fleche 0.8.2

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fleche

A persistent caching solution for arbitrary Python functions - like lru_cache on steroids.

flèche is French for 'arrow' and also used in fencing as a fast attack.

Features

• Persistent Caching: Cache function results across runs using various storage backends
• Flexible Storage: Choose from file-based, SQL, in-memory, or custom storage solutions
• Intelligent Hashing: Automatically generates cache keys from function arguments
• Query Support: Search and retrieve cached results with metadata filtering
• Configurable: Control what gets hashed (version, module, code, arguments)
• Multiple Backends: File (pickle, JSON), SQLAlchemy, Bagofholding, and more
• Thread-Safe: Safe for use in multi-threaded environments
• Type-Aware: Works seamlessly with NumPy, Pandas, and custom types

Installation

Basic Installation

pip install fleche

With Optional Dependencies

# For SQL storage
pip install fleche[sqlalchemy]

# For alternate serialization formats
pip install fleche[cloudpickle,dill]

# For Bagofholding storage
pip install fleche[bagofholding]

# For documentation
pip install fleche[docs]

# For development and testing
pip install fleche[tests]

Quick Start

Basic Usage

from fleche import fleche

@fleche()
def expensive_function(x, y):
    """This function's results will be cached."""
    print(f"Computing for {x}, {y}...")
    return x + y

# First call computes the result
result = expensive_function(1, 2)  # prints "Computing for 1, 2..."

# Second call retrieves from cache
result = expensive_function(1, 2)  # no print - result from cache!

# Different arguments compute again
result = expensive_function(2, 3)  # prints "Computing for 2, 3..."

Working with Cache Results

@fleche()
def compute(x):
    return x ** 2

# Get the cache key (digest) for arguments
digest = compute.digest(5)

# Check if result is cached
if compute.contains(5):
    result = compute.load(5)
else:
    result = compute(5)

# Get Call object with metadata
call = compute.call(5)

Configuration

Decorator Options

@fleche(
    version=1,           # Versioning for function changes
    hash_version=True,   # Include version in cache key
    hash_module=True,    # Include module name in cache key
    hash_code=False,     # Include function code in cache key
    require=None,        # Required argument for caching
    ignore=None,         # Arguments to ignore in cache key
    isolate=False,       # Isolate cache by working directory
)
def my_function(x):
    return x * 2

Storage Backends

In-Memory Storage (Default)

• Default when no fleche.toml configuration file is present
• Transient: data is lost when the process exits

File Storage

• Stores cache in filesystem using pickle (or cloudpickle/dill)
• Persistent across runs
• XDG Base Directory Specification compliant when configured

SQL Storage

• Requires sqlalchemy
• Stores metadata and results in database
• Currently supports SQLite
• Query capabilities for cached results

In-Memory Storage

• For testing or temporary caching
• Loses data when process exits

Custom Backends

Implement the Storage interface to create custom backends.

Advanced Features

Versioning

Track function versions to invalidate cache when implementation changes:

@fleche(version=2)
def process_data(data):
    # Incrementing version invalidates all v1 cache entries
    return data * 2

Ignoring Arguments

Skip certain arguments when generating cache keys:

@fleche(ignore=['verbose', 'debug'])
def compute(x, y, verbose=False, debug=False):
    return x + y  # Cache key only uses x and y

Querying Results

Find cached results matching specific criteria (only after issuing a corresponding call):

@fleche()
def fetch_data(user_id, date):
    return get_data(user_id, date)

# Issue a call to cache the result
fetch_data(user_id=123, date='2024-01-01')

# Get all cached results
all_results = list(fetch_data.query())

# Get results matching specific arguments (None acts as wildcard)
results = list(fetch_data.query(user_id=123))

# Inspect a result
for call in fetch_data.query(user_id=123):
    print(call.arguments, call.result)

Performance

Use the included benchmarks to evaluate performance:

python -m benchmarks.run_benchmarks

Testing

Run the test suite:

pip install "fleche[tests]"
pytest tests/

Contributing

Contributions are welcome! Please ensure tests pass and code follows the project style.

License

BSD-3-Clause License - see LICENSE file for details.

Resources

• Documentation: See docs/ directory
• Tests: See tests/ for usage examples
• Benchmarks: See benchmarks/ for performance metrics