Payam Beigi – Software Developer Portfolio

In the competitive realm of web applications, performance can make or break the user experience. Our Java backend was robust but began to lag under heavy load. Implementing effective caching strategies became our mission to enhance performance and maintain scalability. Here’s our journey.

Identifying Performance Bottlenecks: Initial profiling of our Java application using tools like JProfiler and VisualVM revealed that the most significant delays were database access and heavy computation processes.

The Role of Caching: Caching is the process of storing copies of files in a cache, or temporary storage location, so that they can be accessed more quickly. For our Java backend, it meant keeping frequently accessed data in memory to avoid repetitive database calls and computation.

Choosing the Right Caching Strategy: We evaluated various caching strategies, such as write-through, write-around, and write-back caching. Given the nature of our application, we settled on a combination of write-through and read-through caching to ensure data integrity and quick access.

Implementing Cache with EHCache: We chose EHCache for its simplicity and seamless integration with Spring Framework. It allowed us to cache data at the method level using annotations, which made it easier to maintain and understand.

Cache Configuration: The cache was configured to handle eviction policies, coherence strategies, and expiration times. We set up a time-to-live (TTL) and time-to-idle (TTI) policy to ensure that the data was fresh and the cache was not consuming unnecessary resources.

Handling Cache Eviction: To manage memory, we established a Least Recently Used (LRU) eviction policy. This ensured that the cache did not grow indefinitely and that only the most relevant data was kept in memory.

Distributed Caching with Hazelcast: As we scaled, a single instance of in-memory cache was not enough. We implemented Hazelcast to distribute the cache across our cluster, which improved performance and added redundancy.

Cache Invalidation Strategy: Invalidating stale data was crucial. We implemented a notification system where any updates to the data would invalidate the related cache entries across all nodes, ensuring data consistency.

Cache Monitoring: To optimize cache performance, we used monitoring tools to track hit/miss ratios, cache sizes, and eviction rates. This data helped us to tweak our configurations and improve cache effectiveness.

Graceful Degradation Handling: We designed our caching layer to handle failures gracefully. If the cache was unavailable, the application would revert to fetching data directly from the database, ensuring uninterrupted service.

Lessons Learned: Caching is powerful but not a silver bullet. It required careful consideration of what to cache, when to invalidate, and how to synchronize across a distributed system.

Conclusion: The introduction of caching transformed our Java backend from struggling under load to handling it with ease. It wasn’t just about implementing caching libraries; it was about designing a strategy that included choosing the right tools, configuring them effectively, and continuously monitoring and tweaking to ensure optimal performance.

Related Tech Stack:

• Java (Programming language)
• Spring Framework (Application framework)
• JProfiler and VisualVM (Performance profiling tools)
• EHCache (Caching solution)
• Hazelcast (Distributed caching)
• Cache monitoring tools