Improving Performance with lru2cache: A Two-Layer Caching Mechanism
The demand for fast and efficient software solutions has never been higher. As software engineers and solution architects, it is our responsibility to constantly seek ways to optimize performance. One such solution is the lru2cache package, a two-layer caching mechanism developed by 3Top, Inc. This article will explore the features of lru2cache, its advantages over functools.lru_cache, and how to effectively use and manage the cache.
Understanding lru2cache
lru2cache is a decorator that combines a local cache with a shared cache to improve performance. Based on the Python 2.7 back-port of functools.lru_cache, lru2cache was specifically developed for the ranking and recommendation platform of 3Top, Inc. The first layer of caching is stored in a callable that wraps the function or method, using a dictionary to store the cached results. The decorator handles the discarding of the Least Recently Used (LRU) values. The second layer of caching makes use of the shared cache backend provided by Django’s cache framework.
Benefits Over functools.lru_cache
Combining both types of cache offers several advantages over using either cache alone. The combination of a local cache and a shared cache eliminates the latency of calls to a shared cache, making the process much faster. Additionally, lru2cache allows the ability to choose whether or not to cache None results. This feature reduces the frequency of cache invalidations, further improving performance.
Usage and Configuration
Using lru2cache is straightforward. Simply add the decorator to any function or method that requires caching. The decorator accepts optional arguments such as l1_maxsize, none_cache, typed, l2cache_name, and inst_attr to customize the cache behavior.
To install lru2cache, use pip:
pip install lru2cache
After installation, configure a shared cache as an l2 cache in your Django settings file. This configuration includes specifying the backend, location, and timeout for the shared cache.
Managing the Cache
While lru2cache does not provide a timeout for its cache, it offers other mechanisms for managing the cache programmatically.
To view cache statistics, use f.cache_info(), which displays the number of hits and misses for the local and shared caches, as well as the maximum size and current size of the local cache.
To clear the cache and statistics associated with a function or method, use f.cache_clear(). To clear the shared cache, use Django’s cache framework: cache.get_cache('l2cache').clear().
To invalidate cached results for a specific set of arguments, including the instance, use f.invalidate(*args, **kwargs). For methods, pass the instance explicitly: foo.f.invalidate(foo, a, b).
While lru2cache does not currently provide a function to refresh the cache, you can achieve this by calling invalidate followed by the function.
Conclusion
In conclusion, lru2cache offers a powerful two-layer caching mechanism that can significantly improve performance in software applications. By combining a local cache and a shared cache, lru2cache eliminates latency, reduces computation, and provides the flexibility to choose whether or not to cache None results. With easy installation and configuration, as well as robust cache management capabilities, lru2cache is a valuable tool for any software engineer or solution architect seeking to optimize performance.
If you have any questions or would like to learn more about lru2cache, please feel free to ask during the upcoming technical documentation presentation.
References
- lru2cache GitHub repository: https://github.com/3Top/lru2cache
- lru2cache documentation: https://github.com/3Top/lru2cache/raw/master/README.rst
- functools.lru_cache documentation: https://docs.python.org/3/library/functools.html#functools.lru_cache
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