This release is a pre-release and may not be stable for production use.
pcollections
A persistent collections library for Python.
About
pcollections is a library of persistent (immutable) collections inspired by
the immutable data structures of Clojure but built to
resemble the native Python collections as closely as possible. It is
implemented in C, with a pure-Python fallback, and has no dependencies.
Documentation: https://nben.net/pcollections/
Use of Generative AI
Version 1.0 and later of pcollections was written in collaboration with
Claude Code, using models Sonnet 5 and Opus 5. All code prior to and including
commit 94dbf84
(version 0.4 and earlier) were written by humans. This includes most of the
entire original first implementation of the library and its API as well as most
of the header files and critical performance code for the version 1.0 rewrite.
All AI work was monitored by the author and independently tested prior to release.
Installation
pip install pcollections
pcollections supports CPython 3.8 through 3.15, including the
free-threaded builds of 3.13 and later. Support for Python 3.8 is
deprecated and will be removed in a future minor release (1.1 or 1.2, for
example).
Check that you have the C backend. pcollections has a C backend and a
much slower pure-Python backend, and an installation that can't load the C
backend (because pip built it from source without a working C compiler,
for example) still works, using the Python backend:
python -c "import pcollections; print(pcollections.using_c_extension)"
To make sure you get the C backend, install with
pip install --only-binary pcollections pcollections (which uses a prebuilt
wheel or fails), or set PCOLLECTIONS_REQUIRE_C=1 when installing and when
running (which makes a build without the C extension, and an import without
it, fail). See
Installation and backends.
Overview
This enables structural sharing, which avoids unnecessary copying of data, and by that, reduces memory consumption.
The library implements three persistent types: plist, pset, and
pdict. These are immutable versions of the builtin list, set, and dict
types. The persistent object interfaces are as similar as possible to the native
types, but the method signatures differ in ways necessary to accommodate
efficient immutable ways of doing things. For example, the pdict constructor
is identical to the dict constructor and always returns a pdict equal to the
dict that would be created with the same arguments. However, instead of
supporting operations like d[key] = val, pdicts support a set method: d = d.set(key, val).
In addition to the persistent types, there are two lazy types, llist and
ldict. These types are enabled by the lazy type. A lazy object is
basically a partial object that, when called, caches the function's return
value and returns that value without rerunning the function on subsequent
calls. The llist and ldict types are equivalent to the plist and pdict
types with one exception. Elements of an llist and values of an ldict that
are of the lazy type are dereferenced when requested. This allows a programmer
to easily create data structures (potentially nested data structures) whose
items are the results of complex or long-running computations that only get
computed once requested. The persistent data structures allow the arguments to
these lazy functions to be safe from mutation.
Lazy values
- A
lazyis an ordinary object in any collection other than the lazy collections:pdict(x=lazy(f))['x']is thelazyitself. - Every read from a lazy collection computes the lazy values it returns. That
includes indexing,
get,values(),items(), iteration,pop, comparison, hashing, pickling, and converting to another collection:pdict(ld)computes the values of theldictld.strandreprshow<lazy>without computing anything. - The lazy constructors (
ldict(x),llist(x),tldict(x),tllist(x)) keep lazy values uncomputed. To get the rawlazyobjects out of a lazy collection, usegetlazy,holdlazy(coll), or theheld_pdict,held_tdict,held_plist, andheld_tlistmethods. - A value is computed at most once, even when several threads request it at
the same time. If the computation raises an exception, the failure is
remembered: every request raises a new
LazyErrorwhose__cause__is the original exception, and whose message says where thelazywas created. Setlazy.trace = True(or the environment variablePCOLLECTIONS_LAZY_TRACE=1) to also record the full stack at creation.with lazy_error_unwrap:re-raises the original exception instead, even when the failure started in a lazy value that this one depends on. - A lazy value whose computation requests its own value raises
LazyError. Two threads that each compute a lazy value needed by the other deadlock; this can't happen when lazy values are built from immutable data, because a lazy value can then only depend on lazy values that existed before it. - Pickling a
lazycomputes it and pickles its value. lazycan be subclassed. A subclass may override__call__and callsuper().__call__(); lazy collections andunlazycall the override.
Finally, the persistent and lazy types have transient counterparts that enable
more efficient batch-mutation of the persistent types. The transient types
tlist, tset, tdict, tllist, and tldict all have interfaces equivalent
to their standard mutable counterparts (transient types are mutable).
Threads
The persistent types can be shared freely between threads, including on
free-threaded ("no-GIL") builds of Python, which pcollections supports.
The transient types are meant to be used by one thread at a time, like the
builtin list, set, and dict. They are not corrupted if that rule is
broken: a modification that overlaps another modification of the same transient
(from another thread, or from code such as a key's __eq__ that runs during
the modification) raises RuntimeError. So does a read (a lookup, or a step of
an iteration) that is made while a modification is in progress, or whose
transient changes during a key comparison. As with dict and set, changing
the keys of a tdict or tset while iterating over it raises RuntimeError;
iterating over a tlist while changing it behaves like iterating over a
list.
Behavior shared with the builtins
See Differences from the builtins for the full list.
- Lookups match objects by identity or equality, as
dict,set, andlistdo, sonan in pset([nan])is true whennanis the same object. hash(pset(x)) == hash(frozenset(x)), so apsetand an equalfrozensetare interchangeable as dictionary keys. Transient types are unhashable.pdictandtdictsupport|(andtdictsupports|=),fromkeys, andreversed.tlistsupports slice assignment and deletion.- Persistent types have
drop(key, error=False), which returns the collection unchanged when the key (or index) isn't present, or raisesKeyError(orIndexError) whenerroris true.delete(key)always raises. - All types can be weakly referenced and subscripted for type hints
(
pdict[str, int]).
Subclassing
All of the types can be subclassed, and methods that return a new collection
return an instance of the subclass. A persistent class names its transient
partner in the class attribute __transient_type__ (used by transient()),
and a transient class names its persistent partner in __persistent_type__
(used by persistent()):
class MyDict(pdict):
__slots__ = ()
class MyTDict(tdict):
__slots__ = ()
MyDict.__transient_type__ = MyTDict
MyTDict.__persistent_type__ = MyDict
Backends
pcollections is implemented in C, with a pure-Python fallback. If the C
extension can't be loaded, pcollections uses the (much slower) Python
backend and issues a RuntimeWarning; pcollections.using_c_extension says
which backend is in use, and pcollections.backend_error holds the exception
that prevented loading the C extension. Two environment variables control
this, both when pcollections is built and when it is imported:
PCOLLECTIONS_REQUIRE_C=1makes buildingpcollectionsfail if the C extension doesn't compile, and importing it fail if the C backend can't be loaded.PCOLLECTIONS_NO_C_EXTENSIONS=1buildspcollectionswithout the C extension, and selects the Python backend without a warning.
On Python 3.8, only the first interpreter in a process that imports
pcollections gets the C backend; other interpreters (subinterpreters, for
example) use the Python backend.
Objects pickled with one backend can be unpickled with the other.
License
MIT License
Copyright (c) 2022-2026 Noah C. Benson
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
1 maintainer
Metadata
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