. Tardigrade (temporalio/tardigrade)
  5. Apache Flink (apache/flink)

  AI 推荐了 5 个替代方案，却始终没点名 lakehq/sail。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking a unified distributed engine for batch, stream, and AI workloads with a familiar Python interface.

  你：未被推荐
  AI 推荐顺序：

  1. Apache Spark
  2. Ray (ray-project/ray)
  3. Dask (dask/dask)
  4. Apache Flink
  5. Modin (modin-project/modin)

  AI 推荐了 5 个替代方案，却始终没点名 lakehq/sail。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of lakehq/sail?

  pass
  ✓AI 明确点名了 lakehq/sail

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts lakehq/sail in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 lakehq/sail

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo lakehq/sail solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 lakehq/sail

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？