Speedups
C and Cython extensions for fast STL I/O and PostgreSQL-to-NumPy conversion.
Performance
Cython extensions run ~3x faster than pure Python, consistent across data sizes.
| Operation | Facets | Pure Python | speedups | Speedup |
|---|---|---|---|---|
| Write | 10,000 | 28.5 ms | 9.9 ms | 2.9x |
| Read | 10,000 | 21.6 ms | 7.0 ms | 3.1x |
| Write | 100,000 | 283.4 ms | 89.3 ms | 3.2x |
| Read | 100,000 | 218.7 ms | 71.0 ms | 3.1x |
| Write | 1,000,000 | 2.81 s | 897.5 ms | 3.1x |
| Read | 1,000,000 | 2.19 s | 711.5 ms | 3.1x |
| Write | 10,000,000 | 28.62 s | 9.22 s | 3.1x |
| Read | 10,000,000 | 22.03 s | 7.32 s | 3.0x |
| Type | Elements | Pure Python | speedups | Speedup |
|---|---|---|---|---|
| int32 | 100K | 7.3 ms | 2.6 ms | 2.8x |
| int32 | 1M | 73.8 ms | 24.8 ms | 3.0x |
| int32 | 10M | 747.5 ms | 216.1 ms | 3.5x |
| int32 | 50M | 3.75 s | 1.05 s | 3.6x |
| float64 | 100K | 8.0 ms | 3.3 ms | 2.4x |
| float64 | 1M | 78.3 ms | 32.1 ms | 2.4x |
| float64 | 10M | 786.5 ms | 268.5 ms | 2.9x |
| float64 | 50M | 4.01 s | 1.31 s | 3.1x |
Benchmarked on Apple M2 Pro, Python 3.14, macOS 15.4. PG data pre-populated in tables to isolate COPY+conversion time. Array dimensionality (1D/2D/3D) has no significant effect on performance.
Install
pip install speedups
With PostgreSQL support:
pip install speedups[postgres]
PostgreSQL Array → NumPy
Convert PostgreSQL arrays directly to NumPy ndarrays using psycopg's
binary COPY protocol. Bypasses Python object creation for a significant
speedup over the default loader.
Supports float4, float8, smallint, integer, and bigint arrays,
from 1D to N-D.
import psycopg
from speedups.psycopg_loaders import NumpyLoader
with psycopg.connect('dbname=mydb') as conn:
cursor = conn.cursor(binary=True)
NumpyLoader.install(cursor)
query = """
COPY (
SELECT array_agg(x)
FROM generate_series(1, 100000) x
) TO STDOUT WITH BINARY
"""
with cursor.copy(query) as copy:
copy.set_types(['integer[]'])
for row in copy.rows():
print(row) # numpy.ndarray
ASCII STL I/O
Read and write ASCII STL files at C speed. This module is used internally
by numpy-stl — if you want to read
or write STL files, use numpy-stl for a full-featured API. The Cython
implementation uses direct sscanf/fprintf calls, avoiding Python string
overhead entirely.
from speedups.stl import ascii_read, ascii_write
# Read
with open('model.stl', 'rb') as f:
buf = f.read(8192)
name, mesh = ascii_read(f, buf)
# Write
with open('output.stl', 'wb') as f:
ascii_write(f, b'my_model', mesh)
Supported Types
| PostgreSQL | NumPy | Dimensions |
|---|---|---|
float4 |
float32 |
1D – ND |
float8 |
float64 |
1D – ND |
smallint |
int16 |
1D – ND |
integer |
int32 |
1D – ND |
bigint |
int64 |
1D – ND |
Compatibility
- Python 3.10, 3.11, 3.12, 3.13, 3.14
- NumPy 1.x and 2.x
License
BSD-3-Clause
2 maintainers
Metadata
2 maintainers
Release files for speedups 2.3.1
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| speedups-2.3.1.tar.gz | 365.3 kB | Details |
Built distributions (wheels)
Total release size: 26.1 MB
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