Distributed Cache

A distributed cache lets multiple application instances share cached data through a separate cache service instead of keeping each instance’s cache only in local process memory. It improves cache consistency across instances and can reduce repeated access to slower backing systems.

A distributed cache is an external caching layer that is reachable by multiple application instances. Common deployments place a cache service between the application and a backing store such as a database, so repeated reads can often be served without querying the backing store.

Because the cache is outside the application process, one instance can populate an entry and another instance can read it. This is useful when requests are load-balanced across several servers and local in-memory caches would otherwise contain different values.

The cache usually stores entries under keys with values and an expiration policy such as a TTL. Applications must handle cache misses, expiration, serialization, connection failures, and the possibility that cached data is stale. A distributed cache therefore improves access patterns but does not automatically provide database-level consistency.

In Java applications, frameworks such as Spring can abstract cache operations through annotations or cache APIs, while a separate cache product provides the shared storage. The exact behavior depends on the cache implementation, serialization format, eviction policy, and configuration.

Key points

• Multiple application instances can access the same cache entries.
• The cache is external to the application process and therefore is not tied to one JVM.
• Cache misses normally fall back to a backing data source.
• TTL and eviction policies limit how long entries remain available.
• Distributed caches introduce network, serialization, failure, and staleness considerations.

Interview questions

Why would a Java application use a distributed cache instead of a local cache?

A local cache belongs to one JVM, so different application instances can have different cached values. A distributed cache gives those instances access to shared cached entries. This can reduce repeated database or service calls while avoiding a separate cache copy in every instance. The tradeoff is that cache access now involves a network dependency.

What is a cache miss, and what should happen after one?

A cache miss occurs when the requested key is not present or its entry is no longer usable. The application normally retrieves the data from the backing source, such as a database, and may then populate the cache. This is commonly called a cache-aside pattern. The application should also consider concurrent misses and failures when deciding how to populate the entry.

What is TTL and why is it important in a distributed cache?

TTL, or time to live, specifies how long a cache entry should remain valid before expiration. It bounds how long an application may serve an old value when the underlying data changes. A shorter TTL generally reduces the stale-data window but can increase cache misses and backing-store traffic. The appropriate value depends on the freshness requirements and workload.

What consistency problems can occur when using a distributed cache with a database?

The cache and database can temporarily disagree because they are separate systems. For example, an application may update the database while an old value remains cached. Common approaches include invalidating or updating the cache when data changes, using carefully chosen TTLs, and selecting an appropriate cache population strategy. The correct design depends on whether the application can tolerate stale reads and how updates are coordinated.

What failure modes should you consider when introducing a distributed cache?

The cache can become unavailable, slow, overloaded, or partially populated. Network failures and serialization errors can also prevent otherwise healthy application instances from reading or writing entries. The application should define fallback behavior, timeouts, and appropriate resource limits so that cache failures do not unnecessarily take down the primary data path. Monitoring cache latency, hit rate, errors, and capacity is also important.