https://github.com/chdb-io/chdb

chDB is an in-process OLAP SQL Engine 🚀 powered by ClickHouse
https://github.com/chdb-io/chdb

chdb clickhouse clickhouse-database clickhouse-server data-science database embedded-database olap python sql

Last synced: 16 days ago
JSON representation

chDB is an in-process OLAP SQL Engine 🚀 powered by ClickHouse

Host: GitHub
URL: https://github.com/chdb-io/chdb
Owner: chdb-io
License: apache-2.0
Created: 2023-02-25T13:35:14.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2026-02-26T12:56:50.000Z (27 days ago)
Last Synced: 2026-02-26T13:51:18.769Z (27 days ago)
Topics: chdb, clickhouse, clickhouse-database, clickhouse-server, data-science, database, embedded-database, olap, python, sql
Language: C++
Homepage: https://clickhouse.com/chdb
Size: 893 MB
Stars: 2,618
Watchers: 31
Forks: 102
Open Issues: 43
Metadata Files:
- Readme: README-zh.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Authors: AUTHORS.md

Awesome Lists containing this project

stars - chdb-io/chdb - process OLAP SQL Engine 🚀 powered by ClickHouse (HarmonyOS / Windows Manager)
awesome-clickhouse - chdb-io/chdb - chDB is an in-process OLAP SQL engine powered by ClickHouse that enables efficient analytical queries on various data formats directly within Python environments. (Language bindings / Python)
my-awesome - chdb-io/chdb - database,clickhouse-server,data-science,database,embedded-database,olap,python,sql pushed_at:2026-02 star:2.6k fork:0.1k chDB is an in-process OLAP SQL Engine 🚀 powered by ClickHouse (C++)
awesome - chdb-io/chdb - chDB is an in-process OLAP SQL Engine 🚀 powered by ClickHouse (<a name="C%2B%2B"></a>C++)

README

          


   📢 chDB joins the ClickHouse family 🐍+🚀





  

  

[![构建状态](https://github.com/chdb-io/chdb/actions/workflows/build_linux_x86_wheels.yml/badge.svg?branch=main)](https://github.com/chdb-io/chdb/actions/workflows/build_linux_x86_wheels.yml)

[![PyPI](https://img.shields.io/pypi/v/chdb.svg)](https://pypi.org/project/chdb/)

[![Downloads](https://static.pepy.tech/badge/chdb)](https://pepy.tech/project/chdb)

[![Discord](https://img.shields.io/discord/1098133460310294528?logo=Discord)](https://discord.gg/D2Daa2fM5K)

[![Twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter)](https://twitter.com/chdb_io)



# chDB

[English](README.md)

> chDB 是一个由 ClickHouse 驱动的嵌入式 SQL OLAP 引擎。更多细节：[chDB: ClickHouse as a Function](https://zhuanlan.zhihu.com/p/642345300)

## 特点

     

* 嵌入在 Python 中的 SQL OLAP 引擎，由 ClickHouse 驱动

* 不需要安装 ClickHouse

* 支持 Parquet、CSV、JSON、Arrow、ORC 和其他 60 多种格式的[输入输出](https://clickhouse.com/docs/en/interfaces/formats)，[示例](tests/format_output.py)。

* 支持 Python DB API 2.0 标准, [example](examples/dbapi.py)

## 架构



  



## 安装方式

目前，chDB 只支持在 macOS（x86_64 和 ARM64）和 Linux 上的 Python 3.9+。

```bash

pip install chdb

```

## 用法

### 在命令行中运行

> `python3 -m chdb SQL [OutputFormat]`

```bash

python3 -m chdb "SELECT 1,'abc'" Pretty

```

有三种使用 chdb 的方法：“原始文件查询（性能）”、“高级查询（推荐）”和“DB-API”：

    
🗂️ 原始文件查询
（Parquet、CSV、JSON、Arrow、ORC 等 60 多种格式）

您可以执行 SQL 并返回所需格式的数据。

```python

import chdb

res = chdb.query('select version()', 'Pretty'); print(res)

```

### 使用 Parquet 或 CSV

```python

# 查看更多数据类型格式，请参见 tests/format_output.py

res = chdb.query('select * from file("data.parquet", Parquet)', 'JSON'); print(res)

res = chdb.query('select * from file("data.csv", CSV)', 'CSV');  print(res)

print(f"SQL read {res.rows_read()} rows, {res.bytes_read()} bytes, elapsed {res.elapsed()} seconds")

```

### 参数化查询

```python

import chdb

df = chdb.query(

    "SELECT toDate({base_date:String}) + number AS date "

    "FROM numbers({total_days:UInt64}) "

    "LIMIT {items_per_page:UInt64}",

    "DataFrame",

    params={"base_date": "2025-01-01", "total_days": 10, "items_per_page": 2},

)

print(df)

#         date

# 0 2025-01-01

# 1 2025-01-02

```

### 查询进度（`progress=auto`）

```python

import chdb

# Connection API

conn = chdb.connect(":memory:?progress=auto")

conn.query("SELECT sum(number) FROM numbers_mt(1e10) GROUP BY number % 10 SETTINGS max_threads=4")

```

```python

import chdb

# 一次性 query API

res = chdb.query(

    "SELECT sum(number) FROM numbers_mt(1e10) GROUP BY number % 10 SETTINGS max_threads=4",

    options={"progress": "auto"},

)

```

`progress=auto` 的行为：

- 在终端运行时：在终端中显示文本进度更新。

- 在 Jupyter/Marimo 中：在 notebook 输出区域渲染进度。

其他进度选项：

- 进度条：

  - `progress=tty`：将进度输出到终端 TTY。

  - `progress=err`：将进度输出到 `stderr`。

  - `progress=off`：关闭进度条输出。

- 进度表（终端输出）：

  - `progress-table=tty`：将进度表输出到终端 TTY。

  - `progress-table=err`：将进度表输出到 `stderr`。

  - `progress-table=off`：关闭进度表输出。

更多内容请参见:

* [ClickHouse SQL语法: 定义和使用查询参数](https://clickhouse.com/docs/sql-reference/syntax#defining-and-using-query-parameters)

* [ClickHouse中如何使用参数化查询](https://clickhouse.com/videos/how-to-use-query-parameters-in-clickhouse)

### Pandas DataFrame 输出

```python

# 更多内容请参见 https://clickhouse.com/docs/en/interfaces/formats

chdb.query('select * from file("data.parquet", Parquet)', 'Dataframe')

```

    
🗂️ 高级查询
（Pandas DataFrame、Parquet 文件/字节、Arrow 文件/字节）

### 查询 Pandas DataFrame

```python

import chdb.dataframe as cdf

import pandas as pd

# Join 2 DataFrames

df1 = pd.DataFrame({'a': [1, 2, 3], 'b': ["one", "two", "three"]})

df2 = pd.DataFrame({'c': [1, 2, 3], 'd': ["①", "②", "③"]})

ret_tbl = cdf.query(sql="select * from __tbl1__ t1 join __tbl2__ t2 on t1.a = t2.c",

                  tbl1=df1, tbl2=df2)

print(ret_tbl)

# Query on the DataFrame Table

print(ret_tbl.query('select b, sum(a) from __table__ group by b'))

```

  
🗂️ 基于有状态会话 Session 查询


```python

from chdb import session as chs

## 在临时会话中创建DB, Table, View，当会话被删除时自动清除。

sess = chs.Session()

sess.query("CREATE DATABASE IF NOT EXISTS db_xxx ENGINE = Atomic")

sess.query("CREATE TABLE IF NOT EXISTS db_xxx.log_table_xxx (x String, y Int) ENGINE = Log;")

sess.query("INSERT INTO db_xxx.log_table_xxx VALUES ('a', 1), ('b', 3), ('c', 2), ('d', 5);")

sess.query(

    "CREATE VIEW db_xxx.view_xxx AS SELECT * FROM db_xxx.log_table_xxx LIMIT 4;"

)

print("Select from view:\n")

print(sess.query("SELECT * FROM db_xxx.view_xxx", "Pretty"))

```

参见: [test_stateful.py](tests/test_stateful.py)

    
🗂️ Python DB-API 2.0


```python

import chdb.dbapi as dbapi

print("chdb driver version: {0}".format(dbapi.get_client_info()))

conn1 = dbapi.connect()

cur1 = conn1.cursor()

cur1.execute('select version()')

print("description: ", cur1.description)

print("data: ", cur1.fetchone())

cur1.close()

conn1.close()

```

    
🗂️ Query with UDF(User Defined Functions)


```python

from chdb.udf import chdb_udf

from chdb import query

@chdb_udf()

def sum_udf(lhs, rhs):

    return int(lhs) + int(rhs)

print(query("select sum_udf(12,22)"))

```

参见: [test_udf.py](tests/test_udf.py).

    
🗂️ 流式查询


通过分块流式处理大数据集，保持内存使用恒定。

```python

from chdb import session as chs

sess = chs.Session()

# 示例1：流式查询基础用法

rows_cnt = 0

with sess.send_query("SELECT * FROM numbers(200000)", "CSV") as stream_result:

    for chunk in stream_result:

        rows_cnt += chunk.rows_read()

print(rows_cnt) # 200000

# 示例2：使用fetch()手动迭代

rows_cnt = 0

stream_result = sess.send_query("SELECT * FROM numbers(200000)", "CSV")

while True:

    chunk = stream_result.fetch()

    if chunk is None:

        break

    rows_cnt += chunk.rows_read()

print(rows_cnt) # 200000

# 示例3：提前取消查询

rows_cnt = 0

stream_result = sess.send_query("SELECT * FROM numbers(200000)", "CSV")

while True:

    chunk = stream_result.fetch()

    if chunk is None:

        break

    if rows_cnt > 0:

        stream_result.close()

        break

    rows_cnt += chunk.rows_read()

print(rows_cnt) # 65409

# 示例4：使用PyArrow RecordBatchReader进行批量导出以及与其他库集成

import pyarrow as pa

from deltalake import write_deltalake

# 获取arrow格式的流式结果

stream_result = sess.send_query("SELECT * FROM numbers(100000)", "Arrow")

# 创建自定义批次大小的RecordBatchReader（默认rows_per_batch=1000000）

batch_reader = stream_result.record_batch(rows_per_batch=10000)

# 将RecordBatchReader与外部库（如Delta Lake）一起使用

write_deltalake(

    table_or_uri="./my_delta_table",

    data=batch_reader,

    mode="overwrite"

)

stream_result.close()

sess.close()

```

**重要提示**：使用流式查询时，如果`StreamingResult`没有被完全消耗（由于错误或提前终止），必须显式调用`stream_result.close()`来释放资源，或使用`with`语句进行自动清理。否则可能会阻塞后续查询。

参见: [test_streaming_query.py](tests/test_streaming_query.py) 和 [test_arrow_record_reader_deltalake.py](tests/test_arrow_record_reader_deltalake.py)。

更多示例，请参见 [examples](examples) 和 [tests](tests)。

  
🧠 AI 辅助 SQL 生成


chDB 可以将自然语言提示转换为 SQL。通过连接/会话字符串配置 AI 客户端参数：

- `ai_provider`：`openai` 或 `anthropic`。当设置了 `ai_base_url` 时默认使用 OpenAI 兼容接口，否则自动检测。

- `ai_api_key`：API 密钥；也可从环境变量 `AI_API_KEY`、`OPENAI_API_KEY` 或 `ANTHROPIC_API_KEY` 读取。

- `ai_base_url`：OpenAI 兼容服务的自定义 Base URL。

- `ai_model`：模型名称（如 `gpt-4o-mini`、`claude-3-opus-20240229`）。

- `ai_temperature`：生成温度，默认 `0.0`。

- `ai_max_tokens`：最大全量生成 token 数，默认 `1000`。

- `ai_timeout_seconds`：请求超时时间（秒），默认 `30`。

- `ai_system_prompt`：自定义系统提示词。

- `ai_max_steps`：工具调用的最大步数，默认 `5`。

- `ai_enable_schema_access`：允许 AI 查看数据库/表元数据，默认 `true`。

未开启 AI 或配置缺失时，调用 `generate_sql`/`ask` 会抛出 `RuntimeError`。

```python

import chdb

# 使用环境变量 OPENAI_API_KEY/AI_API_KEY/ANTHROPIC_API_KEY 提供凭据

conn = chdb.connect("file::memory:?ai_provider=openai&ai_model=gpt-4o-mini")

conn.query("CREATE TABLE nums (n UInt32) ENGINE = Memory")

conn.query("INSERT INTO nums VALUES (1), (2), (3)")

sql = conn.generate_sql("Select all rows from nums ordered by n desc")

print(sql)  # 例如：SELECT * FROM nums ORDER BY n DESC

# ask()：一键生成并执行 SQL

# `ask()` 会先调用 `generate_sql` 再执行 `query`，关键字参数会透传给 `query`。

print(conn.ask("List the numbers table", format="Pretty"))

```

`Session` 同样支持以上能力；`Session.ask()` 会将关键字参数透传给 `Session.query`：

```python

from chdb import session as chs

with chs.Session("file::memory:?ai_provider=openai") as sess:

    sess.query("CREATE TABLE users (id UInt32, name String) ENGINE = Memory")

    sess.query("INSERT INTO users VALUES (1, 'alice'), (2, 'bob')")

    df = sess.ask("Show all users ordered by id", format="DataFrame")

    print(df)

```

## 演示和示例

- [Colab Notebook](https://colab.research.google.com/drive/1-zKB6oKfXeptggXi0kUX87iR8ZTSr4P3?usp=sharing) 和更多 [示例](examples)

## 基准测试

- [ClickBench of embedded engines](https://benchmark.clickhouse.com/#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)

- [chDB vs Pandas](https://colab.research.google.com/drive/1FogLujJ_-ds7RGurDrUnK-U0IW8a8Qd0)

- [Benchmark on DataFrame: chDB Pandas DuckDB Polars](https://benchmark.clickhouse.com/#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)



    



## 文档

- 关于 SQL 语法，请参考 [ClickHouse SQL 参考](https://clickhouse.com/docs/en/sql-reference/syntax)

## 贡献

贡献是使开源社区成为一个学习、激励和创造的绝佳场所的原因。您做出的任何贡献都将受到**高度赞赏**。

以下是您可以提供帮助的事项：

- 「Star」和「分享」

- [ ] 帮助测试和报告错误

- [ ] 帮助改进文档

- [ ] 帮助提高代码质量和性能

## 事件

- Demo chDB at [ClickHouse v23.7 livehouse!](https://t.co/todc13Kn19) and [Slides](https://docs.google.com/presentation/d/1ikqjOlimRa7QAg588TAB_Fna-Tad2WMg7_4AgnbQbFA/edit?usp=sharing)

## 版本说明

请查看 [VERSION-GUIDE.md](VERSION-GUIDE.md) 获取更多信息。

## 相关论文

- [ClickHouse - Lightning Fast Analytics for Everyone](https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf)

## 版权信息

Apache 2.0，请查看 [LICENSE](LICENSE.txt) 获取更多信息。

## 鸣谢

chDB 主要基于 [ClickHouse](https://github.com/ClickHouse/ClickHouse)。由于商标和其他原因，我将其命名为 chDB。

## 联系方式

- 知乎: [@auxten](https://www.zhihu.com/people/auxten)

- Discord：[https://discord.gg/D2Daa2fM5K](https://discord.gg/D2Daa2fM5K)

- 电子邮件：auxten@clickhouse.com

- Twitter：[@chdb](https://twitter.com/chdb_io)

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/chdb-io/chdb

Awesome Lists containing this project

README

🗂️ 原始文件查询

🗂️ 高级查询

🗂️ 基于有状态会话 Session 查询

🗂️ Python DB-API 2.0

🗂️ Query with UDF(User Defined Functions)

🗂️ 流式查询

🧠 AI 辅助 SQL 生成