cacherator 1.2.10

Persistent JSON caching for Python with async support - cache function results and object state effortlessly.

Cacherator

Persistent JSON caching for Python with async support - Cache function results and object state effortlessly.

Overview

Cacherator is a Python library that provides persistent JSON-based caching for class state and function results. It enables developers to cache expensive operations with minimal configuration, supporting both synchronous and asynchronous functions.

Key Features

• Zero-configuration caching - Simple inheritance and decorator pattern
• Async/await support - Native support for asynchronous functions
• Persistent storage - Cache survives program restarts
• TTL (Time-To-Live) - Automatic cache expiration
• Selective caching - Fine-grained control over what gets cached
• Cache management - Built-in methods for inspection and clearing
• Cache status tracking - Per-call hit/miss detection
• Flexible logging - Global and per-instance control
• DynamoDB backend - Optional cloud cache for cross-machine sharing

Installation

pip install cacherator

Optional: DynamoDB Support

For cross-machine cache sharing via DynamoDB:

pip install boto3

Quick Start

Basic Function Caching

from cacherator import JSONCache, Cached
import time

class Calculator(JSONCache):
    def __init__(self):
        super().__init__(data_id="calc")
    
    @Cached()
    def expensive_calculation(self, x, y):
        time.sleep(2)  # Simulate expensive operation
        return x ** y

calc = Calculator()
result = calc.expensive_calculation(2, 10)  # Takes 2 seconds
result = calc.expensive_calculation(2, 10)  # Instant!

Async Function Caching

class APIClient(JSONCache):
    @Cached(ttl=1)  # Cache for 1 day
    async def fetch_user(self, user_id):
        # Expensive API call
        response = await api.get(f"/users/{user_id}")
        return response.json()

client = APIClient()
user = await client.fetch_user(123)  # API call
user = await client.fetch_user(123)  # Cached!

State Persistence

class GameState(JSONCache):
    def __init__(self, game_id):
        super().__init__(data_id=f"game_{game_id}")
        if not hasattr(self, "score"):
            self.score = 0
            self.level = 1
    
    def add_points(self, points):
        self.score += points
        self.json_cache_save()

# Session 1
game = GameState("player1")
game.add_points(100)

# Session 2 (after restart)
game = GameState("player1")
print(game.score)  # 100 - persisted!

Advanced Usage

DynamoDB Backend (Cross-Machine Cache Sharing)

Pass dynamodb_table to use DynamoDB as the cache backend instead of local JSON:

from cacherator import JSONCache, Cached

class WebScraper(JSONCache):
    def __init__(self):
        super().__init__(dynamodb_table='my-cache-table')
    
    @Cached(ttl=7)
    def scrape_expensive_data(self, url):
        return fetch_data(url)

# On machine 1 (laptop)
scraper = WebScraper()
data = scraper.scrape_expensive_data("https://example.com")  # Scrapes and caches

# On machine 2 (EC2 instance) - same code
scraper = WebScraper()
data = scraper.scrape_expensive_data("https://example.com")  # Uses cached data!

How it works:

• No table specified: saves/loads from local JSON file
• Table specified: saves/loads from DynamoDB only — no local file used
• Compression: Payloads over 100KB are automatically gzip-compressed before writing to DynamoDB, reducing typical HTML payloads by 80-90%. A warning is logged if the compressed payload still exceeds DynamoDB's 400KB item limit.

DynamoDB table:

• Auto-created if missing (requires IAM permissions)
• Partition key: cache_id (String)
• TTL enabled for automatic expiry
• Pay-per-request billing mode

AWS credentials via standard boto3 chain:

• Environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION
• IAM role (recommended for EC2/Lambda)
• AWS credentials file (~/.aws/credentials)

Custom TTL Configuration

class WeatherService(JSONCache):
    @Cached(ttl=0.25)  # 6 hours (0.25 days)
    def get_forecast(self, city):
        return fetch_weather(city)
    
    @Cached(ttl=30)  # 30 days
    def get_historical(self, city, year):
        return fetch_historical(city, year)

Excluding Variables from Cache

class DataProcessor(JSONCache):
    def __init__(self):
        self._excluded_cache_vars = ["temp_data", "api_key"]
        super().__init__()
        self.results = {}
        self.temp_data = []  # Won't be cached
        self.api_key = "secret"  # Won't be cached

Cache Management

processor = DataProcessor()

# Get cache statistics
stats = processor.json_cache_stats()
print(stats)
# {'total_entries': 5, 'functions': {'process': 3, 'analyze': 2}}

# Clear specific function cache
processor.json_cache_clear("process")

# Clear all cache
processor.json_cache_clear()

Cache Status Tracking

Detect whether a @Cached method returned cached data or executed the function:

class DataService(JSONCache):
    def __init__(self):
        super().__init__(data_id="my-service", ttl=7)

    @Cached(ttl=7)
    def fetch(self, key: str) -> str:
        return expensive_operation(key)

svc = DataService()

# last_cache_status is None before any call
print(svc.last_cache_status)  # None

svc.fetch("foo")
print(svc.last_cache_status)  # "miss" (first run) or "hit" (subsequent runs)

# Full per-call history keyed by function signature
print(svc.cache_status)
# {"fetch('foo',){}": "hit"}

Status values:

• "hit" — returned from cache
• "miss" — function was executed (no valid cache entry)
• None — no @Cached method has been called yet

cache_status is populated on init for all keys loaded from cache, and updated on every @Cached call. It is cleared when json_cache_clear() is called.

Logging Control

from cacherator import JSONCache

# Disable logging globally
JSONCache.set_logging(False)

# Enable logging globally (default)
JSONCache.set_logging(True)

# Per-instance control
processor = DataProcessor(logging=False)  # Silent mode

When logging is enabled:

• DynamoDB operations are logged (table creation, reads, writes)
• Local JSON operations are silent (fast, not interesting)

When logging is disabled:

• All operations are silent

## Configuration

### JSONCache Constructor

```python
JSONCache(
    data_id="unique_id",      # Unique identifier (default: class name)
    directory="cache",         # Cache directory (default: "data/cache"), ignored if dynamodb_table set
    clear_cache=False,         # Clear existing cache on init
    ttl=999,                   # Default TTL in days
    logging=True,              # Enable logging (True/False)
    dynamodb_table=None,       # DynamoDB table name — uses DynamoDB instead of local JSON if set
)

@Cached Decorator

@Cached(
    ttl=7,                     # Time-to-live in days (default: class ttl)
    clear_cache=False          # Clear cache for this function
)

Use Cases

API Client with Caching

class GitHubClient(JSONCache):
    def __init__(self):
        super().__init__(data_id="github_client", ttl=1)
    
    @Cached(ttl=0.5)  # 12 hours
    async def get_user(self, username):
        async with aiohttp.ClientSession() as session:
            async with session.get(f"https://api.github.com/users/{username}") as resp:
                return await resp.json()
    
    @Cached(ttl=7)  # 1 week
    async def get_repos(self, username):
        async with aiohttp.ClientSession() as session:
            async with session.get(f"https://api.github.com/users/{username}/repos") as resp:
                return await resp.json()

Database Query Caching

class UserRepository(JSONCache):
    def __init__(self):
        super().__init__(data_id="user_repo", ttl=0.1)  # 2.4 hours
    
    @Cached()
    def get_user_by_id(self, user_id):
        return db.query("SELECT * FROM users WHERE id = ?", user_id)
    
    @Cached(ttl=1)
    def get_user_stats(self, user_id):
        return db.query("SELECT COUNT(*) FROM posts WHERE user_id = ?", user_id)

Machine Learning Model Predictions

class ModelPredictor(JSONCache):
    def __init__(self):
        super().__init__(data_id="ml_predictor")
        self.model = load_model()
    
    @Cached(ttl=30)
    def predict(self, features_hash, features):
        # Cache predictions by feature hash
        return self.model.predict(features)

Best Practices

Recommended Use Cases

• Expensive API calls and network requests
• Database queries with relatively static data
• Heavy computational operations
• Machine learning model predictions
• Data transformations and aggregations

When to Use TTL

• Set short TTL (minutes to hours) for frequently changing data
• Set long TTL (days to weeks) for stable reference data
• Consider data freshness requirements for your application

What Not to Cache

• Non-deterministic functions (random number generation, timestamps)
• Very fast operations (overhead exceeds benefit)
• Non-JSON-serializable objects without custom handling
• Real-time data without appropriate TTL configuration

Performance

Cacherator introduces minimal overhead:

• Cache hit: ~0.1ms
• Cache miss: Function execution time + ~1ms
• Disk I/O: Non-blocking, asynchronous operations

Performance Improvements

• API calls (100ms - 5s) reduced to ~0.1ms
• Database queries (10ms - 1s) reduced to ~0.1ms
• Heavy computations (1s+) reduced to ~0.1ms

Compatibility

• Python: 3.7 and above
• Async: Full support for async/await syntax
• Operating Systems: Windows, macOS, Linux
• Data Types: All JSON-serializable types plus datetime objects
• Optional Dependencies: boto3 (for DynamoDB backend), dynamorator

Changelog

Version 1.3.0

• Changed: Simplified to single-backend model — local JSON (default) or DynamoDB (when dynamodb_table is set), never both simultaneously
• Changed: cache_status values simplified to "hit" / "miss" (removed "l1" / "l2")
• Removed: save_on_del parameter — no longer needed
• Removed: L1→L2 backfill on load
• Removed: Deprecated json_cache_save_db() method

Version 1.2.6

• Added: cache_status dict — per-function-signature hit/miss tracking with L1/L2 source, populated on init and updated on every @Cached call
• Added: last_cache_status — status of the most recent @Cached call ("l1", "l2", "miss", or None)
• Changed: json_cache_clear() now also clears cache_status entries

Version 1.2.5

• Fixed: L1 cache hits now automatically backfill L2 (DynamoDB) when enabled
• Fixed: Removed misleading json_cache_save_db branch in @Cached decorator — json_cache_save() is always used, which handles both L1 and L2

Version 1.2.4

• Added: save_on_del parameter (default False) — __del__ no longer writes to DynamoDB unless opted in, eliminating unnecessary writes on program exit
• Changed: __del__ always saves to local JSON (L1); DynamoDB (L2) write requires explicit json_cache_save() or save_on_del=True
• Removed: Unreliable dirty-check on json_cache_save() — saves are now always performed when called

Version 1.2.3

• Added: Automatic gzip compression for DynamoDB payloads exceeding 100KB
• Added: Warning logged when compressed payload still exceeds DynamoDB's 400KB limit
• Added: Compression is transparent — no API changes required

Version 1.2.2

• Fixed: json_cache_save() now automatically syncs to DynamoDB (L2) when enabled
• Deprecated: json_cache_save_db() is now redundant (use json_cache_save() instead)

Version 1.2.0

• Added: Optional DynamoDB backend for cross-machine cache sharing via dynamorator
• Added: Two-layer cache architecture (L1: local JSON, L2: DynamoDB)
• Added: Constructor parameter dynamodb_table for enabling DynamoDB
• Added: Automatic DynamoDB table creation with TTL support
• Changed: DynamoDB backend now uses dynamorator package
• Changed: Simplified logging to boolean (True/False)
• Removed: Environment variable configuration (use constructor parameter)
• Removed: LogLevel enum (simplified to boolean)

Troubleshooting

Cache Not Persisting

# Explicitly save cache
obj.json_cache_save()

# Check for serialization errors
obj._excluded_cache_vars = ["problematic_attr"]

Cache Not Being Used

# Verify TTL hasn't expired
obj = MyClass(ttl=30)  # Increase TTL

# Ensure arguments are identical (type matters)
obj.func(1, 2)    # Different from
obj.func(1.0, 2)  # (int vs float)

Large Cache Files

# Exclude large attributes
self._excluded_cache_vars = ["large_data"]

# Use separate cache instances
processor1 = DataProcessor(data_id="dataset1")
processor2 = DataProcessor(data_id="dataset2")

Contributing

Contributions are welcome. Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE file for details.

Resources

• GitHub Repository: https://github.com/Redundando/cacherator
• Issue Tracker: https://github.com/Redundando/cacherator/issues
• PyPI Package: https://pypi.org/project/cacherator/

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Developed by Arved Klöhn