Observers

Observers in Raindrop provide event-driven processing that automatically responds to changes in storage buckets and message queues. Instead of polling for changes, observers receive immediate notifications when events occur, enabling reactive architectures and background processing workflows.

Ready to implement observers?

Check out the Observer reference documentation for detailed API information, code examples, and implementation guidance.

What Observers Provide

Event-Driven Processing Automatically respond to storage changes and queue messages without polling, enabling real-time reactions to system events.

Decoupled Architecture Separate triggering actions from processing logic, allowing components to operate independently and improving system resilience.

Background Task Processing Handle file processing, notifications, and data transformations asynchronously without blocking user-facing operations.

Resource-Specific Monitoring Each observer watches specific buckets or queues, providing focused responsibility and predictable event handling.

When to Use Observers

Good Fit

• File Processing: Automatic image resizing, document conversion, or content analysis when files are uploaded
• Background Jobs: Email sending, data synchronization, and workflow processing triggered by queue messages
• Event-Driven Workflows: Multi-step processes where each stage triggers the next through events
• Integration Pipelines: Automatic processing when data arrives from external sources

Consider Alternatives

• Immediate Responses: Operations requiring instant feedback should use Services with direct responses
• Scheduled Operations: Time-based processing works better with Tasks than event-driven triggers
• High-Frequency Updates: Very frequent events might be better handled through direct processing

Integration Patterns

Storage Event Processing Observers monitor bucket changes to automatically process uploaded files, generate thumbnails, or extract metadata without user waiting.

Queue Message Handling Process messages from queues to handle background tasks, notifications, and integration workflows with natural retry behavior.

Pipeline Architectures Chain observers together where one observer’s processing triggers events that other observers handle, creating sophisticated processing flows.

Cross-Component Communication Enable loose coupling between services and actors by using events to trigger processing without direct dependencies.

Observers excel at building reactive systems where events automatically trigger appropriate processing without tight coupling between components.