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. ```bash 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 ```python 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](references/table-functions.md) | SQL functions → [sql-functions.md](references/sql-functions.md) | Full API → [api-reference.md](references/api-reference.md) ## Session — Stateful Analysis Pipelines ```python 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) ```python 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](references/api-reference.md) — query/Session/connect signatures - [Table Functions](references/table-functions.md) — All ClickHouse table functions - [SQL Functions](references/sql-functions.md) — Commonly used SQL functions - [Examples](examples/examples.md) — 9 runnable examples with expected output - [Official Docs](https://clickhouse.com/docs/chdb) > 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.