Introduction

resnap is a lightweight Python library that lets you cache and recover the output of function calls based on their input arguments — without changing their logic.

It works by decorating a function with @resnap or @async_resnap. When the function is called for the first time with a specific set of arguments, the result is saved to disk (or another backend). On subsequent calls with the same inputs, resnap will reuse the saved result instead of recomputing it — making your workflows faster and more deterministic.

Why use resnap?

🔁 Replay cached results for identical inputs
💾 Multiple serialization formats: pickle, json, csv, parquet, and txt
🧱 Customizable backends: Local or remote storage via pluggable services (e.g., AWS S3)
⚙️ Minimal code changes: Add a single decorator to any function
🧪 Helpful in testing and iterative development to skip slow steps
📊 Metadata tracking: Records call time, argument hashes, errors, and return types

📦 Installation

To test or use in local mode

pip install resnap

If you want to use a S3 solution

pip install resnap[boto]

⏱️ Quick Example

from resnap import resnap


@resnap
def expensive_op(x, y):
    print("Running expensive computation...")
    return x * y + 42


# First call: function runs and result is saved
expensive_op(10, 5)

# Second call with same arguments: result is loaded from cache
expensive_op(10, 5)

🛠️ How it works

@resnap wraps your function with logic that:

Computes a unique hash of the input arguments (and optionally instance attributes)
Checks if a previous result exists for that hash
- If yes, returns the saved result
- Otherwise, runs the function, saves the result, and returns it
Metadata (function name, type of result, call timestamp, success/failure status) is stored separately from the result.

📓 Configuration Options

You can customize how resnap behaves using keyword arguments on the decorator:

Option	Type	Default	Description
output_format	str	backend default	Format used to save result (json, pickle, csv, parquet, txt)
output_folder	str	backend default	Subfolder where to save result files
enable_recovery	bool	True	Whether to try reusing existing results
consider_args	bool	True	Whether to include input arguments in hash
considered_attributes	list[str]	[]	Attributes of class instances to include in the hash

🧩 Use Cases

Caching slow or deterministic computations
Skipping costly steps in data pipelines or experiments
Replaying function results in tests or reproducible builds
Persisting results between sessions or across environments

Want more? Check out the Usage Guide or dive into the API Reference.