The Moment of Proof: Validating a Cluster Monitoring Endpoint in a Distributed S3 Architecture

The Message

$ sleep 8 && cd /home/theuser/gw/test-cluster && FGW_DATA_DIR=/data/fgw2 docker compose exec kuri-1 wget -q -O- http://localhost:9010/api/stats 2>&1
{"nodeId":"kuri-1","groupsCount":1,"storageUsed":19446967194,"requestsPerSecond":0}

This is message 893 in a long coding session building a horizontally scalable S3-compatible storage system for the Filecoin Gateway. On its surface, it is a simple curl command against a local endpoint inside a Docker container, returning a 44-byte JSON blob. But this message represents a critical inflection point — the moment where an architectural insight about distributed monitoring was translated into code, deployed, and verified to work. It is the bridge between a problem discovered and a solution confirmed.

The Context: A Monitoring Blind Spot

To understand why this message matters, we must understand what preceded it. The assistant had built a test cluster with two Kuri storage nodes (kuri-1 and kuri-2), a shared YugabyteDB backend, and a stateless S3 frontend proxy. The cluster was running, traffic was flowing, and a React-based monitoring UI was displaying cluster topology. But something was wrong: the UI showed traffic only on kuri-1, even though the round-robin proxy was demonstrably distributing requests evenly across both nodes.

The assistant had spent messages 854 through 892 debugging this discrepancy. They confirmed that both backends were healthy and configured. They added info-level logging to the round-robin selection logic, rebuilt the Docker image, restarted containers, and verified through logs that requests were indeed alternating between kuri-1 and kuri-2. The round-robin was working. The problem was in the monitoring layer, not the data layer.

The root cause was in the ClusterTopology method in rbstor/diag.go. This method, which the React frontend calls to render the cluster dashboard, only populated statistics for the local node — the node on which the RPC call was made. When the web UI (running on kuri-1's port 9010) called ClusterTopology, it received kuri-1's metrics with full detail, but kuri-2 appeared as a ghost node with zero values for storage used, groups count, and requests per second. The system had no mechanism to aggregate metrics across nodes.

The Decision: A Lightweight Stats Endpoint

The assistant considered several approaches to fix this. One option was to make the existing ClusterTopology RPC method issue WebSocket RPC calls to each remote node to fetch their stats. This would have been architecturally pure but complex — it would require the RPC system to bootstrap its own client connections, handle authentication, and manage concurrent requests.

Instead, the assistant chose a simpler, more pragmatic approach: create a dedicated lightweight HTTP endpoint (/api/stats) on each node that returns just that node's local statistics as a simple JSON object. Then, the ClusterTopology method could be updated to make HTTP GET requests to each remote node's /api/stats endpoint, aggregating the results. This design follows the Unix philosophy of doing one thing well: the stats endpoint is trivial to implement, trivial to test, and trivial to consume.

The implementation was straightforward. In integrations/web/ribsweb.go, the assistant added a StatsHandler method that reads the node's ID from the FGW_NODE_ID environment variable and calls the RIBS diagnostics interface to get groups count and storage used. The handler was registered on the HTTP mux as /api/stats, placed before the catch-all / handler. In Go's http.ServeMux, the most specific pattern wins regardless of registration order, so there was no risk of the root handler swallowing the stats route.

The Verification: Message 893

After editing the source files, the assistant rebuilt the Docker image with docker build -t fgw:local . and restarted the kuri-1, kuri-2, and s3-proxy containers with docker compose up -d --force-recreate. Then came message 893: the moment of truth.

The command is carefully constructed. The sleep 8 gives the containers time to initialize after the restart — the kuri node needs to connect to YugabyteDB, initialize its keyspace, and start its HTTP server. The docker compose exec kuri-1 runs the command inside the kuri-1 container, bypassing any port mapping or nginx routing. The wget -q -O- fetches the URL silently and writes the response to stdout. The target is http://localhost:9010/api/stats — the web UI server running inside the container on port 9010.

The response arrives as a single line of JSON:

{"nodeId":"kuri-1","groupsCount":1,"storageUsed":19446967194,"requestsPerSecond":0}

This is a triumph of minimalism. Four fields, each telling us something important:

Assumptions and Their Validity

This message rests on several assumptions, most of which are validated by the successful response:

  1. The container has finished restarting within 8 seconds. The sleep 8 is a heuristic — long enough for Docker to recreate the container and for the kuri daemon to start, but not guaranteed. If the database connection took longer, the endpoint might return an error or stale data. The successful response confirms the timing was adequate.
  2. The /api/stats route is correctly registered and not shadowed by the catch-all handler. This was a real concern — earlier in the session, the assistant had debugged a route conflict between HEAD / and GET /healthz in Go's ServeMux. The successful response confirms that the new route works as intended.
  3. The FGW_NODE_ID environment variable is set inside the container. The docker-compose configuration sets this for each kuri node, and the response confirms it was read correctly.
  4. The RIBS diagnostics interface returns meaningful values. The groupsCount and storageUsed fields come from the storage engine. If the database connection had failed or the keyspace was empty, these would be zero. The non-zero values confirm the storage layer is operational.
  5. The endpoint is accessible on localhost:9010 from within the container. The web UI server binds to the address specified by FGW_INTERNAL_API_BINDADDR, which is set to :9090 in the docker-compose. But port 9010 is the port exposed by the container — there's a mapping from the internal port (9090) to the published port (9010) happening somewhere, likely in the kuri daemon's startup logic. The successful response confirms this mapping is working.

What This Message Creates

Message 893 creates output knowledge in several forms:

  1. Verified functionality: The /api/stats endpoint is confirmed to work on kuri-1. This is the first piece of evidence that the new monitoring architecture is viable.
  2. A baseline for comparison: The response provides a snapshot of kuri-1's state at a specific point in time. When the assistant later tests the endpoint on kuri-2, they can compare the two responses to verify that each node reports its own independent state.
  3. Confidence to proceed: With the endpoint verified, the assistant can now implement the second phase — updating ClusterTopology to call this endpoint on each remote node and aggregate the results. Without this verification, any further work would be based on untested code.
  4. A debugging tool: The /api/stats endpoint is now available for ad-hoc inspection. If a node misbehaves in the future, a quick curl against this endpoint reveals its current state without needing to parse logs or navigate the web UI.

The Thinking Process

The assistant's reasoning in this message is concise but reveals a disciplined debugging methodology. The sleep 8 shows an understanding of container startup timing. The use of docker compose exec (rather than curling from the host) shows an understanding of Docker networking — the stats endpoint is bound to the container's internal port, and while port 9010 is published to the host, using exec eliminates any ambiguity about routing. The wget -q -O- flags show a preference for machine-readable output over human-friendly formatting.

The choice to test kuri-1 first is strategic. If the endpoint fails, the assistant can debug on a single node before rolling out to the cluster. If it succeeds, the same code path is shared by both nodes (same Docker image, same binary), so kuri-2 should work identically — barring environment-specific issues like missing environment variables.

The fact that the assistant doesn't immediately test kuri-2 is telling. The response confirms the endpoint works, but the requestsPerSecond: 0 field is a reminder that the full aggregation story isn't complete yet. The assistant's next steps would logically be: (1) verify the endpoint on kuri-2, (2) update ClusterTopology to call both endpoints and aggregate, (3) rebuild and redeploy, and (4) verify the UI shows live stats for both nodes.

Conclusion

Message 893 is a small but pivotal moment in a complex engineering session. It transforms a hypothesis ("we can expose per-node stats via a lightweight HTTP endpoint") into a verified fact. The JSON response is only 44 bytes, but it carries the weight of multiple design decisions, debugging iterations, and deployment steps. It is the quiet click of a puzzle piece locking into place — invisible to the end user, but essential to the architecture's integrity.