Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/clickhouse/agent-skills --skill chdb-sqlPrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
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 chdbDecision 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 insteadchdb.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}) # parametrizedTable functions → table-functions.md | SQL functions → sql-functions.md | Full API → 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 — 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.
