Skip to content

Use cases and adoption

BielSort has a deliberately narrow promise: make some large list[int] sorting workloads faster without requiring callers to migrate their data to a different container. The only reliable way to decide whether it helps is to measure the complete operation in the application that owns the data.

A promising fit

Your data already exists as a large Python list, contains exact integers in the signed 64-bit range, needs natural ascending order, and is sufficiently unsorted. Sorting must also account for a meaningful share of the pipeline runtime.

Usually not a fit

Prefer Python's built-in sort for small, nearly ordered, mixed-object, arbitrary-size-integer, key=, or reverse= workloads. If the pipeline already uses NumPy, Polars, Pandas, or a database, keeping the data in that system is normally more valuable than converting it only for BielSort.

Start with a decision checklist

Exact list[int]?

Native paths require exact Python integers; conversions change the comparison.

Signed 64-bit range?

Larger integers deliberately fall back to Timsort.

Large enough?

Native buffers are most relevant for tens or hundreds of thousands of values.

Natural ascending order?

key= and reverse= use the compatible Timsort fallback.

Measured bottleneck?

Accelerating a small fraction of total runtime has little application impact.

Memory headroom?

BielSort trades additional memory for its integer fast paths.

Transparent workload proxies

The repository includes three deterministic, synthetic proxies. They are not presented as production data; each isolates a plausible shape that a user can replace with an anonymized generator matching the real workload.

Event timestamps

Unordered second-resolution timestamps from one day, with duplicates. This is a dense range that may favor Counting Sort at scale.

Signed record IDs

Unordered signed 64-bit identifiers across a wide range exercise Radix Sort.

Mostly ordered offsets

Append-like offsets with a few displaced values should trigger Timsort and demonstrate compatibility rather than a native speedup.

Install the project with the optional benchmark dependency and run:

python -m pip install -e ".[benchmark]"
python benchmarks/workload_validation.py \
  -n 10000 100000 1000000 \
  -r 7 \
  --json bielsort-workload-report.json

The comparison includes equivalent new-list operations:

  • sorted(values);
  • bielsort.sort(values);
  • NumPy end to end: list[int] to ndarray, stable sort, then back to list[int].

Input generation and the expected result are outside the timed region. The algorithm order is interleaved deterministically, and the report records medians, environment metadata, strategies, whether a native fast path ran, winners, and speedups. If NumPy is not needed, pass --without-numpy.

Measure application impact, not only sorting

A large sorting speedup can still have little user-visible impact. If sorting is 10% of a pipeline and becomes four times faster, the complete pipeline is only about 1.08 times faster:

overall speedup = 1 / (0.90 + 0.10 / 4) = 1.08x

Record at least:

  1. total pipeline time before and after;
  2. isolated sort time using equivalent input and output types;
  3. peak memory and process memory limits;
  4. input length, value range, duplicate rate, and existing order;
  5. CPU, operating system, Python version, and repetition count.

Share a real result

Use the BielSort Workload Evaluator to compare APIs while the list remains on your machine. It writes JSON and Markdown without raw values or automatic uploads; review both before sharing.

Use the real-world use case form to report a win, a loss, or an incompatibility. Negative results are useful: they improve the selector, documentation, and understanding of the library's actual niche.

Do not upload proprietary datasets or confidential identifiers. Prefer a small anonymized generator that reproduces the input distribution.

Production adoption gate

Consider adoption only after all of these are true:

  • correctness matches the current implementation on representative inputs;
  • the end-to-end pipeline improvement is material to the application;
  • peak memory remains within a safe operating margin;
  • a compatible wheel exists for every deployment target;
  • Timsort fallback behavior is acceptable for unsupported inputs;
  • the application pins a tested BielSort version and has a rollback path.

This process may conclude that sorted(), NumPy, or a database remains the better choice. That is a successful validation result, not a failed benchmark.

For package and cross-platform evidence, continue with the hosted runner validation. It tests the published wheel independently but does not replace an application workload.