Technical & DevelopmentIntermediate
chdb-datastore
Drop-in pandas replacement with ClickHouse performance.
Developer Setup
Setup & Installation
bash
npx skills add https://github.com/ClickHouse/agent-skills --skill chdb-datastorenpx skills add https://github.com/ClickHouse/agent-skills --skill chdb-datastoreOr paste this URL into your assistant to install:
Overview
What This Skill Does
Enables drop-in pandas-style DataFrame operations compiled to optimized ClickHouse query plans, facilitating fast cross-source joins.
Application
When to use this Skill
- Importing pandas-like fast DataFrames backed by ClickHouse's execution engine.
- Analyzing large datasets that cause traditional pandas code to bottleneck.
- Connecting to remote databases or cloud object stores as pandas DataFrames.
- Executing cross-source joins between local files, S3, and relational databases.
- Writing data back to multiple formats like Parquet, CSV, or database tables.
Documentation
Show Skills.md file
chdb DataStore — It's Just Faster Pandas
The Key Insight
# Change this:
import pandas as pd
# To this:
import chdb.datastore as pd
# Everything else stays the same.
DataStore is a lazy, ClickHouse-backed pandas replacement. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., print(), len(), iteration).
pip install chdb
Decision Tree: Pick the Right Approach
1. "I have a file/database and want to analyze it with pandas"
→ DataStore.from_file() / from_mysql() / from_s3() etc.
→ See references/connectors.md
2. "I need to join data from different sources"
→ Create DataStores from each source, use .join()
→ See examples/examples.md #3-5
3. "My pandas code is too slow"
→ import chdb.datastore as pd — change one line, keep the rest
4. "I need raw SQL queries"
→ Use the chdb-sql skill instead
Lines 1 - 35 of 124
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