Looking for testers: Python JSON-style serializer that's ~2× smaller and ~3–4× faster to encode in my benchmarks

I built HKD JSON, an HKD∞-inspired structural serializer for large collections of similarly structured Python records.

It's intended for things like API data, logs, telemetry, event streams and structured datasets where thousands or millions of records repeatedly contain the same schema.

Migration from Python's json API is deliberately small:

import hkd_json as json

with open("data.hkd", "wb") as f:
    json.dump(records, f)

with open("data.hkd", "rb") as f:
    restored = json.load(f)

assert restored == records

The default HKD representation is compact binary rather than textual JSON. There's also format="json" when normal JSON interoperability is required.

On my 15,000-record high-entropy benchmark suite:

Dataset     Size reduction    Encode speedup    Exact
API             2.02×             2.96×          Yes
Logs            2.09×             3.53×          Yes
Telemetry       2.13×             3.94×          Yes

The packaged 8 MiB benchmark measured:

JSON:       6,020,857 bytes
HKD JSON:   3,348,069 bytes

size reduction: 1.80×
encode speedup:  3.29×
exact=True
PASS=True

Every benchmark performs a real serialize → deserialize → equality test.

Free supports datasets through 8 MiB. Unlimited removes the HKD JSON file-size restriction.

Project / free version:

https://github.com/yangofzeal/hkd_json

The Unlimited purchase link is in the README.

I'm looking for independent testing, especially on large homogeneous record collections, logs, telemetry and API datasets. I'd be interested in both positive and negative results—particularly datasets where the structural approach stops helping.