Radixor/python/benchmarks

Runtime stemming benchmarks

These scripts measure runtime stemming throughput only — model construction and dictionary compilation happen once during setup and are excluded from every timing. Anyone can reproduce the numbers on their own machine.

What is measured, and why batch sizes

Each engine is driven through its batch entry point over a fixed word budget (default 5 000 tokens), split into batches of 10, 20, 50, 100 words. The harness fits the descriptive line per_call(N) ≈ intercept + N · slope across those sizes. The fit is unconstrained and timing noise can make its intercept negative, so it describes observed scaling rather than physically separating overhead from word work. We report the best (minimum) of many repeats — the microbenchmark convention that suppresses OS/GC noise.

Data — identical to the Java JMH benchmarks

The workload is the changed-token corpus built from the repository's canonical dictionaries (models/<model>/src/modelInput/stemmer.gz), mirroring LanguageBenchmarkCorpus in the Java project: each dictionary field is paired with its line's root, normalized trim().lower(), and only tokens that differ from their root are kept, in dictionary order, padded to ≥ 5 000 tokens. See corpus.py.

Fairness — three things that quietly break stemmer comparisons

Getting a fair comparison turned out to matter more than any micro-optimization. Three asymmetries, if left in, make the numbers meaningless:

1. Result caching. PyStemmer caches results by default (maxCacheSize=10000). Because a benchmark stems the same corpus every repeat, that cache turns measured passes into dict lookups rather than stemming. The harness explicitly disables both caches: PyStemmer uses maxCacheSize=0 and radixor uses cache_size=0, so both engines do real stemming. snowballstemmer-pure, nltk-porter, and cistem have no cache.

2. Lowercasing. Snowball/PyStemmer do no case handling — they assume the caller pre-lowercased the input (our corpus is pre-lowercased for everyone). radixor normally lowercases internally; for a same-work comparison the harness runs radixor with lowercase=False (assume-already-lowercased), so Snowball and radixor do identical normalization work on identical input. CISTEM always performs its own lowercasing and German umlaut normalization; that unavoidable extra work modestly biases its comparison in radixor's favour.

3. Delegation. snowballstemmer delegates to PyStemmer when it is installed (they become the same C code). The harness bypasses that and uses snowballstemmer's genuine pure-Python backend, and records each engine's backing module + whether it is a compiled extension (--json) as proof.

Engines compared

Engine	Implementation	Batch API
radixor	Rust patch-command trie (cache disabled)	stem_batch — one FFI call per batch
PyStemmer	Snowball C libstemmer (cache disabled)	stemWords(list) — one C call per batch
snowballstemmer-pure	Official pure-Python Snowball	stemWords(list) — Python loop
nltk-porter	Porter (English), pure Python	scalar loop
cistem	CISTEM (German), pure Python (nltk)	scalar loop

Reproduce

cd python/
maturin develop --release             # build the radixor extension into your env
cd ..
./gradlew pythonBuildStandardModels    # generate and package standard models
pip install --no-deps build/python/dist/standard/radixor_models_standard-0.0.0-py3-none-any.whl
cd python/
pip install -r benchmarks/requirements-bench.txt
python benchmarks/run_benchmark.py --language en de fr ru fi \
    --sizes 10 20 50 100 --repeats 21 \
    --json benchmarks/results.json --csv benchmarks/results.csv

From the repository root, the Gradle integration builds an isolated host wheel and benchmarks Radixor plus every available comparison engine over all supported languages with the fixed 10/20/50/100 size sweep:

./gradlew pythonBenchmarkAllLanguagesBatch

The generated CSV and JSON reports are placed in build/reports/python-benchmarks/.

Comparison engines are auto-detected in the environment selected by the Gradle pythonExecutable property. Install requirements-bench.txt in that environment to enable the complete comparison set.

The run prints machine/Python/engine versions and each engine's backing module, and writes per-point rows (CSV) plus the full report incl. environment and provenance (JSON), so results are self-describing and verifiable.

Published results

The canonical, current single-machine results and complete environment metadata are published on the documentation site's Python performance page. Keeping the measured table in one place prevents results from different CPUs or benchmark runs from being mixed.

Interpretation

• In the published 2026-08-08 run, radixor is the fastest stemmer measured in Python in all 18 languages directly shared with PyStemmer.
• The benchmark intentionally disables caches. Cached-operation performance is outside this suite and must not be inferred from its results.
• radixor and Snowball remain different classes of stemmer: radixor is dictionary-based (UniMorph gold coverage), Snowball is rule-based. radixor gives dictionary-quality stems and the best measured throughput.