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chdb-sql

Execute ClickHouse SQL queries on local files and remote databases in Python.

1.6k|107|Updated Feb 15, 2026
Search Tags:#data pipelines#parquet#clickhouse#python analytics#sql queries#chdb#cross-source joins
One-click install
npx skills add https://github.com/MapleTechLabs/maple --skill chdb-sql-mapletechlabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chdb-sql
Source: https://github.com/MapleTechLabs/maple/tree/main/.agents/skills/chdb-sql
Command: npx skills add https://github.com/MapleTechLabs/maple --skill chdb-sql-mapletechlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires chdb, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill eliminates the need to deploy and maintain a standalone ClickHouse server to run SQL queries, letting you analyze data directly in your Python environment with zero infrastructure overhead.

Core Features & Use Cases

  • Cross-Source SQL Querying: Run ClickHouse SQL queries on local files (Parquet, CSV, JSON), remote databases (MySQL, PostgreSQL), cloud storage (S3, GCS), and data lakes (Iceberg, Delta Lake) in a single query.
  • Stateful Analytical Pipelines: Build persistent or in-memory analytical workflows with Session objects that maintain table state across multiple queries.
  • Advanced SQL Support: Use parametrized queries, window functions, user-defined functions (UDFs), and cross-source joins for complex analytical tasks. For example, join MySQL customer data with S3-stored Parquet order records to calculate per-customer revenue without moving data between systems.

Quick Start

Use the chdb-sql skill to run a SQL query that joins your local 'customers.parquet' file with a remote MySQL 'orders' table to calculate total sales per customer.

Frequently Asked Questions about chdb-sql

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run ClickHouse SQL queries in Python without deploying a standalone server?▼

You can run ClickHouse SQL queries directly in Python processes without a standalone server deployment using chDB. It enables zero infrastructure overhead by executing analytical queries on local files, remote databases, and cloud storage within your Python environment.

Can I join local Parquet files with remote MySQL tables in a single SQL query?▼

Yes, you can perform cross-source joins in a single SQL query. This allows you to join local Parquet files with remote MySQL tables, or query PostgreSQL data alongside S3 cloud storage, without moving data between systems.

What's the best way to query data lakes like Iceberg and Delta Lake from Python?▼

The best way is to use chDB to run ClickHouse SQL queries directly on data lakes. It supports querying Iceberg and Delta Lake formats alongside local files and cloud storage, outputting results directly to pandas DataFrames or Apache Arrow formats.

Does chDB support advanced SQL features like window functions and parametrized queries?▼

Yes, chDB supports advanced SQL features including parametrized queries, window functions, and user-defined functions (UDFs). You can use these for complex analytical tasks and stateful pipeline building with persistent Session objects.

How do I stream large SQL result sets into pandas DataFrames for downstream analysis?▼

You can stream large result sets directly into pandas DataFrames or Apache Arrow formats. This allows you to execute heavy analytical queries on CSV, JSON, or Parquet files and immediately process the results in Python for downstream analysis.

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