# chdb **Repository Path**: mirrors/chdb ## Basic Information - **Project Name**: chdb - **Description**: chDB 是一个由 ClickHouse 驱动的嵌入式 SQL OLAP 引擎 - **Primary Language**: C/C++ - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: https://www.oschina.net/p/chdb - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2023-05-18 - **Last Updated**: 2025-10-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

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# 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 3.8+。 ```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") ``` ### 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)。 ## 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)