chdb
chDB is an in-process OLAP SQL Engine powered by ClickHouse
Decision gist · record as of 2026-08-14
Yes. chDB is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It fills a clear niche for developers who want SQL analytics performance without database infrastructure. The pandas-compatible API lowers the learning curve for existing pandas users, and the SQL API provides escape hatches for advanced ClickHouse features.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9+; supports macOS and Linux (x86_64 and ARM64) only.
- Low friction: pure Python wheel with three runtime dependencies (chdb-core, pandas, pyarrow).
- Active maintenance with a recent release (32 days old) and 2868 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-07-13 (32 days) · last repo commit 2026-08-12 · 2,868 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,493,256 downloads/mo, #2,601 on PyPI
Alternatives
Verify before relying
pip install chdb
import chdb
res = chdb.query('SELECT 1 as num', 'Pretty')
print(res)
# Or with pandas:
import pandas
df = pandas.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
print(df[df['age'] > 26])- Whether the pandas-compatible API is fully production-ready or still experimental.
- Performance characteristics and memory overhead when processing datasets larger than available RAM.
- Support status for Windows or other operating systems beyond macOS and Linux.
What it is and what it does
chDB embeds ClickHouse's SQL OLAP engine directly into Python, letting you run SQL queries on local files and in-memory data without installing a separate database server. It supports multiple input/output formats (Parquet, CSV, JSON, Arrow, ORC, and 60+ others) and offers two APIs: a low-level SQL interface for direct queries, and a pandas-compatible API that compiles operations to optimized SQL.
The package is designed for analytics workflows where you need SQL performance on moderately large datasets without the operational overhead of a database server. It uses lazy evaluation to defer execution until results are needed, and minimizes data transfer between C++ and Python through memory views. Runtime dependencies include chdb-core, pandas, and pyarrow.
Use it for
- Analyze Parquet or CSV files with SQL queries without loading them entirely into memory.
- Use familiar pandas syntax while getting ClickHouse SQL performance on large datasets.
- Execute SQL queries on multiple file formats and data sources with a unified interface.
- Build interactive data exploration workflows in Jupyter notebooks with lazy evaluation.
- Perform aggregations and groupby operations on large datasets using ClickHouse's multi-threaded engine.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
chDB is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It fills a clear niche for developers who want SQL analytics performance without database infrastructure. The pandas-compatible API lowers the learning curve for existing pandas users, and the SQL API provides escape hatches for advanced ClickHouse features.
Install
chdb on PyPI
Before you install
Low friction: pure Python wheel with three runtime dependencies (chdb-core, pandas, pyarrow). Active maintenance with a recent release (32 days old) and 2868 repository stars.
Requires Python 3.9+; supports macOS and Linux (x86_64 and ARM64) only.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install chdb
import chdb
res = chdb.query('SELECT 1 as num', 'Pretty')
print(res)
# Or with pandas:
import pandas
df = pandas.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
print(df[df['age'] > 26])Verify before relying
- Whether the pandas-compatible API is fully production-ready or still experimental.
- Performance characteristics and memory overhead when processing datasets larger than available RAM.
- Support status for Windows or other operating systems beyond macOS and Linux.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packageschdb-corepandaspyarrow |
| Maintenance | Actively maintained 32 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,493,256 / month, #2,601 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: PluginsIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: DatabaseTopic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: chdb-4.2.1-py3-none-any.whl
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See also chdb-core · mcp-clickhouse · duckdb · clickhouse-connect · clickhouse-driver · clickhouse-migrations · qpd · airflow-clickhouse-plugin · aiochclient · asynch