Speedups

CI Build Wheels PyPI Python License

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.

STL I/O Performance

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

PostgreSQL COPY to NumPy Performance

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

Project links

Data verified by PyPI on Jul 19, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (2.3.3)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for jhammudoglu from gravatar.com jhammudoglu Avatar for wolph from gravatar.com wolph

Credits

Author: Rick van Hattem

GitHub Statistics

Data verified by PyPI on Jul 19, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.10

Provides Extra

postgres test lint typecheck dev benchmark

Tags

Cython C C++ speedups Numpy PostgreSQL PsycoPG

Classifiers

Report project as malware

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  • Tags: CPython 3.10, Windows x86-64
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  • Uploaded via: twine/6.1.0 CPython/3.13.14

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