chdb-sql

chdb SQL — ClickHouse in Your Python Process

Run ClickHouse SQL directly in Python — no server needed. Query local files, remote databases, and cloud storage with full ClickHouse SQL power.

pip install chdb

Decision Tree: Pick the Right API

1. One-off query on files or databases → chdb.query()
2. Multi-step analysis with tables      → Session
3. DB-API 2.0 connection                → chdb.connect()
4. Pandas-style DataFrame operations    → Use chdb-datastore skill instead

chdb.query() — One Line, Any Data

import chdb

chdb.query("SELECT * FROM file('data.parquet', Parquet) WHERE price > 100 LIMIT 10")       # local files
chdb.query("SELECT * FROM mysql('db:3306', 'shop', 'orders', 'root', 'pass')")              # databases
chdb.query("SELECT * FROM s3('s3://bucket/data.parquet', NOSIGN) LIMIT 10")                 # cloud storage
chdb.query("SELECT * FROM deltaLake('s3://bucket/delta/table', NOSIGN) LIMIT 10")           # data lakes

# Cross-source join
chdb.query("""
    SELECT u.name, o.amount FROM mysql('db:3306', 'crm', 'users', 'root', 'pass') AS u
    JOIN file('orders.parquet', Parquet) AS o ON u.id = o.user_id ORDER BY o.amount DESC
""")

data = {"name": ["Alice", "Bob"], "score": [95, 87]}
chdb.query("SELECT * FROM Python(data) ORDER BY score DESC")                                # Python data
df = chdb.query("SELECT * FROM numbers(10)", "DataFrame")                                   # output formats
chdb.query("SELECT toDate({d:String}) + number FROM numbers({n:UInt64})",
    "DataFrame", params={"d": "2025-01-01", "n": 30})                                      # parametrized

Table functions → table-functions.md | SQL functions → sql-functions.md | Full API → api-reference.md

Session — Stateful Analysis Pipelines

from chdb import session as chs
sess = chs.Session("./analytics_db")   # persistent; Session() for in-memory

sess.query("CREATE TABLE users ENGINE=MergeTree() ORDER BY id AS SELECT * FROM mysql('db:3306','crm','users','root','pass')")
sess.query("CREATE TABLE events ENGINE=MergeTree() ORDER BY (ts,user_id) AS SELECT * FROM s3('s3://logs/events/*.parquet',NOSIGN)")
sess.query("""
    SELECT u.country, count() AS cnt, uniqExact(e.user_id) AS users
    FROM events e JOIN users u ON e.user_id = u.id
    WHERE e.ts >= today() - 7 GROUP BY u.country ORDER BY cnt DESC
""", "Pretty").show()
sess.close()

Connection API (DB-API 2.0)

from chdb import dbapi
conn = dbapi.connect()
cur = conn.cursor()
cur.execute("SELECT * FROM file('data.parquet', Parquet) WHERE value > 100")
print(cur.fetchall())
cur.close()
conn.close()

Troubleshooting

Problem	Fix
ImportError: No module named 'chdb'	pip install chdb
DB::Exception: FILE_NOT_FOUND	Check file path; use absolute path or verify cwd
DB::Exception: Unknown table function	Check function name spelling (e.g., deltaLake not deltalake)
Connection refused to remote DB	Check host:port format; ensure remote DB allows connections
Environment check	Run python scripts/verify_install.py (from skill directory)

References

• API Reference — query/Session/connect signatures
• Table Functions — All ClickHouse table functions
• SQL Functions — Commonly used SQL functions
• Examples — 9 runnable examples with expected output
• Official Docs

Note: This skill teaches how to use chdb SQL. For pandas-style operations, use the chdb-datastore skill. For contributing to chdb source code, see CLAUDE.md in the project root.

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chdb SQL

Agent skill for using chdb's SQL API — run ClickHouse SQL directly in Python without a server.

Installation

npx skills add clickhouse/agent-skills

What's Included

File	Purpose
SKILL.md	Skill definition with quick-start examples
references/api-reference.md	chdb.query(), Session, Connection signatures
references/table-functions.md	All ClickHouse table functions (file, s3, mysql, etc.)
references/sql-functions.md	Commonly used ClickHouse SQL functions
examples/examples.md	9 runnable examples with expected output
scripts/verify_install.py	Environment verification script

Trigger Phrases

This skill activates when you:

• "Query this Parquet/CSV file with SQL"
• "Use chdb to run a query"
• "Join MySQL and S3 data with SQL"
• "Create a ClickHouse session"
• "Use ClickHouse table functions"
• "Write a parametrized query"

Related

• chdb-datastore — For pandas-style DataFrame operations, use the chdb-datastore skill instead
• clickhouse-best-practices — For ClickHouse schema/query optimization

Documentation

• chdb docs
• chdb GitHub