jetro 0.1.1

Query, shape, and patch JSON in one expression — at Rust speed.

jetro

Query, shape, and patch JSON in one expression — at Rust speed.

Python bindings for jetro, a JSON query, transform, and patch DSL. Filter, project, group, aggregate, write back to deeply nested data, and pattern-match, all in a single expression. Streaming with demand propagation, structural bitmap index for deep search, and a complete patch surface (.set / .modify / .delete).

Install

pip install jetro

Wheels are published for CPython 3.9+ on linux (x86_64, aarch64), macOS (universal2), and windows (x86_64). Building from source requires a Rust toolchain.

Quickstart

from jetro import Jetro, JetroEngine

j = Jetro.from_str('{"books":[{"title":"a","price":12},{"title":"b","price":7}]}')

j.collect("$.books.len()")                          # 2
j.collect("$.books.filter(price > 10).map(title)")  # ["a"]
j.collect("$.books.map(price).sum()")               # 19

JetroEngine amortises parsing and planning across many queries:

eng = JetroEngine()
for doc in stream_of_jsons:
    payload = eng.collect_bytes(doc, "$.users.filter(active).map(email)")
    ...

Pattern matching

j = Jetro.from_str('{"u": {"role": "admin", "id": 9}}')

j.collect("""
    match $.u with {
        {role: "admin", id: i} -> {tag: "a", n: i},
        {role: "user",  id: i} -> {tag: "u", n: i},
        _                      -> {tag: "x"}
    }
""")
# {"tag": "a", "n": 9}

Deep search:

j.collect("""
    $..match {
        {tag: "click", id: i} -> i,
        _                     -> false
    }
""")

Performance

jetro outperforms several comparable Python JSON DSL on the same queries, and matches or beats hand-rolled Python on compound workloads. Numbers below are median wall-clock per iteration on a 403 KB JSON document with 1000 users and ~5–50 orders each. Each library is queried in its own idiomatic single expression for the same semantic workload.

Warm path (cached parse + cached plan)

The document is parsed once; subsequent queries hit JetroEngine's plan cache. Mirrors a long-running process answering many queries against the same document.

Workload	jetro	jmespath	jsonpath-ng	pyjq	glom (py)
users.filter(active).map(email)	86 µs	537 µs	2 899 µs	18 829 µs	32 µs
users.filter(role=="admin").len()	37 µs	1 237 µs	1 682 µs	18 951 µs	29 µs
top-5 active users by score	43 µs	931 µs	2 165 µs	20 643 µs	84 µs
sum of order totals (active users)	129 µs	1 704 µs	11 051 µs	22 290 µs	177 µs
orders with total > 100	498 µs	7 798 µs	11 219 µs	24 624 µs	221 µs
group users by role, count	73 µs	n/a	n/a	20 610 µs	68 µs
users with any open order ≥ 50	2 601 µs	14 642 µs	816 µs*	28 032 µs	379 µs

The glom (py) column is hand-written Python list / generator expressions over an already-parsed dict, included as a "no DSL, maximum-speed Python" reference.

Cold path (parse + query per iteration)

Every iteration re-parses the raw bytes, then runs the query. Mirrors the typical web-server pattern of "receive JSON request, run query, return result" where parse cost dominates. jetro's simd-json bytes→tape parser is several times faster than json.loads, so the gap to several other library widens substantially.

Workload	jetro	jmespath	jsonpath-ng	pyjq	glom (py)
users.filter(active).map(email)	574 µs	3 828 µs	15 984 µs	23 526 µs	3 340 µs
top-5 active users by score	538 µs	4 214 µs	15 016 µs	25 131 µs	3 405 µs
group users by role, count	547 µs	n/a	n/a	25 037 µs	3 339 µs

In the cold path jetro is ~6× faster than hand-rolled Python and 7–46× faster than every other DSL because parse cost dominates and simd-json beats json.loads by a wide margin.

Reproduce with:

pip install -r benches/requirements.txt
python benches/compare.py

Errors

from jetro import JetroParseError, JetroEvalError

JetroParseError covers JSON parse failures; JetroEvalError covers runtime query errors. Both subclass JetroError.

API

Class	Method	Notes
Jetro	from_bytes(data) -> Jetro	accepts bytes / bytearray / memoryview
Jetro	from_str(text) -> Jetro	accepts str
Jetro	collect(expr) -> Any	evaluates against this document
JetroEngine	collect(doc, expr) -> Any	reuses cached plan
JetroEngine	collect_bytes(data, expr) -> Any	parses + queries in one call
JetroEngine	clear_cache()	drops cached plans

Language reference

See the upstream SYNTAX.md for the full language surface.

Building from source

maturin develop --release
pytest

License

MIT.