CHDBSQL
NVlabs2026-10-25查看 628复制 1918
>- 当用户想要在本地文件(parquet/csv/json)、URL、S3 路径或远程数据库 (Postgres、MySQL、MongoDB、ClickHouse Cloud、Iceberg、Delta Lake) 上运行 SQL——尤其是分析型 SQL——而无需搭建服务器时使用。 提供 chDB——Python 中嵌入式的 ClickHouse SQL, 拥有 1000+ 函数、用于有状态多步流水线的 Session、 参数化查询,以及通过 `s3()`、 `mysql()`、`postgresql()`、`iceberg()`、`deltaLake()`、`remoteSecure()` 表函数实现的跨源连接。 触发条件:用户想要在 parquet/csv/文件上或跨远程 分析源运行 SQL;使用 ClickHouse SQL 特性(窗口函数、 windowFunnel、geoToH3、JSON 路径操作、Session、参数化查询); 导入 `chdb` 或调用 `chdb.query()`。 对于 pandas 风格的 DataFrame 方法链(改用 chdb-datastore)或 ClickHouse 服务器管理,跳过此技能。
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SKILL.md4947 chars
---
name: chdb-sql
description: >-
Use when the user wants to run SQL — especially analytical SQL — on
local files (parquet/csv/json), URLs, S3 paths, or remote databases
(Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake)
without setting up a server. Provides chDB — embedded ClickHouse SQL
in Python with 1000+ functions, Session for stateful multi-step
pipelines, parametrized queries, and cross-source joins via `s3()`,
`mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()`
table functions.
TRIGGER when: user wants SQL on parquet/csv/files or across remote
analytical sources; uses ClickHouse SQL features (window functions,
windowFunnel, geoToH3, JSON path ops, Session, parametrized queries);
imports `chdb` or calls `chdb.query()`.
SKIP this skill for pandas-style DataFrame method-chaining (use
chdb-datastore instead) or ClickHouse server administration.
license: Apache-2.0
compatibility: Requires Python 3.9+, macOS or Linux. pip install chdb.
metadata:
author: chdb-io
version: "4.1"
homepage: https://clickhouse.com/docs/chdb
---
# 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.