Observers

Ready to implement observers?

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

Opening Context

How do you handle workflows that span multiple steps without creating tightly coupled systems? Traditional approaches often involve synchronous chains where one service calls another, which calls another, creating fragile dependencies. If any step in the chain fails, the entire workflow breaks. If one component is slow, it blocks everything downstream.

Consider a typical file processing workflow: a user uploads an image, which needs to be resized, optimized, scanned for inappropriate content, tagged with metadata, and indexed for search. In a synchronous system, the user waits for all these steps to complete. In an asynchronous but tightly coupled system, each step needs to know about the next step, creating maintenance nightmares when requirements change.

Observers solve this by creating event-driven architectures where components react to events without knowing who generated them. When a file is uploaded to bucket storage, an observer automatically detects this and begins processing. When it completes, it might trigger another event that another observer handles. Each component does its job independently, and the overall workflow emerges from these independent reactions.

Core Concepts

Event-Driven Activation Observers don’t run continuously - they activate when specific events occur. When a file is uploaded to a bucket, when a message arrives in a queue, or when a storage operation completes, the relevant observer springs into action. This reactive model is both resource-efficient and naturally scalable.

Resource-Specific Binding Each observer is bound to specific resources - particular buckets or queues. This creates clear responsibility boundaries where you know exactly which observer handles events from which resource. The binding also enables efficient event routing since the system knows which code to execute for each event.

Automatic Retry and Error Handling When observers fail to process events, the underlying system provides automatic retry mechanisms. Failed events don’t disappear - they get retried with exponential backoff, and ultimately can be moved to dead letter queues for manual investigation. This robustness makes observers reliable for critical business processes.

Concurrent Processing Multiple observer instances can process events simultaneously, providing natural parallelism for high-volume workflows. When many files are uploaded simultaneously, multiple observer instances can process them in parallel without requiring explicit coordination.

How It Works

Observer execution follows an event-delivery pattern where the underlying infrastructure detects events and invokes observer code. When a file is uploaded to a bucket, the storage system generates an event containing information about the uploaded object. The system then locates the observer bound to that bucket and executes its code with the event details.

The event payload contains everything needed to process the change: object keys for storage events, message content for queue events, metadata about the operation, and timestamps. Observer code can use this information to perform appropriate processing without needing to query for additional context.

Error handling and retry logic operate at the infrastructure level. If observer code throws an exception, the system marks the event as failed and schedules it for retry. Retry attempts use exponential backoff to avoid overwhelming downstream systems. After multiple failed attempts, events can be routed to dead letter queues for manual investigation and reprocessing.

Scaling happens automatically based on event volume. When many events arrive simultaneously, the system spawns multiple observer instances to handle the load. When event volume decreases, unused instances are cleaned up. This elasticity ensures that observers can handle both steady-state and burst workloads efficiently.

Trade-offs and Considerations

Asynchronous vs Real-Time Observers excel at asynchronous processing but aren’t suitable for operations requiring immediate feedback to users. There’s inherent latency between event generation and observer execution, making observers inappropriate for synchronous user interactions.

Event Ordering vs Parallel Processing While observers can process events in parallel for better throughput, this means events might not be processed in the exact order they occurred. Applications requiring strict ordering need to implement coordination mechanisms or use different architectural patterns.

Eventual Consistency vs Immediate Consistency Observer-based workflows create eventually consistent systems where changes propagate over time rather than happening atomically. This is usually acceptable for background processing but might not work for workflows requiring immediate consistency.

Debugging and Observability Event-driven systems can be harder to debug since the flow of execution jumps between different components triggered by events. Good logging and monitoring become critical for understanding system behavior and diagnosing issues.

Resource Usage vs Cost Observers consume compute resources only when events occur, making them cost-effective for intermittent workflows. However, the infrastructure needed to support event detection and delivery adds operational overhead that might not be justified for simple synchronous operations.

Connections

Observers naturally integrate with queues to create sophisticated workflow orchestration systems. Services can send messages to queues, observers process those messages and potentially send results to other queues, creating multi-stage processing pipelines that are both resilient and scalable.

The relationship with bucket storage enables powerful file processing workflows. When users upload files, observers can automatically trigger image processing, document analysis, virus scanning, or content indexing without requiring synchronous processing that would slow down the user experience.

Database integration allows observers to update persistent state based on events. An observer processing uploaded images might update database records with metadata, processing status, or analysis results. This creates a pattern where events drive data updates across the system.

Actors can coordinate with observers for stateful event processing. An observer might send updates to specific actors based on event content, enabling stateful workflows that maintain context across multiple events while benefiting from observer-based triggering mechanisms.

The connection to external systems makes observers powerful integration points. Observers can react to internal events by calling external APIs, sending notifications, updating third-party systems, or triggering webhooks, creating bridges between your application and the broader ecosystem.