fastuuid7
High-performance, fork-safe UUIDv7 generation for Python 3.9-3.14, implemented
in C and compatible with Python's uuid.UUID API.
Features
- RFC 9562 UUIDv7 layout with monotonic ordering within a millisecond
- Random fields sourced from the operating-system CSPRNG
- Automatic entropy and counter reset after a process fork
uuid.uuid7()-compatible API returninguuid.UUID- Native object, canonical string, and raw bytes fast paths
- Exact historical timestamps with
uuid7_at()anduuid7_at_many() - Python 3.9-3.14 on Linux, macOS, and Windows
- Typed
fastuuid7and backward-compatibleuuidv7imports
Python 3.14 includes uuid.uuid7() in the standard library. Use the standard
library when it meets your needs. fastuuid7 is intended for older Python
versions and workloads that benefit from its explicit output shapes and lower
per-call overhead.
Installation
Using uv
uv add fastuuid7
Using pip
pip install fastuuid7
From source
git clone https://github.com/nekrasovp/uuidv7.git
cd uuidv7
uv pip install -e .
Usage
Basic Usage
from fastuuid7 import uuid7
# Generate a UUID v7 (matches Python's uuid.uuid7() API)
u = uuid7()
print(u) # e.g., 018f1234-5678-7abc-def0-123456789abc
print(repr(u)) # e.g., UUID('018f1234-5678-7abc-def0-123456789abc')
print(u.time) # Unix timestamp in milliseconds
uuid7() returns a uuid.UUID object, matching Python's built-in
uuid.uuid7() function available in Python 3.14+. Use str(uuid7()) when a
string is needed. See
Python documentation
for details.
Fast Paths
For performance-critical code that does not need a uuid.UUID object:
from fastuuid7 import (
uuid7_bytes,
uuid7_bytes_many,
uuid7_many,
uuid7_obj,
uuid7_str,
)
uuid_native = uuid7_obj()
uuid_text = uuid7_str()
uuid_raw = uuid7_bytes()
uuid_batch = uuid7_many(1_000)
packed_batch = uuid7_bytes_many(1_000) # 16,000 contiguous bytes
uuid7() remains the compatibility API. uuid7_obj() returns a compact native
UUIDv7 object for maximum throughput while still supporting str(), int(),
bytes(), ordering, hashing, .time, .hex, .fields, .version, and
.variant. uuid7_str() and uuid7_bytes() are explicit raw-output fast
paths. The *_many() functions generate a batch in one C call;
uuid7_bytes_many() uses one contiguous bytes allocation.
The original from uuidv7 import ... path remains supported for existing
applications.
For historical records use uuid7_at(unix_ms=1_645_557_742_123). It preserves
the exact timestamp and uses fresh OS randomness without changing the live
generator's monotonic counter. Repeated historical calls are not ordered.
See the backfill recipe.
For a chunk of historical records, pass their timestamps in source order:
from fastuuid7 import uuid7_at_many
ids = uuid7_at_many(unix_ms=[1_645_557_742_123, 1_645_557_742_124])
assert [item.time for item in ids] == [1_645_557_742_123, 1_645_557_742_124]
The entire finite iterable is consumed and validated before generation. Save assigned IDs with source keys so retries can reuse them; see the executable resumable migration example.
See the API reference for complete scalar and batch contracts.
Security properties
Random fields are read from a buffered operating-system CSPRNG. The generator
detects PID changes and discards inherited entropy and counters after fork().
UUIDv7 embeds its creation timestamp and is therefore an identifier, not a
secret or authentication token. See the security policy.
Examples
For more detailed usage examples, see the examples/ directory:
- Basic Usage - Simple UUID generation, validation, and performance demo
- Batch Generation - High-throughput UUID generation and uniqueness verification
- Database Usage - Real SQLAlchemy 2 primary-key integration
- Integration Recipes - Django, Pydantic/FastAPI, and PostgreSQL
- Integration Tests - Executed ORM, driver, validation and UUID compatibility checks
- Adoption Guide - Choosing a generator, public usage evidence and feedback
Quick Start:
# Install the package first (required)
uv pip install -e .
# Run examples using python -m (recommended)
python -m examples.basic_usage
python -m examples.batch_generation
uv run --with sqlalchemy python -m examples.database_usage
# Or using uv run
uv run python -m examples.basic_usage
See the examples README for more details.
Development
Setup
# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install the project and development dependencies
uv sync --extra dev --locked
Running Tests
# Using pytest
uv run --extra dev --locked pytest
# Using uv
uv run --extra dev --locked pytest tests/
Linting and Formatting
# Run ruff linter
uv run --extra dev --locked ruff check .
# Run ruff formatter
uv run --extra dev --locked ruff format .
# Fix auto-fixable issues
uv run --extra dev --locked ruff check --fix .
# Check the typed public API
uv run --extra dev --locked mypy
Building
# Build wheel
uv build
# Build source distribution
uv build --sdist
Running Benchmarks
# Run performance benchmarks comparing different implementations
python benchmarks/benchmark.py
# Or using uv
uv run python benchmarks/benchmark.py
Performance Benchmarks
Run benchmarks before making speed claims:
python benchmarks/benchmark.py --output benchmark-results.md
python benchmarks/benchmark_competitors.py --install-optional --rounds 5 --output competitor-results.md
python benchmarks/benchmark_batch.py --output batch-results.md
python benchmarks/clock_sources.py --output clock-source-results.md
python benchmarks/batch_clock.py --output batch-clock-results.md
The benchmark report includes OS, CPU, Python version, package versions, iterations, UUIDs/second, and ns/op for:
fastuuid7.uuid7()returninguuid.UUIDfastuuid7.uuid7_obj()returning a compact native UUIDv7 objectfastuuid7.uuid7_str()fastuuid7.uuid7_bytes()str(fastuuid7.uuid7())- Python stdlib
uuid.uuid7()when available - published
fastuuid7==0.4.0in an isolated temporary environment - optional competitors when installed:
uuid-utils,fastuuidv7,uuid7,uuid7-rs,c_uuid_v7,uuid-v7, anduuid6
Published benchmark results must describe both the output shape and generation guarantees. In particular, CSPRNG-backed and non-cryptographic generators are not presented as equivalent cases. Release benchmarks are generated in CI from the exact release candidate rather than copied from a developer workstation.
The PostgreSQL workload harness measures full SQLAlchemy bulk INSERT, binary COPY and historical import, including persisted row verification. It separates client libraries from PostgreSQL 18 server-side generation and records time, client CPU and memory. Use end-to-end measurements to decide whether generator changes matter for your workload.
Python 3.15.0rc2 is checked in a separate prerelease matrix; the stable support range remains 3.9–3.14. The free-threading research is an isolated prototype and does not enable production generation without the GIL.
CI/CD
This project uses GitHub Actions for continuous integration and deployment:
-
CI Pipeline (
.github/workflows/ci.yml):- Runs tests on Python 3.9 through 3.14
- Runs linting with ruff
- Builds the package to verify it compiles correctly
- Triggers on push and pull requests
-
Wheel Pipeline (
.github/workflows/wheels.yml):- Builds glibc and musl Linux wheels for x86-64 and ARM64
- Builds macOS x86-64/ARM64 and Windows x86/x86-64/ARM64 wheels
- Verifies both import paths and the installed batch API
-
Publish Pipeline (
.github/workflows/publish.yml):- Automatically publishes to PyPI when a new release is created
- Builds platform wheels with cibuildwheel plus an sdist before publishing
- Uses trusted publishing (no API tokens required)
- Can be manually triggered via workflow_dispatch, but GitHub Releases are the recommended release path
Publishing a New Release
Follow the complete release checklist, before publishing.
- Run tests, builds, and benchmarks.
- Review benchmark results and decide whether optimization is needed.
- Verify all source versions with
python tools/check_release_version.py v0.5.0. - Create a new GitHub Release.
- The workflow will validate, build, inspect, and publish the distributions.
License
MIT License - see LICENSE file for details.
Author
Pavel Nekrasov
Metadata
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