Chdb Datastore
Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and...
Install
npx skills add ClickHouse/agent-skills --skill chdb-datastore -g -a claude-code -y- Repository
- ClickHouse/agent-skills565
- Category
- Data and ML
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,149 tokens
- Runs scripts
- Yes, review before use
What it does
Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore, same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.
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
Connect to Any Data Source, One Pattern
from datastore import DataStore
# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)
ds = DataStore.from_file("sales.parquet")
# Database
ds = DataStore.from_mysql(host="db:3306", database="shop", table="orders", user="root", password="pass")
# Cloud storage
ds = DataStore.from_s3("s3://bucket/data.parquet", nosign=True)
# URI shorthand, auto-detects source type
ds = DataStore.uri("mysql://root:pass@db:3306/shop/orders")
All 16+ sources and URI schemes → connectors.md
