Caching Strategy
- lru-cache:
The
lru-cachepackage also uses the LRU caching strategy but adds more features, such as setting a maximum size for the cache (in bytes) and the ability to specify a time-to-live (TTL) for cached items. This allows for more fine-grained control over how and when items are evicted from the cache. - quick-lru:
The
quick-lrupackage provides a fast and efficient implementation of the LRU caching strategy with a focus on performance and low memory usage. It offers a simple API for adding, retrieving, and deleting items from the cache, making it easy to use in applications that require quick access to cached data. - lru:
The
lrupackage implements a basic LRU (Least Recently Used) caching strategy, which evicts the least recently accessed items when the cache reaches its limit. This strategy helps keep frequently accessed data in memory while freeing up space for new entries. - lru-memoize:
The
lru-memoizepackage combines LRU caching with memoization, a technique that stores the results of expensive function calls and returns the cached result when the same inputs occur again. This package is particularly useful for optimizing functions that are called repeatedly with the same arguments, as it reduces the need for redundant calculations.
Memory Management
- lru-cache:
The
lru-cachepackage includes memory management features, such as setting a maximum size for the cache (in bytes) and supporting time-based expiration for cached items. This allows developers to control how much memory the cache uses and ensures that stale or unused items are removed automatically, preventing memory leaks. - quick-lru:
The
quick-lrupackage provides a lightweight and efficient implementation of LRU caching with minimal memory overhead. It does not include advanced memory management features, but its simple design and efficient eviction strategy help keep memory usage low while maintaining fast access to cached data. - lru:
The
lrupackage does not provide built-in memory management features, such as limiting the total size of the cache or automatically expiring items after a certain period. It relies on the LRU strategy to evict items based on their access patterns, which helps manage memory usage over time but does not prevent the cache from growing indefinitely. - lru-memoize:
The
lru-memoizepackage focuses on memory management for function results by caching the output of function calls based on their input arguments. It does not provide features for managing the overall memory usage of the cache, but it does limit the number of cached results based on the LRU strategy, which helps keep memory usage in check while still allowing for efficient caching of function results.
Expiration and Eviction
- lru-cache:
The
lru-cachepackage supports both LRU eviction and time-based expiration of cached items. Developers can set a maximum size for the cache and specify a time-to-live (TTL) for each item, after which it will be automatically removed from the cache. This combination of LRU eviction and expiration helps keep the cache fresh and prevents it from holding onto stale data. - quick-lru:
The
quick-lrupackage implements LRU eviction but does not support expiration of cached items. It removes the least recently used items from the cache when it reaches its capacity, but once an item is cached, it will remain until it is evicted. This makes it a simple and efficient LRU cache, but it lacks the ability to automatically expire items after a certain time. - lru:
The
lrupackage implements eviction based on the LRU (Least Recently Used) algorithm, which removes the least recently accessed items from the cache when it reaches its capacity. However, it does not support expiration of cached items based on time or any other criteria, meaning that once an item is cached, it will remain in the cache until it is evicted due to LRU policy. - lru-memoize:
The
lru-memoizepackage focuses on caching the results of function calls based on their input arguments. It does not provide built-in expiration for cached items, but it limits the number of cached results based on the LRU strategy. Once the cache reaches its limit, the least recently used results are evicted to make room for new ones, ensuring that the cache remains efficient without retaining too many old values.
Use Case
- lru-cache:
The
lru-cachepackage is suitable for applications that need a more robust caching solution with features like size limits, time-based expiration, and event hooks. It is ideal for scenarios where memory management and cache freshness are important, such as caching API responses or database queries. - quick-lru:
The
quick-lrupackage is ideal for performance-sensitive applications that need a fast and lightweight LRU caching solution. It is suitable for scenarios where you want to cache data with minimal overhead and do not require advanced features like expiration or size limits. - lru:
The
lrupackage is best suited for applications that require a simple and efficient LRU caching solution without any additional features. It is ideal for scenarios where you want to cache data based on access patterns but do not need advanced features like expiration or size limits. - lru-memoize:
The
lru-memoizepackage is designed for optimizing the performance of functions that are called repeatedly with the same arguments. It is ideal for use cases where you want to cache the results of expensive computations to avoid redundant calculations, such as in data processing or rendering tasks.
Ease of Use: Code Examples
- lru-cache:
LRU Cache Example with
lru-cacheimport LRU from 'lru-cache'; const cache = new LRU({ max: 100, // Maximum size of the cache ttl: 1000 * 60 // Time-to-live for cached items (1 minute) }); cache.set('key1', 'value1'); console.log(cache.get('key1')); // Output: 'value1' setTimeout(() => { console.log(cache.get('key1')); // Output: undefined (item expired) }, 1000 * 61); - quick-lru:
Simple LRU Cache Example with
quick-lruimport QuickLRU from 'quick-lru'; const lru = new QuickLRU({ maxSize: 100 }); // Create an LRU cache with a max size of 100 lru.set('key1', 'value1'); lru.set('key2', 'value2'); lru.set('key3', 'value3'); lru.set('key4', 'value4'); // This will evict 'key1' as it is the least recently used console.log(lru.get('key1')); // Output: undefined console.log(lru.get('key2')); // Output: 'value2' - lru:
Basic LRU Cache Example with
lruimport LRU from 'lru'; const cache = new LRU(3); // Create an LRU cache with a limit of 3 items cache.set('a', 1); cache.set('b', 2); cache.set('c', 3); console.log(cache.get('a')); // Output: 1 cache.set('d', 4); // 'b' is evicted because it is the least recently used console.log(cache.get('b')); // Output: undefined - lru-memoize:
Memoization Example with
lru-memoizeimport memoize from 'lru-memoize'; const memoizedFn = memoize(3); // Limit cache to 3 items const slowFunction = (num) => { // Simulate a slow computation for (let i = 0; i < 1e9; i++); // Delay return num * 2; }; const result1 = memoizedFn(slowFunction)(5); const result2 = memoizedFn(slowFunction)(5); // Cached result console.log(result1, result2); // Output: 10 10