spork-pds

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spork-pds provides fast, immutable persistent data structures for CPython. They use familiar collection operators, but return new values while sharing unchanged structure with the original. Snapshots stay inexpensive and previous values remain unchanged.

The package is the standalone home of the persistent data structures originally developed for Spork. It has no dependency on the Spork language or runtime.

Features

  • Vector: bit-partitioned persistent vector with indexing, slicing, + concatenation, and * repetition
  • Map: persistent hash map backed by a HAMT with dict-style | merging
  • Set: persistent hash set with |, &, -, and ^ operations
  • SortedVector: ordered persistent collection backed by a size-annotated red-black tree
  • Cons: immutable linked-list cells
  • DoubleVector and IntVector: specialized float64 and int64 vectors with the read-only buffer protocol
  • Transient variants for efficient batches of controlled mutation
  • Structural ABC integration, hashing, iteration, generic aliases, and pickle support
  • CPython 3.10+ support, including native free-threaded execution on CPython 3.14t

Installation

python -m pip install spork-pds

A C compiler and Python development headers are required when installing from a source distribution. Published releases are intended to provide wheels for supported Python versions and platforms.

Quick start

from spork.pds import Map, Set, Vector, sorted_vec

numbers = Vector([1, 2, 3])
extended = numbers + [4, 5]
repeated = numbers * 2

assert list(numbers) == [1, 2, 3]
assert list(extended) == [1, 2, 3, 4, 5]
assert list(repeated) == [1, 2, 3, 1, 2, 3]

config = Map({"host": "localhost", "port": 8000})
production = config | {"host": "example.com"}

assert config["host"] == "localhost"
assert production["host"] == "example.com"

roles = Set(["reader", "writer"])
admin_roles = roles | {"admin"}

assert "admin" not in roles
assert "admin" in admin_roles

ordered = sorted_vec([5, 1, 3, 2, 4])
assert list(ordered) == [1, 2, 3, 4, 5]

Native operators, persistent values

Operators always produce persistent spork.pds collections and leave their operands unchanged:

updated_map = config | {"port": 443}
combined_set = roles | {"admin", "auditor"}
reduced_set = combined_set - {"reader"}
longer_vector = numbers + range(4, 7)

Augmented assignment follows Python's normal immutable-value behavior. It rebinds the name rather than mutating the collection:

original = Map({"users": 100})
updated = original
updated |= {"users": 101}

assert original["users"] == 100
assert updated["users"] == 101

The named persistent operations—such as .assoc(), .conj(), and .disj()—remain available when an individual update is clearer.

Batch updates with transients

Persistent updates are ideal when each intermediate version matters. For a batch where only the final value matters, use a transient:

from spork.pds import EMPTY_VECTOR

builder = EMPTY_VECTOR.transient()
for value in range(100_000):
    builder.conj_mut(value)

values = builder.persistent()
assert values[-1] == 99_999

Calling persistent() invalidates the transient. Further edits and element access raise RuntimeError; discard the transient immediately after conversion.

Native free-threading

On free-threaded CPython 3.14t, importing spork-pds leaves the GIL disabled. Persistent values are immutable and may be shared between threads, and different transient builders may run in parallel.

A transient is confined to the Python thread that created it. Cross-thread access to the same transient raises RuntimeError; convert it to a persistent value before sharing it. Stored Python objects remain responsible for their own thread safety, just as they are when stored in a tuple or dictionary. Isolated and per-interpreter-GIL subinterpreters remain unsupported. See Native Free-Threading Support for stress, sanitizer, performance, and wheel validation details.

Typed vectors and NumPy

from spork.pds import vec_f64, vec_i64

floats = vec_f64(1.0, 2.0, 3.0)
integers = vec_i64(1, 2, 3)

# The exported buffers are read-only.
assert memoryview(floats).format == "d"
assert memoryview(integers).format == "q"

# NumPy can view the vectors through the buffer protocol.
import numpy as np
array = np.asarray(floats)

The first buffer request materializes and caches contiguous storage; subsequent views reuse that immutable cache.

Documentation

Development

Clone the repository and set up the development environment:

git clone https://github.com/spork-it/spork-pds.git
cd spork-pds

make venv
make test
make fuzz

Useful targets:

make build
make build-debug
make stress-free-threading STRESS_ARGS="--require-no-gil"
make benchmark BENCH_ARGS="--size 100000 --iter 50"
make benchmark-free-threading FT_BENCH_ARGS="--size 4096 --repeats 9"
make dist
make check-dist

See make help for the complete target list.

License

MIT. See LICENSE.

Project links

Data verified by PyPI on Aug 30, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for grant-wade from gravatar.com grant-wade

Credits

Author: Grant Wade

GitHub Statistics

Data verified by PyPI on Aug 30, 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

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

test dev

Tags

persistent-data-structures immutable functional-programming vector hamt structural-sharing

Classifiers

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0.1.4 This release

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0.1.3

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0.1.2

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0.1.1

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0.1.0

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