Quick Start

Evaluate rules instantly in Python using the datalogic-py binding.

Simple One-Shot Evaluation

Use apply for simple, one-off evaluations:

from datalogic_py import apply

# Arithmetic
result = apply({"+": [1, 2, 3]}, {})
print(result) # 6

# Variable Access
result = apply(
    {"var": "user.age"},
    {"user": {"age": 25}}
)
print(result) # 25

Reusable Compiled Rules

For production loops, compile the rule once. This eliminates parsing overhead and parses the rule directly into the optimized Rust bytecode:

from datalogic_py import Engine

engine = Engine()

# 1. Compile once
rule = engine.compile({"if": [{">": [{"var": "score"}, 50]}, "pass", "fail"]})

# 2. Evaluate many times
for user in [{"score": 75}, {"score": 30}, {"score": 90}]:
    print(rule.evaluate(user)) # prints "pass", "fail", "pass"

Parsing Performance: evaluate vs evaluate_str

• rule.evaluate(dict_data) accepts a Python dict or list and converts it directly into Rust types using pythonize. This is 3–10× faster than a standard JSON-string round-trip.
• rule.evaluate_str(json_string) accepts a raw JSON string. If you already have a serialized JSON payload (e.g. read from a network socket or file), use this method to bypass Python-to-Rust dictionary marshaling completely.

Next: Configuration & Errors covers engine configuration presets, the exception hierarchy, and type conversion.