RRepoGEO

REPOGEO REPORT · LITE

chdb-io/chdb

Default branch main · commit 3d01e781 · scanned 6/22/2026, 2:17:03 PM

GitHub: 2,698 stars · 116 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 chdb-io/chdb, 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
    Strengthen README's opening statement with core differentiator

    Why:

    CURRENT
    > chDB is an in-process SQL OLAP Engine powered by ClickHouse [^1]
    COPY-PASTE FIX
    > chDB is an in-process, serverless OLAP SQL Engine powered by ClickHouse, offering high-performance analytical queries directly within your Python applications without external dependencies. Think DuckDB, but with the proven power and SQL dialect of ClickHouse.
  • mediumreadme#2
    Add a dedicated 'chDB vs. X' comparison section to README

    Why:

    COPY-PASTE FIX
    ## chDB vs. DuckDB and Polars

chDB provides an embedded, serverless OLAP SQL engine leveraging the full power of ClickHouse's analytical core and SQL dialect, making it ideal for high-performance, in-process data analysis. While DuckDB offers similar embedded SQL capabilities, chDB differentiates by bringing the ClickHouse ecosystem directly to your application. Compared to Polars, which focuses on DataFrame operations, chDB offers a complete SQL engine for complex analytical queries on various file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) without requiring a separate server.
  • lowtopics#3
    Expand topics with competitor and use-case keywords

    Why:

    CURRENT
    chdb, clickhouse, clickhouse-database, clickhouse-server, data-science, database, embedded-database, olap, python, sql
    COPY-PASTE FIX
    chdb, clickhouse, clickhouse-database, clickhouse-server, data-science, database, embedded-database, olap, python, sql, duckdb-alternative, in-process-database, serverless-database, analytical-database, data-lake-analytics

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 chdb-io/chdb
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DuckDB
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DuckDB · recommended 1×
  2. SQLite · recommended 1×
  3. Apache Calcite · recommended 1×
  4. ClickHouse · recommended 1×
  5. Polars · recommended 1×
  • CATEGORY QUERY
    What are good options for an embedded OLAP SQL engine in Python?
    you: not recommended
    AI recommended (in order):
    1. DuckDB
    2. SQLite
    3. Apache Calcite
    4. ClickHouse
    5. Polars

    AI recommended 5 alternatives but never named chdb-io/chdb. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a fast, lightweight Python library for analytical queries on various data files.
    you: not recommended
    AI recommended (in order):
    1. Polars (pola-rs/polars)
    2. DuckDB (duckdb/duckdb)
    3. Pandas (pandas-dev/pandas)
    4. NumExpr (pydata/numexpr)
    5. Dask (dask/dask)
    6. pyarrow (apache/arrow)
    7. fastparquet (dask/fastparquet)
    8. Vaex (vaexio/vaex)
    9. PySpark (apache/spark)

    AI recommended 9 alternatives but never named chdb-io/chdb. 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 chdb-io/chdb?
    pass
    AI named chdb-io/chdb explicitly

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

  • If a team adopts chdb-io/chdb in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chdb-io/chdb 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 chdb-io/chdb solve, and who is the primary audience?
    pass
    AI named chdb-io/chdb explicitly

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

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chdb-io/chdb — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite