What are the best practices for in-memory caching?
An example of an AI chatbot conversation about best practices for in-memory caching.
What are the best practices for in-memory caching?
In-memory caching is crucial for improving application performance. Key practices include choosing the right cache size, using appropriate eviction policies, and ensuring data consistency.
Can you explain what eviction policies are?
Eviction policies determine how data is removed from the cache when it reaches its limit. Common policies include LRU (Least Recently Used), LFU (Least Frequently Used), and FIFO (First In, First Out).
How do I choose the right cache size?
Choosing the right cache size depends on your application's data access patterns and memory constraints. Monitor usage and adjust based on performance metrics.
What tools can I use for in-memory caching?
Popular tools for in-memory caching include Redis, Memcached, and Apache Ignite. Each has its strengths depending on your use case.
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