The Moment the Cluster Became Observable: Debugging Cross-Node Metrics Aggregation

The Message

[assistant] Now update ClusterTopology to fetch remote stats:
[edit] /home/theuser/gw/rbstor/diag.go
Edit applied successfully.

At first glance, this message appears trivial—a single edit command, a success confirmation, barely twenty words. But this message is the culmination of a debugging chain spanning over thirty messages, representing the moment a distributed system finally became observable as a unified whole. It is the surgical strike that resolved a fundamental architectural blind spot in a horizontally scalable S3 storage cluster.

The Problem That Led Here

The story begins with a user observation that feels deceptively simple: "There is traffic visible on Frontend Proxies table, but all of it goes only to kuri-1, none to kuri-2." On the surface, this sounds like a routing failure—perhaps the round-robin load balancer was broken, or kuri-2 was unhealthy. The assistant dutifully followed this scent, adding debug logging to the S3 frontend proxy, rebuilding the Docker image, restarting containers, and generating test traffic. The logs confirmed something surprising: the round-robin was working. Traffic was being distributed evenly between kuri-1 and kuri-2, alternating with each PUT request. The cluster was functioning correctly at the data layer.

Yet the UI stubbornly showed zero activity on kuri-2. This discrepancy between operational reality and observability revealed a deeper problem: the monitoring system itself had a blind spot. The ClusterTopology RPC endpoint, which the web UI queried to render its cluster view, was only reporting metrics from the node it ran on. When the web UI called ClusterTopology on kuri-1, it received kuri-1's stats and zero-filled placeholders for kuri-2. The system was healthy but invisible.

The Root Cause: Local-Only Metrics

The assistant traced the issue to rbstor/diag.go, where the ClusterTopology() method iterated over configured backend nodes but only populated live statistics for the local node—the one matching selfNodeID. For every other node, it returned default zero values. This was not a bug in the traditional sense; the code compiled, ran without errors, and returned valid data structures. It was an architectural oversight in the observability design. The cluster topology system had been built with a single-node mindset, assuming that metrics would be collected centrally or that each node would only report itself. In a horizontally scaled system with independent Kuri storage nodes, this assumption was fatal to the monitoring UI's usefulness.

The assistant recognized that the fix required cross-node communication. Each Kuri node needed to fetch live metrics from its peers and aggregate them into a unified cluster view. The question was how to implement this efficiently without introducing tight coupling or heavyweight dependencies.## The Design Decision: HTTP Stats Endpoints vs. WebSocket RPC

The assistant faced a fork in the road. One approach was to reuse the existing WebSocket RPC infrastructure, making each node call RIBS.ClusterTopology or similar methods on remote peers. This would be architecturally pure—reusing the same JSON-RPC mechanism already in place for the web UI. But it was also complex: WebSocket connections require persistent state, handshake overhead, and careful connection management. The rbstor/diag.go code was a pure diagnostic function with no existing network client infrastructure.

The alternative, which the assistant chose, was to add a lightweight HTTP endpoint—/api/stats—to each node's web server and have ClusterTopology make simple HTTP GET requests to its peers. This was simpler, stateless, and easier to debug. The trade-off was introducing a second communication mechanism (plain HTTP alongside WebSocket RPC), but the simplicity won out. The assistant added the endpoint to integrations/web/ribsweb.go and then turned to modifying rbstor/diag.go to consume it.

The Subject Message in Context

The message "Now update ClusterTopology to fetch remote stats" followed by the edit command is the pivot point. Before this edit, ClusterTopology was a local-only function. After this edit, it would become a cluster-aware aggregator that reached out to peer nodes, collected their metrics over HTTP, and merged them into a single unified topology response.

This was not the first edit attempt. The assistant had already made two prior edits to ribsweb.go that triggered LSP errors—first an unused encoding/json import, then a reference to ri.ribs.NodeID that didn't exist on the RIBS interface. Each error was diagnosed and corrected: the unused import was removed, and NodeID was replaced with an environment variable lookup via os.Getenv("FGW_NODE_ID"). These stumbles are instructive. They show the assistant working iteratively, making small changes, running the LSP checker, and fixing type errors before proceeding. The edit to diag.go was the third in this chain, applying the lessons learned from the earlier attempts.

Assumptions and Their Consequences

The assistant made several assumptions during this debugging session. The first was that the round-robin was broken—a natural assumption given the symptom, but one that proved incorrect after logging was added. The second assumption was that adding info-level logging to the proxy would immediately surface in the container logs; this required also configuring the gw/s3frontend/backend logger in the Docker Compose environment variables, which wasn't done until message 870. The third assumption was that the log variable defined in backend_pool.go would be accessible in server.go (it was, since they share the s3frontend package), but the logger wasn't emitting messages until its log level was explicitly set.

The most significant assumption, however, was architectural: that ClusterTopology would be called on a node that already had access to all peers' metrics. This assumption was baked into the original implementation and only surfaced when the user observed the UI discrepancy. It's a classic distributed systems pitfall—assuming local state is global state.## The Thinking Process: A Diagnostic Chain

The assistant's reasoning in the messages leading up to this edit reveals a methodical debugging process. When the user reported that all traffic appeared to go to kuri-1 only, the assistant did not immediately assume the routing was broken. Instead, it followed a systematic chain:

  1. Verify the routing logic by reading backend_pool.go and confirming the round-robin implementation was correct.
  2. Check backend health by curling /healthz on both kuri-1 and kuri-2 from inside the s3-proxy container. Both returned "OK".
  3. Add instrumentation by inserting info-level logging at the round-robin selection point in server.go, then rebuilding and restarting.
  4. Generate test traffic with a loop of PUT requests to confirm distribution.
  5. Read the logs to discover that round-robin was indeed alternating between backends. At this point, the assistant had disproven its own initial hypothesis. The routing was fine. The problem was elsewhere. This is a textbook debugging pattern: form a hypothesis, add instrumentation to test it, gather evidence, and pivot when the evidence contradicts the hypothesis. The pivot led to examining the monitoring stack rather than the data path—a shift from "is the system working?" to "is the system observable?"

Input Knowledge Required

To fully understand this message, a reader needs familiarity with several concepts. First, the architecture of the system: there are stateless S3 frontend proxies that route requests to Kuri storage nodes, each of which has its own independent data store and metrics. Second, the monitoring architecture: the web UI queries a ClusterTopology RPC endpoint to render its cluster view, and this endpoint runs on a single node (kuri-1 in this case). Third, the Go programming language and its package system—understanding that log defined in backend_pool.go is accessible in server.go because they share the s3frontend package. Fourth, Docker Compose environment variable configuration and how log levels control output visibility. Fifth, the concept of LSP (Language Server Protocol) diagnostics and how they catch type errors during development.

Without this context, the message reads as a mundane edit confirmation. With it, the message becomes the resolution of a multi-layered debugging session that spanned routing logic, container configuration, logging infrastructure, and distributed metrics aggregation.

Output Knowledge Created

This edit produced a fundamental change in the system's observability. Before it, the ClusterTopology RPC returned an incomplete picture—only the local node's metrics were real, while all remote nodes appeared idle. After it, the monitoring UI could display accurate, live statistics from every Kuri storage node in the cluster. This transformed the web dashboard from a single-node monitoring tool into a genuine cluster observability platform.

The output knowledge also includes the pattern established for cross-node metrics collection. By adding a simple HTTP /api/stats endpoint and consuming it from ClusterTopology, the assistant created a lightweight, stateless aggregation pattern that could scale to any number of nodes. This pattern is reusable for future metrics, health checks, and diagnostic endpoints.

Mistakes and Lessons

The most instructive mistake in this chain was the initial assumption that the round-robin was broken. It was a reasonable hypothesis given the symptom, but it led the assistant down a path of adding logging, rebuilding containers, and generating test traffic—all of which ultimately confirmed that the routing was fine. The real issue was invisible until the assistant shifted perspective from "is data flowing?" to "is data being reported?" This is a common pitfall in distributed systems debugging: the monitoring system itself can be the source of the problem, and its failures often mimic data-path failures.

Another subtle mistake was the assumption that adding log.Info() calls would automatically produce visible output. In Go's go-log library used by this project, loggers are configured independently via environment variables. Adding a log statement in server.go using the log variable from backend_pool.go was correct in terms of Go package visibility, but the gw/s3frontend/backend logger wasn't included in the RIBS_LOGLEVEL environment variable. The assistant had to explicitly add it to the Docker Compose configuration before the log messages appeared. This is a reminder that logging infrastructure is not transparent—it requires explicit configuration at every layer of the stack.

Conclusion

The message "Now update ClusterTopology to fetch remote stats" is a turning point in the debugging session. It represents the moment when the assistant stopped looking at the data path and started fixing the observability path. The edit itself is small, but the reasoning behind it—the diagnostic chain, the design trade-off between WebSocket RPC and HTTP endpoints, the iterative error correction from earlier edits—is rich with lessons for distributed systems engineering. The cluster was working all along. What was broken was the ability to see it work.