The First Event: Validating a Real-Time Metrics Pipeline in a Distributed S3 Cluster

The Message

[assistant] [bash] echo '{"jsonrpc":"2.0","method":"RIBS.ClusterEvents","params":[10],"id":1}' | websocat ws://localhost:9010/rpc/v0 2>&1
{"id":1,"jsonrpc":"2.0","result":[{"Timestamp":1769867179,"Type":"node_started","NodeID":"kuri-1","Message":"S3 server started","Severity":""}]}

At first glance, this is a simple test: a developer sends a JSON-RPC request over a WebSocket connection and receives a one-element array containing a node_started event. But this message represents a critical inflection point in a much larger engineering effort—the moment when a previously stub-ridden metrics system first produced real, live data from a running distributed cluster. Understanding why this message was written, what it reveals about the system's architecture, and the chain of reasoning that led to it requires unpacking several layers of context.

The Context: From Empty Stubs to Live Metrics

The message belongs to a debugging and implementation session for a horizontally scalable S3-compatible storage gateway built on the Filecoin network. The system follows a three-layer architecture: stateless S3 frontend proxies accept client requests and route them to Kuri storage nodes, which in turn store data in a shared YugabyteDB database. The session had already resolved numerous infrastructure issues—HTTP route conflicts in Go 1.22's ServeMux, missing database columns, container networking problems, and a fundamental architectural correction that separated the S3 proxy layer from the storage nodes.

Just moments before this message, the user had sent a series of JSON-RPC requests to the cluster's WebSocket endpoint and received uniformly empty responses. The RequestThroughput, LatencyDistribution, ErrorRates, ActiveRequests, and ClusterEvents methods all returned zeroed-out data structures—empty arrays, empty maps, and zero values. Only ClusterTopology returned meaningful data, listing the two storage nodes and one proxy with health status. The user's message, with its terse "still empty" annotation, made the problem clear: the monitoring dashboard was showing node topology but no operational metrics.

The assistant correctly diagnosed the root cause. The RPC handlers in rbstor/diag.go were stub implementations that returned hardcoded empty structures. They had never been wired to actual data collection. The ClusterTopology method worked because it performed active health checks against backend nodes, but the throughput, latency, error rate, and event methods had no instrumentation backing them.

Designing the Metrics Collector

The assistant's response was to build a proper metrics collection system from scratch. The new ClusterMetrics structure in rbstor/cluster_metrics.go introduced a rolling window data store that could track request throughput (total, reads, writes), latency percentiles (P50, P95, P99), error rates by type, active request counts, and I/O byte throughput. Each metric type had its own configurable retention window—five minutes for throughput and latency, ten minutes for error rates—with automatic pruning of expired data points.

A key design decision was the use of a mutex-protected in-memory store rather than persisting metrics to the database. This choice reflected an assumption about the monitoring system's purpose: it was intended for real-time operational visibility, not historical analysis. The metrics would survive only as long as the node process lived, which was acceptable for a test cluster and aligned with the lightweight, non-invasive monitoring philosophy. The assistant also assumed that the metrics collector would be instantiated once per node and shared across all request-handling code paths, which required careful threading of the collector reference through the dependency injection framework.

Wiring the Pipeline

The assistant then modified the S3 server code in server/s3/server.go to instrument every request. A wrapper handler was added that incremented counters, recorded latency, and tracked byte throughput before and after delegating to the actual S3 handler. This approach introduced minimal overhead—a few atomic operations and a timer per request—while providing comprehensive visibility into cluster activity.

The fx.go lifecycle file received an important addition: an event emission on server startup. When the S3 server began listening, it would push a node_started event into the metrics collector's event buffer. This was the event that would later appear in the subject message's response. The assistant also added a ClusterEvents method to the RPC handler that would drain the event buffer and return recent events to the caller.

The Verification Step

After rebuilding the Docker image, redeploying the containers, uploading ten test objects, and reading them back, the assistant ran two verification checks. The first tested RequestThroughput and received a non-empty response: a single timestamp with a rate of 0.5 requests per second (reflecting the ten operations spread across the node's uptime). The second check, which is the subject message, tested ClusterEvents and received the node_started event.

This second verification was the more important of the two. While the throughput metric proved that the instrumentation was counting requests, the event proved that the entire pipeline—from event generation in fx.go, through storage in the metrics collector, to retrieval via the WebSocket RPC endpoint—was functioning correctly. The event carried a Unix timestamp of 1769867179, which corresponded to the container's startup time, confirming that the event had been recorded at the correct moment and had persisted in the buffer until queried.

What the Message Reveals About the System

Several architectural details are visible in this single response. The Severity field is empty, indicating that the event severity classification was not yet implemented—a future enhancement deferred for now. The NodeID field contains "kuri-1", which matches the node naming convention established in the Docker Compose configuration. The event type node_started aligns with the code added to fx.go, confirming that the instrumentation was correctly wired.

The fact that only one event appears (rather than two, for both kuri-1 and kuri-2) is also informative. The WebSocket connection was made to port 9010, which proxies to kuri-1's web UI. Kuri-2's events would be visible by querying port 9011. This demonstrates the per-node isolation of the metrics collection—each node maintains its own event buffer and metric state, and the frontend aggregates data by querying both endpoints.

Assumptions and Their Validity

The assistant made several assumptions in implementing this system. First, it assumed that in-memory metrics collection with a rolling window was sufficient for the test cluster's needs. This was reasonable for a development environment but would need revisiting for production deployments where metrics must survive process restarts and support longer-range trend analysis. Second, it assumed that a mutex-based approach would not become a bottleneck under high concurrency. For the test cluster handling tens of requests per second, this was safe, but a production system handling thousands would require lock-free data structures or per-CPU sharding.

The assistant also assumed that the ClusterEvents buffer should be drained on read (the events disappear after being retrieved). This is evident from the response containing only the startup event despite the node having processed ten uploads and ten reads. The design choice treats events as ephemeral notifications rather than an audit log, which aligns with the real-time monitoring use case but would be inappropriate for compliance or debugging scenarios requiring historical event reconstruction.

The Thinking Process

The assistant's reasoning is visible in the sequence of actions leading to this message. After identifying the stub problem, the assistant read the interface definitions to understand the expected data structures, then built the metrics collector to match those interfaces exactly. The careful attention to JSON field naming (camelCase) reveals an awareness of the frontend expectations—the React components would be consuming these responses and expected specific property names.

The verification strategy also shows deliberate thinking. Rather than immediately testing the most complex metric (latency distribution, which requires multiple data points), the assistant started with the simplest possible test: uploading and reading a few objects, then checking throughput and events. This incremental approach minimized debugging surface area and provided quick validation of the core instrumentation before moving to more complex metrics.

Conclusion

This message, for all its apparent simplicity, marks the transition of the cluster monitoring system from a theoretical framework to a working, data-producing subsystem. The single node_started event represents the successful integration of metrics collection, event recording, RPC exposure, and WebSocket transport—a pipeline that spans multiple Go packages, a Docker container, and a distributed database backend. It transformed the "still empty" dashboard into a live monitoring interface, completing a critical piece of the horizontally scalable S3 architecture and providing the operational visibility necessary for the next phases of development.