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High-Performance Lightweight Python ORM designed for
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Python ORM for RayforceDB, a high-performance columnar database designed for analytics and data operations. Core is written in pure C with minimal overhead - combines columnar storage with SIMD vectorization for lightning-fast analytics on time-series and big data workloads.
Full Documentation: https://py.rayforcedb.com/
Features
- Pythonic API - Chainable, convenient, intuitive and fluent query syntax
- High Performance - One of the fastest solutions available, minimal overhead between Python and RayforceDB runtime via C API
- Lightweight - Core is less than 1 MB footprint
- 0 dependencies - Library operates without dependencies - pure Python and C
- Rapid Development - Continuously expanding functionality
Benchmarks run on: macOS M4 32GB, 100 groups, 20 runs (median), 5 warmup runs. H2OAI
Quick Start
>>> from datetime import time
>>> from rayforce import Table, Column, Vector, Symbol, Time, F64
>>> quotes = Table({
"symbol": Vector(items=["AAPL", "AAPL", "AAPL", "GOOG", "GOOG", "GOOG"], ray_type=Symbol),
"time": Vector(
items=[
time.fromisoformat("09:00:00.095"),
time.fromisoformat("09:00:00.105"),
time.fromisoformat("09:00:00.295"),
time.fromisoformat("09:00:00.145"),
time.fromisoformat("09:00:00.155"),
time.fromisoformat("09:00:00.345"),
],
ray_type=Time,
),
"bid": Vector(items=[100.0, 101.0, 102.0, 200.0, 201.0, 202.0], ray_type=F64),
"ask": Vector(items=[110.0, 111.0, 112.0, 210.0, 211.0, 212.0], ray_type=F64),
})
>>> result = (
quotes
.select(
max_bid=Column("bid").max(),
min_bid=Column("bid").min(),
avg_ask=Column("ask").mean(),
records_count=Column("time").count(),
first_time=Column("time").first(),
)
.where((Column("bid") >= 110) & (Column("ask") > 100))
.by("symbol")
.execute()
)
>>> print(result)
┌────────┬─────────┬─────────┬─────────┬───────────────┬──────────────┐
│ symbol │ max_bid │ min_bid │ avg_ask │ records_count │ first_time │
├────────┼─────────┼─────────┼─────────┼───────────────┼──────────────┤
│ GOOG │ 202.00 │ 200.00 │ 211.00 │ 3 │ 09:00:00.145 │
├────────┴─────────┴─────────┴─────────┴───────────────┴──────────────┤
│ 1 rows (1 shown) 6 columns (6 shown) │
└─────────────────────────────────────────────────────────────────────┘
Installation
Package is available on PyPI:
pip install rayforce-py
This installation also provides a command-line interface to access the native Rayforce runtime:
~ $ rayforce
Launching Rayforce...
RayforceDB: 0.1 Dec 6 2025
Documentation: https://rayforcedb.com/
Github: https://github.com/RayforceDB/rayforce
↪ (+ 1 2)
3
Built with ❤️ for high-performance data processing | MIT Licensed | RayforceDB Team
2 maintainers
Metadata
2 maintainers
Release files for rayforce-py 2.3.0
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Built distributions (wheels)
Total release size: 33.8 MB
Release files / rayforce_py-2.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
| Download URL | rayforce_py-2.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl |
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| Size | 5.9 MB |
| Tags | CPython 3.14 Linux glibc 2.17+ x86-64 Linux glibc 2.28+ x86-64 |
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