hario-core 0.3.0

Hario Core – Type-safe HAR Model & Transform

Hario Core — Type-safe HAR Model & Transform

A modern, extensible, and type-safe Python library for parsing, transforming, and analyzing HAR (HTTP Archive) files. Built on Pydantic, Hario-Core provides robust validation, flexible transformation, and easy extension for custom HAR formats.

Features

• Type-Safe Parsing: Validates HAR files using Pydantic models, catching errors early.
• Transformers: Apply built-in or custom transformations to each HAR entry (e.g., flattening, normalization).
• Normalization: Ensures all numeric fields (sizes, timings) are non-negative, so you can safely sum, aggregate, and analyze data without errors from negative values. This is crucial for analytics and reporting.
• Deterministic & Random IDs: Generate unique or deterministic IDs for each entry. Deterministic IDs ensure that the same request always gets the same ID—useful for deduplication, comparison, and building analytics pipelines.
• Extensible: Register your own entry models to support browser-specific or proprietary HAR extensions (e.g., Chrome DevTools, Safari).
• Composable Pipelines: Chain any number of transformers and ID strategies for flexible data processing.

Installation

pip install hario-core

Quickstart

from hario_core import parse, Pipeline, by_field, normalize_sizes, flatten

# Build a processing pipeline: deterministic ID, normalization, flattening
pipeline = Pipeline(
    id_fn=by_field(["request.url", "startedDateTime"]),
    transformers=[normalize_sizes(), flatten()],
)

# Parse your HAR file (from path, bytes, or file-like object)
model = parse("example.har")
result_dict = pipeline.process(model)

for entry in result_dict:
    print(entry["id"], entry["request"]["url"])

Documentation

• API Reference
• Changelog
• Contributing

License

MIT License. See LICENSE.

Supported Python Versions

• Python 3.10+