marisa-trie
Static memory-efficient and fast Trie-like structures for Python.
Decision gist · record as of 2026-08-14
Yes, if you have a static string dataset and memory efficiency is a priority. The package is actively maintained, supports modern Python versions (3.9–3.14), has no known vulnerabilities, and offers prebuilt wheels for most platforms. The copyleft license (LGPL/BSD 2-clause bundled library) requires compliance but is not a blocker for most use cases. Install friction is moderate due to C++ compilation, but wheels mitigate this for common platforms.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; compiled C++ extension requires a compatible C++ compiler at build time if wheels are not available for your platform.
- Medium install friction due to compiled C++ extensions, but prebuilt wheels are available for Python 3.9–3.14 across macOS, Linux (including aarch64), Windows, and musllinux platforms.
- Actively maintained with recent releases.
License · maintenance · safety
MIT AND (BSD-2-Clause OR LGPL-2.1-or-later) (copyleft) — Dual licensing: wrapper code is MIT; bundled C++ library is dual-licensed under LGPL or BSD 2-clause. Users must comply with LGPL or BSD 2-clause terms for the underlying library.
last release 2026-04-08 (128 days) · last repo commit 2026-07-20 · 1,123 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,718,771 downloads/mo, #2,245 on PyPI
Alternatives
Verify before relying
pip install marisa-trie
from marisa_trie import Trie
trie = Trie(['apple', 'application', 'apply'])
if 'apple' in trie:
print(trie.key_id('apple'))- Whether the 50x-100x memory reduction claim applies to all data sizes or primarily to large datasets.
- Performance characteristics of prefix search and other advanced methods compared to alternative trie implementations.
- Practical use-case suitability for dynamic (mutable) datasets, given the static/immutable design.
What it is and what it does
marisa-trie wraps the MARISA C++ trie library to provide immutable, memory-efficient trie data structures for Python. It trades mutability for dramatic memory savings and fast lookups on static string collections. The package includes Trie (for string-to-integer ID mapping), BytesTrie (for arbitrary byte sequences), RecordTrie (for string-to-value mappings), and StringTrie (for string-to-string mappings). All structures support prefix search and iteration over keys and items.
The primary use case is storing and querying large static string datasets—such as dictionaries, word lists, or IP address ranges—where memory footprint is a concern. Once built, a trie is serialized to disk and can be memory-mapped for fast access without reloading into RAM. The trade-off is that the trie cannot be modified after creation; new data requires rebuilding.
Use it for
- Store a large dictionary or word list for spell-checking or autocomplete with minimal memory overhead.
- Index IP addresses or network prefixes for fast lookup and prefix-based filtering.
- Build a static lexicon for natural language processing tasks requiring efficient string-to-ID or string-to-value mapping.
- Implement a fast prefix-search engine over a fixed set of strings (e.g., domain names, product SKUs).
- Serialize and memory-map a trie to disk for fast startup and low-memory footprint in long-running services.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have a static string dataset and memory efficiency is a priority.
The package is actively maintained, supports modern Python versions (3.9–3.14), has no known vulnerabilities, and offers prebuilt wheels for most platforms. The copyleft license (LGPL/BSD 2-clause bundled library) requires compliance but is not a blocker for most use cases. Install friction is moderate due to C++ compilation, but wheels mitigate this for common platforms.
Install
marisa-trie on PyPI
Before you install
Medium install friction due to compiled C++ extensions, but prebuilt wheels are available for Python 3.9–3.14 across macOS, Linux (including aarch64), Windows, and musllinux platforms. Actively maintained with recent releases.
Requires Python 3.9 or later; compiled C++ extension requires a compatible C++ compiler at build time if wheels are not available for your platform.
License in practice
Dual licensing: wrapper code is MIT; bundled C++ library is dual-licensed under LGPL or BSD 2-clause. Users must comply with LGPL or BSD 2-clause terms for the underlying library.
Quickstart
pip install marisa-trie
from marisa_trie import Trie
trie = Trie(['apple', 'application', 'apply'])
if 'apple' in trie:
print(trie.key_id('apple'))Verify before relying
- Whether the 50x-100x memory reduction claim applies to all data sizes or primarily to large datasets.
- Performance characteristics of prefix search and other advanced methods compared to alternative trie implementations.
- Practical use-case suitability for dynamic (mutable) datasets, given the static/immutable design.
Package facts
| License | MIT AND (BSD-2-Clause OR LGPL-2.1-or-later) copyleft |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 128 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,718,771 / month, #2,245 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchProgramming Language :: CythonProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Programming Language :: Python :: Free Threading :: 2 - BetaProgramming Language :: Python :: Implementation :: CPythonTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development :: Libraries :: Python ModulesTopic :: Text Processing :: Linguistic |
Evidence: marisa_trie-1.4.1-cp310-cp310-macosx_10_9_x86_64.whl; marisa_trie-1.4.1-cp310-cp310-macosx_11_0_arm64.whl; marisa_trie-1.4.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; marisa_trie-1.4.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; marisa_trie-1.4.1-cp310-cp310-musllinux_1_2_aarch64.whl; marisa_trie-1.4.1-cp310-cp310-musllinux_1_2_x86_64.whl; marisa_trie-1.4.1-cp310-cp310-win32.whl; marisa_trie-1.4.1-cp310-cp310-win_amd64.whl; marisa_trie-1.4.1-cp310-cp310-win_arm64.whl; marisa_trie-1.4.1-cp311-cp311-macosx_10_9_x86_64.whl; marisa_trie-1.4.1-cp311-cp311-macosx_11_0_arm64.whl; marisa_trie-1.4.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; marisa_trie-1.4.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; marisa_trie-1.4.1-cp311-cp311-musllinux_1_2_aarch64.whl; marisa_trie-1.4.1-cp311-cp311-musllinux_1_2_x86_64.whl; marisa_trie-1.4.1-cp311-cp311-win32.whl; marisa_trie-1.4.1-cp311-cp311-win_amd64.whl; marisa_trie-1.4.1-cp311-cp311-win_arm64.whl; marisa_trie-1.4.1-cp312-cp312-macosx_10_13_x86_64.whl; marisa_trie-1.4.1-cp312-cp312-macosx_11_0_arm64.whl
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