Ballerina @cacheable Annotation and External Data Source Synchronization

Direct answer

Ballerina's @cacheable annotation does not provide a documented programmatic API to invalidate a specific cache entry or force an immediate purge. It is a time-to-live (TTL)–based mechanism: responses are cached by request parameters and automatically expire only after the configured duration. When an external process updates the underlying data source, the service cache receives no invalidation signal, so stale responses persist until the TTL elapses.

Likely explanation vs. confirmed behavior

The likely explanation is that @cacheable is intended as a declarative convenience for read-heavy, slowly changing data rather than a full cache-coherence solution. The confirmed behavior—based on the current annotation specification—is that no public purge or key-level invalidation method exists. Clearing the internal runtime cache without a supported interface would require restarting the service, which is not viable for production synchronization.

Recommended approach: manual cache client

To keep the cache consistent with external writes, replace the annotation with an explicit caching layer backed by an external store such as Redis or Memcached. This gives you direct control over individual keys.

Implementation steps

1. Remove @cacheable from the resource.
2. Introduce a cache client into the service.
3. On each request, check the cache first. On a miss, fetch from the data source and write the result back to the cache with a TTL.
4. When the external process changes the data, have that process—or an event listener in your service—delete the affected key using the cache client's native command.

Fallback when events are unavailable

If the external writer cannot emit invalidation events, the only option with @cacheable is to reduce the TTL so that staleness is bounded. This is an eventual-consistency trade-off and should be sized to your data-change frequency.

Verification and version assumptions

Cache internals can evolve between Ballerina releases. Verify the lack of an invalidation API against the documentation for your exact version. A useful test is to run a minimal service with @cacheable, update the backing store externally, and confirm the response remains unchanged until the TTL expires. Then compare that with a manual Redis-backed implementation where a direct key deletion refreshes the response immediately.

One diagnostic question

Can the external data source emit a change event—for example, through a message queue, webhook, or database trigger? If yes, an event-driven manual cache is the robust path. If no, a short TTL is the only feasible mitigation.