RRepoGEO

REPOGEO REPORT · LITE

lakehq/sail

Default branch main · commit 3d8fbb97 · scanned 5/28/2026, 6:02:28 AM

GitHub: 2,749 stars · 159 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface lakehq/sail, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to clarify core identity

    Why:

    CURRENT
    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.
    COPY-PASTE FIX
    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.
  • mediumabout#2
    Update the repository description for clarity

    Why:

    CURRENT
    Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.
    COPY-PASTE FIX
    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.
  • lowreadme#3
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## 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).

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface lakehq/sail
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. ballista-rs/ballista · recommended 1×
  3. apache/arrow-datafusion · recommended 1×
  4. temporalio/tardigrade · recommended 1×
  5. apache/flink · recommended 1×
  • CATEGORY QUERY
    Need a distributed data processing framework in Rust, aiming for better performance than JVM-based systems.
    you: not recommended
    AI recommended (in order):
    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 recommended 5 alternatives but never named lakehq/sail. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a unified distributed engine for batch, stream, and AI workloads with a familiar Python interface.
    you: not recommended
    AI recommended (in order):
    1. Apache Spark
    2. Ray (ray-project/ray)
    3. Dask (dask/dask)
    4. Apache Flink
    5. Modin (modin-project/modin)

    AI recommended 5 alternatives but never named lakehq/sail. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

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

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite