infi.clickhouse-orm
A Python library for working with the ClickHouse database
Decision gist · record as of 2026-08-14
Yes, if you are actively using ClickHouse and prefer a Python ORM interface over raw SQL or another client library. The low install friction and permissive license are advantages. However, dormant maintenance since 2022-11-29 is a real concern—verify that it works with your ClickHouse server version and Python release before committing to it in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with no runtime dependencies.
- Maintenance is dormant—last release was 2022-11-29, over 1354 days ago, though the repository remains active with a recent commit on 2024-06-03.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing commercial and private use with minimal restrictions.
last release 2022-11-29 (1354 days) · last repo commit 2024-06-03 · 420 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 260,404 downloads/mo, #8,396 on PyPI
Alternatives
Verify before relying
from infi.clickhouse_orm import Database, Model, DateTimeField, UInt16Field, Float32Field, Memory
class CPUStats(Model):
timestamp = DateTimeField()
cpu_id = UInt16Field()
cpu_percent = Float32Field()
engine = Memory()
db = Database('demo')
db.create_table(CPUStats)
queryset = CPUStats.objects_in(db)
print(queryset.count())- Whether the package works with current ClickHouse server versions and modern Python releases.
- Whether dormant maintenance means bug fixes or security patches are unlikely if issues arise.
- Compatibility with ClickHouse protocol changes since the last release in 2022-11-29.
What it is and what it does
infi.clickhouse_orm is a lightweight Python ORM for ClickHouse, a columnar analytics database. It provides a declarative model system where you define table schemas as Python classes with typed fields (DateTimeField, UInt16Field, Float32Field, etc.), then insert and query data through a Python API. The package handles the connection to ClickHouse, table creation, bulk inserts, and result mapping back to model instances.
You can query using a fluent query builder (filter, aggregate, count) or drop into raw SQL when needed. The example in the documentation shows defining a CPU statistics model, collecting data in a loop, and then analyzing it with both query-builder and aggregation patterns. It has no external runtime dependencies, making installation straightforward, but the project has been dormant since 2022-11-29, so compatibility with recent ClickHouse versions or Python releases is not guaranteed.
Use it for
- Define ClickHouse table schemas as Python classes and create tables programmatically without raw DDL.
- Bulk insert time-series or event data into ClickHouse from Python applications.
- Query ClickHouse tables using a Python query builder instead of writing SQL strings directly.
- Map ClickHouse query results back to typed Python model instances for easier data handling.
- Prototype or migrate analytics workloads that use ClickHouse as the backend.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively using ClickHouse and prefer a Python ORM interface over raw SQL or another client library.
The low install friction and permissive license are advantages. However, dormant maintenance since 2022-11-29 is a real concern—verify that it works with your ClickHouse server version and Python release before committing to it in production.
Install
infi-clickhouse-orm on PyPI
Before you install
Low install friction with no runtime dependencies. Maintenance is dormant—last release was 2022-11-29, over 1354 days ago, though the repository remains active with a recent commit on 2024-06-03.
License in practice
BSD license is permissive, allowing commercial and private use with minimal restrictions.
Quickstart
from infi.clickhouse_orm import Database, Model, DateTimeField, UInt16Field, Float32Field, Memory
class CPUStats(Model):
timestamp = DateTimeField()
cpu_id = UInt16Field()
cpu_percent = Float32Field()
engine = Memory()
db = Database('demo')
db.create_table(CPUStats)
queryset = CPUStats.objects_in(db)
print(queryset.count())Verify before relying
- Whether the package works with current ClickHouse server versions and modern Python releases.
- Whether dormant maintenance means bug fixes or security patches are unlikely if issues arise.
- Compatibility with ClickHouse protocol changes since the last release in 2022-11-29.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 1,354 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 260,404 / month, #8,396 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: System AdministratorsLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 2.7Programming Language :: Python :: 3.4Topic :: DatabaseTopic :: Software Development :: Libraries :: Python Modules |
Evidence: infi.clickhouse_orm-2.1.3-py3-none-any.whl
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See also django-clickhouse-backend · django-clickhouse · clickhouse-driver · aiochclient · clickhouse-sqlalchemy · clickhouse-connect · clickhouse-migrations · SQLAlchemy · chdb · sqlalchemy-solr