jsonatapy 2.1.4

High-performance Python/Rust implementation of JSONata query and transformation language

jsonata-core + jsonatapy

High-performance JSONata implementation in Rust, with Python bindings.

Much of this project was built with human guidance using Claude Code. There was no performant JSONata implementation in Python, so the goal was to port JSONata to Rust (with a PyO3 wrapper for Python) and see how fast it could go. The answer: faster than V8 for most expression workloads, and ~40x faster than the next pure-Rust implementation.

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Two packages, one implementation

	jsonata-core	jsonatapy
Language	Rust	Python
Published on	crates.io	PyPI
Install	cargo add jsonata-core	pip install jsonatapy
Use when	You're writing Rust	You're writing Python

jsonatapy is a thin PyO3 wrapper around jsonata-core. Both live in this repo.

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Rust quick start

use jsonata_core::evaluator::Evaluator;
use jsonata_core::parser;
use jsonata_core::value::JValue;

let ast = parser::parse("orders[price > 100].product")?;
let data = JValue::from_json_str(r#"{"orders":[
    {"product":"Laptop","price":1200},
    {"product":"Mouse","price":25}
]}"#)?;

let result = Evaluator::new().evaluate(&ast, &data)?;

# Cargo.toml
[dependencies]
jsonata-core = "2.1.2"          # pure Rust, no Python dependency

# Optional: disable SIMD for constrained targets
jsonata-core = { version = "2.1.2", default-features = false }

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Python quick start

pip install jsonatapy

import jsonatapy

# One-off evaluation
result = jsonatapy.evaluate('"Hello, " & name', {"name": "World"})
print(result)  # "Hello, World"

# Compile once, evaluate many times (10–1000x faster for repeated use)
expr = jsonatapy.compile("$sum(orders.(quantity * price))")
result = expr.evaluate({
    "orders": [
        {"product": "Laptop", "quantity": 2, "price": 1200},
        {"product": "Mouse",  "quantity": 5, "price": 25},
    ]
})
print(result)  # 2450

# Pre-convert data once for maximum throughput
data = jsonatapy.JsonataData(large_dataset)
result = expr.evaluate_with_data(data)   # 6–15x faster than evaluate(dict)

Supports Python 3.10, 3.11, 3.12, 3.13 on Linux, macOS (Intel & ARM), and Windows.

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What is JSONata?

JSONata is a query and transformation language for JSON data:

• Query — person.name
• Filter — products[price > 50]
• Transform — items.{"name": title, "cost": price}
• Aggregate — $sum(orders.total)
• Conditionals — price > 100 ? "expensive" : "affordable"

See official JSONata docs for the full language reference.

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Performance

jsonata-core passes 1258/1258 JSONata reference tests and is the fastest JSONata implementation available in either Rust or Python.

Pure Rust (Criterion benchmarks, no Python overhead)

Category	jsonata-core	vs jsonata-rs
Simple path lookup	81 ns	~40x faster
Arithmetic expression	140 ns	~40x faster
Conditional	106 ns	~30x faster
String operations	126–284 ns	~30x faster
$sum (100 elements)	287 ns	~70x faster
Filter predicate (100 objects)	7.9 µs	~50x faster
Realistic workload (100 products)	9–44 µs	~40x faster

Run the benchmarks yourself:

cargo bench --no-default-features --features simd

Python path (jsonatapy)

jsonatapy is the fastest Python JSONata implementation by a large margin, and faster than the JavaScript reference implementation for most pure expression workloads:

Category	vs JavaScript (V8)	vs jsonata-python
Simple paths	2–14x faster	~20–40x faster
Conditionals	17x faster	~40x faster
String operations	4–9x faster	~30–45x faster
Complex transformations	3–17x faster	~20–40x faster
Higher-order functions	~1x (roughly equal)	~50–70x faster
Array-heavy workloads	varies	~10–50x faster

The Python boundary

For large array workloads, the dominant cost is converting Python dicts to Rust values on each evaluate() call — not expression evaluation itself. Two API paths avoid this:

# Path 1: Pre-convert data once, reuse across many queries (6–15x faster)
data = jsonatapy.JsonataData(large_dataset)
result = expr.evaluate_with_data(data)

# Path 2: Data arrives as a raw JSON string — pass it directly
result_str = expr.evaluate_json(raw_json_string)

With pre-converted data, array-heavy workloads run within 2–7x of V8 — the irreducible gap between a Rust interpreter and V8's JIT compiler.

See Performance docs for full benchmark results and methodology.

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Features

• Full JSONata 2.1.0 compatibility — 1258/1258 reference tests passing
• Pure Rust core — no JavaScript runtime, no Node.js dependency
• Optional Python bindings — PyO3/maturin, zero-copy where possible
• Cross-platform — Linux, macOS (Intel & ARM), Windows; Python 3.10–3.13
• SIMD-accelerated JSON parsing — via simd-json (optional feature)

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Documentation

• Installation
• API Reference
• Usage Guide
• Performance
• Optimization Tips
• Building from Source

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Building from source

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Clone
git clone https://github.com/txjmb/jsonata-core.git
cd jsonata-core

# Build and install Python extension
pip install maturin
maturin develop --release

# Run Python tests
pytest tests/python/ -v

# Run Rust benchmarks (no Python required)
cargo bench --no-default-features --features simd

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License

MIT — see LICENSE.

This project implements the JSONata specification. jsonata-js (the reference implementation) is also MIT licensed.