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Caching Antipatterns

(Compiled from AWS, Azure, Google and and other community sources)

The following are antipatterns to avoid...

Avoid:​

  • Caching everything
    • Caching everything can lead to performance issues, increased memory usage, and increased complexity. Only cache what you need to.
  • Not expiring or invalidating cache
    • Not expiring or invalidating cache can lead to stale data being served to users, causing issues with data consistency and accuracy.
  • Cache without considering cache coherence
    • Cache coherence is critical for distributed systems, and not considering it can lead to data inconsistencies and synchronization issues.
  • Ignoring cache performance
    • Caching performance needs to be measured, monitored, and optimized, and ignoring cache performance can lead to poor performance and negative user experiences.
  • Overreliance on caching
    • Overreliance on caching can make the application complex, brittle, and inflexible, and may not solve performance issues.
  • Storing non-serializable objects in the cache
    • Caching non-serializable objects can cause issues with cache management, seriali
  • Caching data that is not frequently accessed
    • Caching data that is not frequently accessed can lead to increased memory usage and cache management overhead, reducing the effectiveness of caching.
  • Caching user-specific data globally
    • Caching user-specific data globally can lead to data leakage, security vulnerabilities, and data consistency issues.
  • Not using the right caching strategy for the use case
    • Different caching strategies (e.g., write-through, write-behind, read-through, read-behind) have different performance and consistency trade-offs, and not using the right strategy for the use case can lead to poor performance and data consistency issues.
  • Not monitoring the cache
    • Caching needs to be monitored to ensure that it is working correctly, to detect and resolve issues, and to optimize performance. Not monitoring the cache can lead to poor performance, user dissatisfaction, and security vulnerabilities.
  • Not considering cache storage capacity
    • Caching requires storage capacity, and not considering this can lead to issues with memory usage, performance, and scalability.
  • Caching data without considering data volatility
    • Caching data that is highly volatile, such as frequently updated data or user session data, can lead to data consistency issues and reduced effectiveness of caching.
  • Storing sensitive data in the cache
    • Storing sensitive data, such as personally identifiable information or authentication tokens, in the cache can lead to security vulnerabilities and data leakage.
  • Caching data without considering network latency
    • Caching data without considering network latency can lead to performance issues, as data may take longer to retrieve from the cache than to retrieve from the original data source.
  • Not considering cache consistency in distributed systems
    • Cache consistency is critical in distributed systems, and not considering it can lead to data inconsistencies, synchronization issues, and reduced effectiveness of caching.
  • Caching without considering concurrency
    • Caching without considering concurrency can lead to race conditions and other concurrency issues, resulting in data inconsistencies and reduced effectiveness of caching.
  • Not considering cache warm-up time
    • Cache warm-up time is the time it takes to populate the cache with frequently accessed data, and not considering this can lead to poor performance, especially during periods of high load or when the cache is restarted.