The Commit That Made Cluster Monitoring Real: How a Single /api/stats Endpoint Transformed Distributed Visibility
The Message
[assistant] [bash] cd /home/theuser/gw && git add integrations/web/ribsweb.go && git commit -m "web: add /api/stats endpoint for cluster-wide stats aggregation
Add HTTP endpoint that returns node's local statistics (nodeId,
groupsCount, storageUsed, requestsPerSecond) as JSON. This allows
ClusterTopology to fetch stats from remote nodes and display
accurate metrics for all storage nodes in the cluster, not just
the local node."
[pgf-port c10017b] web: add /api/stats endpoint for cluster-wide stats aggregation
1 file changed, 38 insertions(+)The Moment Before the Commit
At first glance, message 914 looks like a routine git commit—one file changed, thirty-eight lines inserted, a clean commit message. But this commit was the culmination of a debugging session that revealed a fundamental blind spot in the architecture of a horizontally scalable S3 storage system. The message captures the precise moment when the assistant recognized that a distributed system cannot be monitored from a single node's perspective, and built the bridge that made true cluster-wide observability possible.
To understand why this commit matters, we must trace the events that led to it. The assistant had been building a cluster monitoring dashboard for a distributed S3 storage system consisting of stateless frontend proxies (S3 frontend nodes) and Kuri storage backends. The architecture followed a three-layer hierarchy: S3 proxies on port 8078 routing to Kuri storage nodes (on ports 7001/7002), which in turn stored data in a shared YugabyteDB database. Each Kuri node ran its own web UI on port 9010 (for kuri-1) and 9011 (for kuri-2), served through an nginx reverse proxy.
The monitoring system had a ClusterTopology RPC method designed to return the state of all nodes in the cluster. But when the assistant tested it, the results were puzzling. From kuri-1's web UI at port 9010, the topology showed only kuri-2 as a proxy. From kuri-2's UI at port 9011, it showed only kuri-1. The data was incomplete and, worse, inconsistent between nodes.
The Debugging Trail
The assistant's investigation (visible in messages 893–906) followed a methodical path. First, it verified that the /api/stats endpoint worked locally by querying each container directly. Both returned valid JSON with node-specific data. Then it confirmed cross-node communication worked—kuri-1 could reach kuri-2's stats endpoint and vice versa. The round-robin load balancing was distributing traffic correctly, with both nodes showing requestsPerSecond: 0.5.
But the ClusterTopology response was wrong. The proxies list showed the other node instead of the local one. This led the assistant down a rabbit hole of checking environment variables (FGW_NODE_ID), configuration files, and nginx routing. Everything was configured correctly on paper. The breakthrough came when the assistant checked the raw /api/stats endpoint directly via curl on the host ports:
curl -s http://localhost:9010/api/stats | jq .
{"nodeId": "kuri-2", ...}
curl -s http://localhost:9011/api/stats | jq .
{"nodeId": "kuri-1", ...}The ports were swapped. Port 9010—supposed to serve kuri-1's UI—was returning kuri-2's data. The nginx configuration was correct, but the nginx container had stale DNS entries. After the Kuri containers had been recreated during the docker compose up -d --force-recreate cycle, their internal IP addresses changed. Nginx, which resolves container hostnames at connection time, was still pointing at the old addresses. A simple docker compose restart webui fixed it.
The Deeper Problem This Commit Solved
The port-swapping bug was a red herring, but it exposed a deeper architectural issue. The ClusterTopology function in rbstor/diag.go was designed to aggregate stats from all nodes, but it had no mechanism to actually fetch remote node data. Before this commit, the function returned zeros for any metric it couldn't compute locally. The monitoring dashboard showed storage nodes with storageUsed: 0, requestsPerSecond: 0, and no meaningful health data for remote peers. The system was distributed, but its observability was single-node.
The /api/stats endpoint was the missing link. By adding a lightweight HTTP handler that returned four key metrics—nodeId, groupsCount, storageUsed, and requestsPerSecond—as JSON, the assistant created a universal data source that any node could query from any other node. The ClusterTopology function could then iterate over the list of known backend nodes (configured via the FGW_BACKEND_NODES environment variable), fetch each node's stats via HTTP, and assemble a complete picture of the cluster.
Why This Approach Was Chosen
The assistant could have solved this problem in several ways. One alternative would have been to store cluster-wide metrics in the shared YugabyteDB database, making them available through CQL queries. Another would have been to implement a gossip protocol where nodes periodically exchange state. A third option would have been to extend the existing RPC framework to support remote procedure calls between nodes.
The HTTP endpoint approach was chosen for pragmatic reasons. It was the simplest thing that could work: a single HTTP handler, returning JSON, requiring no database schema changes, no new protocol definitions, and no additional infrastructure. The endpoint was lightweight—just four integer fields—and could be queried with a standard HTTP GET request. It reused the existing HTTP server infrastructure already running on each node for the web UI. Most importantly, it was immediately testable with curl or wget, as the assistant demonstrated repeatedly during debugging.
The commit message itself reveals the assistant's reasoning: "This allows ClusterTopology to fetch stats from remote nodes and display accurate metrics for all storage nodes in the cluster, not just the local node." The key insight is that the ClusterTopology function already had the logic to iterate over remote nodes; it just lacked a data source to query. The /api/stats endpoint filled that gap with minimal code—just 38 insertions.
Assumptions and Their Validity
The commit rests on several assumptions. First, that HTTP is an acceptable transport for inter-node monitoring data. In a production system, this might raise concerns about latency, reliability, and security. The assistant implicitly assumed that for a test cluster and development environment, HTTP was sufficient—an assumption validated by the successful cross-node queries during testing.
Second, the commit assumes that each node can reach every other node via HTTP. This depends on correct Docker networking and DNS resolution—the very thing that failed with the nginx port swap. The assistant discovered this assumption was fragile when the nginx container cached stale DNS entries. The fix (restarting nginx) was a workaround, not a solution, and the commit doesn't address the underlying DNS caching issue.
Third, the commit assumes that the four metrics returned by /api/stats are sufficient for cluster-wide monitoring. This was true for the immediate debugging needs, but the monitoring dashboard later expanded to include I/O throughput, latency distributions, error rates, and active request counts—metrics that required dedicated RPC methods and separate charts.
Input Knowledge Required
To understand this commit, a reader needs to know the architecture of the distributed S3 system: that there are multiple Kuri storage nodes, each with its own web UI, sharing a YugabyteDB database. They need to understand the role of the ClusterTopology function and why it previously returned incomplete data. They need to know that the FGW_BACKEND_NODES environment variable lists all cluster members, and that the web UI runs an HTTP server capable of serving custom endpoints. They also need to understand Docker Compose networking and the way container hostnames resolve to internal IP addresses.
Output Knowledge Created
This commit created a reusable pattern for inter-node data exchange. The /api/stats endpoint became the foundation for all cluster-wide monitoring. It demonstrated that lightweight HTTP endpoints could serve as effective data sources for distributed system observability, without requiring database queries or complex RPC frameworks. The commit also documented, through its message, the design rationale: that local-only metrics are insufficient for a distributed system, and that each node must be able to report its state to its peers.
The Thinking Process
The reasoning visible in this commit is a study in incremental problem-solving. The assistant didn't design a grand monitoring architecture upfront. Instead, it identified a specific failure—ClusterTopology returning zeros for remote nodes—and built the minimal infrastructure needed to fix it. The commit message is remarkably precise: it states the problem ("display accurate metrics for all storage nodes... not just the local node"), the solution ("HTTP endpoint that returns node's local statistics as JSON"), and the mechanism ("allows ClusterTopology to fetch stats from remote nodes").
What's not visible in the commit message, but evident from the surrounding conversation, is the debugging process that led to this solution. The assistant spent several messages investigating why the monitoring data was wrong, checking environment variables, configuration files, container logs, and DNS resolution. The port-swapping bug was a distraction that nearly derailed the investigation. But the assistant persisted, methodically ruling out each potential cause until the true problem emerged: not a configuration error, but an architectural gap.
This commit represents the moment when the assistant shifted from debugging a symptom (wrong data in the UI) to fixing a root cause (no mechanism for cross-node data exchange). It's a small commit—38 lines in a single file—but it transformed the cluster monitoring system from a single-node view into a truly distributed observability platform. In the history of the project, this is the commit that made cluster monitoring real.