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Overview

@Filter performs application-side filtering in Java. All events arrive from MongoDB, and a predicate decides which ones are forwarded to handler methods. Events that don’t pass the filter are silently skipped — but their resume token is still checkpointed as lastSeenToken. Unlike @Pipeline which runs a MongoDB aggregation pipeline once at stream startup to reduce network traffic, @Filter executes on every event and can leverage Spring beans, service calls, and any Java logic.
At most one @Filter method is allowed per @ChangeStream class.

Supported Signatures

Basic Example

Startup Validation (Fail-Fast)

FlowWarden validates @Filter compatibility at application startup and rejects clearly incompatible configurations immediately with a clear error message. The application will fail to start if @Filter is combined with a typed handler covering an operation where MongoDB does not provide a fullDocument.

Why?

@Filter predicates typically call ctx.getFullDocument(), which returns Optional.empty() for DELETE, DROP, and INVALIDATE operations. Running the filter on a typed handler dedicated to one of those operations is almost always a mistake, so FlowWarden rejects that combination at startup. Combining @Filter with the @OnChange catch-all is allowed — DROP and INVALIDATE will reach the predicate, and the predicate must handle Optional.empty() explicitly.

Rule

The following combination is rejected:
This configuration causes a BeanCreationException at startup — the application will not start.
@Filter + typed handler for a no-fullDocument operation
The error message:

Valid Combinations

The following combinations are accepted:

Quick Reference

If you need to handle DELETE events alongside other operations while filtering, use @Pipeline for server-side filtering, guard the @Filter predicate against Optional.empty(), or move the filter logic directly into your handler method.

Checkpoint Interaction

Events rejected by @Filter are not forwarded to the handler, so they never advance lastProcessedToken. Their resume token does, however, advance the in-memory lastSeenToken tracker, which the heartbeat timer (@Checkpoint(saveIntervalSeconds)) persists on each tick. This is what enables the resume cascade level-2 fallback on streams where most events are filter-rejected. On filter-heavy streams, a gap grows between lastSeenToken (advancing) and lastProcessedToken (stalled until a non-filtered event succeeds). Keep saveIntervalSeconds > 0 so the heartbeat can persist lastSeenToken regularly — otherwise the cascade has no level-2 safety net.

Combining with @Pipeline

@Filter can coexist with @Pipeline on the same @ChangeStream, forming a double filtering funnel: Typical use case: pre-filter operationType = insert | update and status = PAID server-side with @Pipeline, then verify application-side with @Filter that the tenant is active via a Spring service call.
When combining @Pipeline + @Filter, events that pass the server-side pipeline reach FlowWarden and advance lastSeenToken regardless of whether @Filter accepts them. The heartbeat timer persists lastSeenToken, enabling the resume cascade level-2 fallback on streams where lastProcessedToken lags behind.

See Also

@Pipeline

Server-side aggregation pipeline filtering

Filtering Events Guide

Complete guide combining @Pipeline and @Filter

@Checkpoint

Resume token persistence and dual checkpoint

Event Handlers

@OnInsert, @OnUpdate, @OnDelete, @OnChange