The Invisible Node: Diagnosing a Distributed Metrics Blind Spot

The Message

[assistant] The issue is that to fetch stats from other nodes, we'd need to make WebSocket RPC calls to them. This is a more complex change. For now, a simpler approach is to add a new lightweight RPC endpoint that returns just the node's local stats, and then have ClusterTopology call this endpoint for each remote node via HTTP.

Let me create a simple HTTP endpoint that returns node stats:
[read] /home/theuser/gw/integrations/web/web.go

Context: A Cluster That Appears Half-Dead

The conversation leading up to this message reveals a frustrating debugging session. The user had deployed a horizontally scalable S3 architecture consisting of a stateless frontend proxy (the S3 API endpoint on port 8078) that routes requests to two backend Kuri storage nodes (kuri-1 and kuri-2). The architecture was designed so that the frontend proxy distributes write operations across backends using round-robin load balancing, ensuring both nodes share the workload.

After deploying the updated monitoring dashboard and generating test traffic, the user reported a puzzling observation: "There is traffic visible on Frontend Proxies table, but all of it goes only to kuri-1, none to kuri-2." From the user's perspective, the cluster appeared broken — one node was doing all the work while the other sat idle. This is precisely the kind of failure that undermines confidence in a horizontally scalable system.

The assistant immediately began investigating. It checked the round-robin logic in the backend pool code, confirmed that both backends were configured and healthy, added debug-level logging to track which backend was selected for each request, and even rebuilt and redeployed the container with the new logging. After generating fresh test traffic and inspecting the logs, the assistant made a critical discovery: the round-robin was working correctly. Traffic was being distributed evenly between kuri-1 and kuri-2, alternating as expected. The logs showed both backends receiving requests.

This meant the problem was not in the data path — the S3 proxy was correctly balancing writes — but in the observability path. The monitoring dashboard was only showing metrics from kuri-1, making kuri-2 appear inactive. The user's report was accurate as far as the UI was concerned, but the underlying cause was a monitoring blind spot, not a load-balancing failure.

The Root Cause: A Self-Centered Topology View

The assistant traced the issue to the ClusterTopology RPC implementation in rbstor/diag.go. This method is responsible for returning the complete cluster state — which storage nodes exist, their health status, request rates, storage usage, and other live metrics. The dashboard calls this endpoint to render the cluster topology view.

Reading the code, the assistant found the problem on lines 244–248 of diag.go: the ClusterTopology method only filled in live statistics for the local node — the node on which the RPC was invoked. For remote nodes, it populated the topology with their identifiers and addresses but left their metrics at zero values. The comment in the code confirmed this: "Add self as proxy if we have FGW_NODE_ID set." Only the local node's RequestsPerSecond was populated; remote nodes got empty stats.

This design flaw made sense in a single-node deployment — if there's only one Kuri node, its local metrics are the cluster's metrics. But in a multi-node cluster, each node is an island of visibility. When the web UI called ClusterTopology on kuri-1 (because that's where the web UI was connected), it received kuri-1's own metrics and zeroed-out entries for kuri-2. The dashboard faithfully rendered what it was given: one busy node and one apparently idle node.

The Design Decision: HTTP Over WebSocket RPC

Message 877 captures the moment when the assistant, having identified the root cause, evaluates possible solutions and makes a deliberate architectural decision. The message begins with a clear statement of the trade-off:

"The issue is that to fetch stats from other nodes, we'd need to make WebSocket RPC calls to them. This is a more complex change. For now, a simpler approach is to add a new lightweight RPC endpoint that returns just the node's local stats, and then have ClusterTopology call this endpoint for each remote node via HTTP."

This is a classic engineering decision: between the "correct" long-term solution and the pragmatic short-term fix. The ideal solution would be to have the ClusterTopology implementation make WebSocket RPC calls to each remote storage node, collect their metrics, and aggregate everything into a unified response. This would be architecturally clean — reusing the existing RPC framework, maintaining a single communication protocol, and keeping the aggregation logic centralized in one place.

However, the assistant correctly identifies that this approach carries significant complexity. WebSocket RPC calls require connection management, authentication, error handling for unreachable nodes, timeout handling, and careful serialization. The existing RPC system was designed for client-to-server communication, not server-to-server aggregation. Implementing this would mean building a mini-orchestration layer within the diagnostic endpoint, with all the attendant edge cases.

The alternative — a lightweight HTTP endpoint that each node exposes with its local stats — is simpler in several dimensions. HTTP is stateless, so there's no connection management. The endpoint can return a simple JSON response that's easy to parse. The ClusterTopology method can make parallel HTTP GET requests to all known remote nodes, collect their responses, and merge them with local data. Error handling is straightforward: if a node doesn't respond, it's marked as unhealthy.

The assistant's choice reflects a deep understanding of the system's architecture. The Kuri nodes already serve HTTP on their LocalWeb ports (7001, 7002) and the S3 proxy communicates with them via HTTP. Adding one more HTTP endpoint fits naturally into the existing communication patterns without introducing a new protocol or dependency.

Assumptions and Their Implications

The assistant makes several assumptions in this message, most of which are sound but worth examining.

First, the assistant assumes that a lightweight HTTP endpoint is genuinely simpler than WebSocket RPC. This is almost certainly correct for the immediate task. However, it introduces a second communication channel for metrics (HTTP alongside WebSocket RPC), which could lead to fragmentation over time. If future features need cross-node communication, they might replicate this pattern, leading to a proliferation of ad-hoc HTTP endpoints instead of a unified RPC layer.

Second, the assistant assumes that the HTTP endpoint can be added quickly and that the ClusterTopology method can be modified to call it. This is a reasonable assumption given the existing code structure — the integrations/web/web.go file already handles HTTP routing, and the rbstor/diag.go file contains the topology logic. The changes are localized and well-understood.

Third, the assistant assumes that the remote nodes' HTTP endpoints are reachable from the node serving the ClusterTopology RPC. In the test cluster, all containers are on the same Docker network, so this is trivially true. In a production deployment with nodes across different networks or data centers, this assumption might fail. The assistant implicitly acknowledges this by framing the solution as "for now" — a temporary measure that works within the current deployment topology.

The Input Knowledge Required

To fully understand this message, one needs familiarity with several layers of the system:

The Output Knowledge Created

This message creates several pieces of knowledge that shape the subsequent implementation:

  1. A diagnosis: The root cause is identified — ClusterTopology only reports local metrics.
  2. A design decision: HTTP endpoints are chosen over WebSocket RPC for cross-node metrics collection.
  3. A plan of action: The next step is to read integrations/web/web.go to understand the existing HTTP routing and determine where to add the new endpoint.
  4. An architectural precedent: Cross-node communication via simple HTTP endpoints is established as an acceptable pattern.

The Thinking Process Revealed

The reasoning visible in this message is a textbook example of systematic debugging. The assistant moved through several stages:

Observation: The user reports that traffic only appears on kuri-1 in the UI.

Hypothesis testing: The assistant suspects the round-robin load balancer might be broken. It adds debug logging, rebuilds, redeploys, generates test traffic, and inspects logs.

Surprise discovery: The logs show round-robin is working correctly — both nodes receive traffic. The problem is not in the data path.

Reframing: The assistant realizes the bug is in the observability path, not the data path. The UI is showing an incomplete picture.

Root cause analysis: Reading the ClusterTopology implementation reveals that it only populates metrics for the local node.

Solution evaluation: The assistant considers WebSocket RPC (complex, architecturally pure) versus HTTP endpoint (simple, pragmatic).

Decision: The HTTP approach is chosen, with the explicit acknowledgment that it's a simpler solution "for now."

This thinking process demonstrates a crucial skill in distributed systems debugging: distinguishing between problems in the system itself and problems in the observation of the system. The user's report was accurate about what the UI showed, but the root cause was a monitoring limitation, not a load-balancing failure. The assistant had to look past the symptom to find the true cause.

Conclusion

Message 877 is a pivotal moment in the debugging session. It represents the transition from investigation to implementation — from asking "what's wrong?" to asking "how do we fix it?" The assistant's decision to use a lightweight HTTP endpoint rather than WebSocket RPC reflects a pragmatic engineering philosophy: choose the simplest solution that works for the current deployment, acknowledge its limitations, and leave the door open for a more sophisticated approach when the architecture demands it. This message captures not just a technical decision, but a way of thinking about distributed systems debugging that prioritizes clear diagnosis, honest trade-off analysis, and incremental improvement.