Metadata-Version: 2.3
Name: checkpointer
Version: 2.0.2
Summary: A Python library for memoizing function results with support for multiple storage backends, async runtimes, and automatic cache invalidation
Project-URL: Repository, https://github.com/Reddan/checkpointer.git
Author: Hampus Hallman
License: Copyright 2024 Hampus Hallman
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Requires-Python: >=3.12
Requires-Dist: relib
Description-Content-Type: text/markdown

# checkpointer &middot; [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/Reddan/checkpointer/blob/master/LICENSE) [![pypi](https://img.shields.io/pypi/v/checkpointer)](https://pypi.org/project/checkpointer/) [![Python 3.12](https://img.shields.io/badge/python-3.12-blue)](https://pypi.org/project/checkpointer/)

`checkpointer` is a Python library for memoizing function results. It provides a decorator-based API with support for multiple storage backends. Use it for computationally expensive operations where caching can save time, or during development to avoid waiting for redundant computations.

Adding or removing `@checkpoint` doesn't change how your code works, and it can be applied to any function, including ones you've already written, without altering their behavior or introducing side effects. The original function remains unchanged and can still be called directly when needed.

### Key Features:
- 🗂️ **Multiple Storage Backends**: Built-in support for in-memory and pickle-based storage, or create your own.
- 🎯 **Simple Decorator API**: Apply `@checkpoint` to functions without boilerplate.
- 🔄 **Async and Sync Compatibility**: Works with synchronous functions and any Python async runtime (e.g., `asyncio`, `Trio`, `Curio`).
- ⏲️ **Custom Expiration Logic**: Automatically invalidate old checkpoints.
- 📂 **Flexible Path Configuration**: Control where checkpoints are stored.

---

## Installation

```bash
pip install checkpointer
```

---

## Quick Start 🚀

```python
from checkpointer import checkpoint

@checkpoint
def expensive_function(x: int) -> int:
    print("Computing...")
    return x ** 2

result = expensive_function(4)  # Computes and stores the result
result = expensive_function(4)  # Loads from the cache
```

---

## How It Works

When you use `@checkpoint`, the function's **arguments** (`args`, `kwargs`) are hashed to create a unique identifier for each call. This identifier is used to store and retrieve cached results. If the same arguments are passed again, `checkpointer` loads the cached result instead of recomputing.

Additionally, `checkpointer` ensures that caches are invalidated when a function's implementation or any of its dependencies change. Each function is assigned a hash based on:
1. **Its source code**: Changes to the function's code update its hash.
2. **Dependent functions**: If a function calls others, changes in those dependencies will also update the hash.

### Example: Cache Invalidation

```python
def multiply(a, b):
    return a * b

@checkpoint
def helper(x):
    return multiply(x + 1, 2)

@checkpoint
def compute(a, b):
    return helper(a) + helper(b)
```

If you modify `multiply`, caches for both `helper` and `compute` are invalidated and recomputed.

---

## Parameterization

### Custom Configuration

Set up a `Checkpointer` instance with custom settings, and extend it by calling itself with overrides:

```python
from checkpointer import checkpoint

IS_DEVELOPMENT = True  # Toggle based on your environment

tmp_checkpoint = checkpoint(root_path="/tmp/checkpoints")
dev_checkpoint = tmp_checkpoint(when=IS_DEVELOPMENT)  # Adds development-specific behavior
```

### Per-Function Customization & Layered Caching

Layer caches by stacking checkpoints:

```python
@checkpoint(format="memory")  # Always use memory storage
@dev_checkpoint  # Adds caching during development
def some_expensive_function():
    print("Performing a time-consuming operation...")
    return sum(i * i for i in range(10**6))
```

- **In development**: Both `dev_checkpoint` and `memory` caches are active.
- **In production**: Only the `memory` cache is active.

---

## Usage

### Force Recalculation
Force a recalculation and overwrite the stored checkpoint:

```python
result = expensive_function.rerun(4)
```

### Call the Original Function
Use `fn` to directly call the original, undecorated function:

```python
result = expensive_function.fn(4)
```

This is especially useful **inside recursive functions** to avoid redundant caching of intermediate steps while still caching the final result.

### Retrieve Stored Checkpoints
Access cached results without recalculating:

```python
stored_result = expensive_function.get(4)
```

---

## Storage Backends

`checkpointer` works with both built-in and custom storage backends, so you can use what's provided or roll your own as needed.

### Built-In Backends

1. **PickleStorage**: Stores checkpoints on disk using Python's `pickle`.
2. **MemoryStorage**: Keeps checkpoints in memory for non-persistent, fast caching.

You can specify a storage backend using either its name (`"pickle"` or `"memory"`) or its corresponding class (`PickleStorage` or `MemoryStorage`) in the `format` parameter:

```python
from checkpointer import checkpoint, PickleStorage, MemoryStorage

@checkpoint(format="pickle")  # Equivalent to format=PickleStorage
def disk_cached(x: int) -> int:
    return x ** 2

@checkpoint(format="memory")  # Equivalent to format=MemoryStorage
def memory_cached(x: int) -> int:
    return x * 10
```

### Custom Storage Backends

Create a custom storage backend by inheriting from the `Storage` class and implementing its methods. Access configuration options through the `self.checkpointer` attribute, an instance of `Checkpointer`.

#### Example: Custom Storage Backend

```python
from checkpointer import checkpoint, Storage
from datetime import datetime

class CustomStorage(Storage):
    def exists(self, path) -> bool: ...  # Check if a checkpoint exists at the given path
    def checkpoint_date(self, path) -> datetime: ...  # Return the date the checkpoint was created
    def store(self, path, data): ...  # Save the checkpoint data
    def load(self, path): ...  # Return the checkpoint data
    def delete(self, path): ...  # Delete the checkpoint

@checkpoint(format=CustomStorage)
def custom_cached(x: int):
    return x ** 2
```

Using a custom backend lets you tailor storage to your application, whether it involves databases, cloud storage, or custom file formats.

---

## Configuration Options ⚙️

| Option         | Type                                | Default     | Description                                 |
|----------------|-------------------------------------|-------------|---------------------------------------------|
| `format`       | `"pickle"`, `"memory"`, `Storage`   | `"pickle"`  | Storage backend format.                     |
| `root_path`    | `Path`, `str`, or `None`            | User Cache  | Root directory for storing checkpoints.     |
| `when`         | `bool`                              | `True`      | Enable or disable checkpointing.            |
| `verbosity`    | `0` or `1`                          | `1`         | Logging verbosity.                          |
| `path`         | `Callable[..., str]`                | `None`      | Custom path for checkpoint storage.         |
| `should_expire`| `Callable[[datetime], bool]`        | `None`      | Custom expiration logic.                    |

---

## Full Example 🛠️

```python
import asyncio
from checkpointer import checkpoint

@checkpoint
def compute_square(n: int) -> int:
    print(f"Computing {n}^2...")
    return n ** 2

@checkpoint(format="memory")
async def async_compute_sum(a: int, b: int) -> int:
    await asyncio.sleep(1)
    return a + b

async def main():
    result1 = compute_square(5)
    print(result1)

    result2 = await async_compute_sum(3, 7)
    print(result2)

    result3 = async_compute_sum.get(3, 7)
    print(result3)

asyncio.run(main())
```
