Choosing and Implementing Caching for Spring Boot Microservices

Ever wondered why caching is so important in a Spring Boot application? It boosts performance by reducing data retrieval time. This article dives into the impact of caching on microservice patterns and discusses how to choose the right cache based on usability, speed, availability, and observability, while also covering ways to maximize cache performance and availability.

1 Cache Implementation

1.1 Impact of Cache on Microservice Architecture

Imagine an Edge API exposed to the Internet that triggers additional requests to services A and B, which in turn call services C and D. Introducing a client‑side cache can dramatically improve application performance and break this dependency chain.

1.2 Choosing the Right Cache

Before picking a cache, understand your application’s requirements and evaluate the following factors:

Usability – Does adding a new component to the system make sense?

Speed – How long does it take to retrieve or set a value in the cache?

Availability – How does the cache improve overall system availability?

Observability – How easy is it to infer the system’s state?

2 Cache Types

There are three main cache categories:

2.1 Local Cache

Limited to the local instance running on a single application/node.

Faster because data is stored locally.

Lacks consistency since data isn’t shared with other caches.

Inefficient when large amounts of data need to be shared across nodes.

Use case: data specific to a single instance that doesn’t need to be shared.

Local Cache:

2.2 Distributed Cache

Data is shared across multiple instances, allowing any node to access the cache (useful when several instances need a common cache).

Network latency may add some delay when accessing remote nodes, but not always.

Consistency is maintained as each instance propagates changes to others.

Highly scalable.

Distributed Cache:

2.3 Hierarchical Cache

Each client keeps both a local cache and a remote cache as a fallback.

Conceptually similar to CPU caching.

if local_cache_hit(request):
    return get_from_local_cache(request)
else:
    if remote_cache_hit(request):
        return get_from_remote_cache(request)
    else:
        response = call_a(request)
        set_local_cache_in_background(response)
        set_remote_cache_in_background(response)
        return response

All cache types aim to maximize cache hits and improve overall system performance. When dealing with frequently updated dynamic data that also needs to be cached, consider setting a Time‑to‑Live (TTL). A long TTL (e.g., 24 hours) may return stale data, while a short TTL improves freshness but can increase server load and latency.

Two common strategies are discussed:

Active Invalidation – Ideal for event‑driven architectures. Clients listen for server‑emitted events and invalidate or update cache entries accordingly, allowing a longer TTL.

Background Refresh – When the server does not emit events, a background task periodically refreshes cache entries, reducing staleness and latency.

3 Conclusion

In essence, caching in Spring Boot is a key lever for performance. By breaking dependency chains and optimizing cache hits, it becomes an essential tool for building efficient and responsive systems in the microservice world.