Common anti-patterns related to caching that can impact application performance
Caching is a technique used to store copies of data in a high-speed data storage layer to improve retrieval times. While caching can significantly improve application performance, improper implementation can lead to various issues. This document outlines common anti-patterns related to caching and provides optimization strategies.
Cache Invalidation Failures
Java Example:
JavaScript Example:
This anti-pattern occurs when an application fails to properly invalidate or update cached data after the underlying data has changed. This leads to stale data being served to users, potentially causing inconsistencies, incorrect business decisions, or confusing user experiences.
Java Example:
JavaScript Example:
Implement proper cache invalidation strategies to ensure data consistency. When data is updated, either remove the corresponding cache entry or update it with the new value. For distributed systems, consider using cache invalidation events or time-based expiration policies. Always ensure that all code paths that modify data also handle cache invalidation appropriately.
Cache Stampede
Java Example:
JavaScript Example:
Cache stampede (also known as thundering herd or cache avalanche) occurs when many concurrent requests attempt to access a cached item that is either expired or missing, causing all of them to simultaneously try to fetch the data from the underlying data source. This can overwhelm the database or service, leading to increased latency, timeouts, or even system failures.
Java Example:
JavaScript Example:
Request Coalescing: When multiple requests for the same resource arrive simultaneously, only one request should fetch from the underlying data source while others wait for that result.
Staggered Expiration Times: Add random jitter to cache expiration times to prevent many items from expiring simultaneously.
Background Refresh: Proactively refresh cache items before they expire, ideally during low-traffic periods.
Soft Expiration: Continue serving stale data while asynchronously refreshing the cache.
Cache Lock: Use a distributed lock to ensure only one process can refresh a particular cache entry at a time.
Fallback to Stale Data: If fetching fresh data fails, temporarily continue serving stale data rather than failing completely.
Circuit Breaker Pattern: Implement circuit breakers to prevent overwhelming the backend system during outages.
Tiered Caching: Implement multiple layers of caching with different expiration policies.
By implementing these strategies, you can prevent cache stampedes and ensure your application remains responsive even under high load or when cached data expires.
Inefficient Cache Eviction Policies
Java Example:
JavaScript Example:
This anti-pattern occurs when an application uses inefficient or inappropriate cache eviction policies. Common issues include clearing the entire cache when it’s full, randomly removing entries without considering their usage patterns, or using a one-size-fits-all approach for all types of data. This leads to poor cache hit ratios, unnecessary recomputation of values, and degraded application performance.
Java Example:
JavaScript Example:
Choose Appropriate Eviction Policies:
Use Different Policies for Different Data Types:
Implement Size-Aware Caching:
Monitor Cache Effectiveness:
Consider Hybrid Approaches:
Implement Segmented Caching:
Pre-emptive Eviction:
By implementing appropriate cache eviction policies, you can significantly improve cache hit ratios, reduce resource consumption, and enhance application performance.