The Critical Restart: Applying Distributed Monitoring to a Horizontally Scalable S3 Cluster

The Message

[assistant] [bash] cd /home/theuser/gw/test-cluster && FGW_DATA_DIR=/data/fgw2 docker compose up -d --force-recreate kuri-1 kuri-2 s3-proxy 2>&1
time="2026-01-31T15:28:31+01:00" level=warning msg="No services to build"
 Container test-cluster-yugabyte-1 Running 
 Container test-cluster-kuri-2-1 Recreate 
 Container test-cluster-kuri-1-1 Recreate 
 Container test-cluster-kuri-1-1 Recreated 
 Container test-cluster-kuri-2-1 Recreated 
 Container test-cluster-s3-proxy-1 Recreate 
 Container test-cluster-s3-proxy-1 Recreated 
 Container test-cluster-yugabyte-1 Waiting 
 Container test-cluster-yugabyte-1 Healthy 
 Container test-cluster-db-in...

Introduction: A Single Command Carrying Architectural Weight

On the surface, message 892 of this coding session appears unremarkable—a routine Docker Compose restart command, executed after a rebuild, with the standard output of containers being recreated and dependencies waiting for health checks. Yet this message sits at a critical juncture in a much deeper debugging narrative. It represents the moment when a series of architectural fixes, spanning multiple files and subsystems, are finally applied to a live test cluster. The command docker compose up -d --force-recreate kuri-1 kuri-2 s3-proxy is not merely a restart; it is the deployment of a corrected monitoring architecture for a horizontally scalable S3 storage system, and the culmination of a debugging session that revealed a fundamental blind spot in how distributed systems report their own health.

To understand why this message was written, one must trace the reasoning that led to it. The assistant had been investigating a puzzling discrepancy: the cluster monitoring UI showed traffic only on kuri-1, even though the round-robin load balancing logic was confirmed to be distributing requests evenly across both kuri-1 and kuri-2. The S3 frontend proxy logs, once debug logging was enabled, showed alternating backend selections. But the web dashboard—the operator's window into the cluster—remained stubbornly silent about kuri-2's activity. This was not a load-balancing bug; it was a monitoring bug, and a subtle one at that.## The Context: A Distributed Monitoring Blind Spot

The debugging session that preceded message 892 had uncovered a critical architectural oversight. The ClusterTopology method in rbstor/diag.go was designed to report the state of the entire cluster, but it only populated metrics for the local node. When the web UI on kuri-1 called ClusterTopology, it received full, live statistics for kuri-1 and empty, zero-valued entries for kuri-2. The system was architecturally aware of kuri-2's existence—it appeared in the topology—but it had no mechanism to fetch kuri-2's actual operational metrics. The cluster was, in effect, blind to half of its own storage capacity.

This is a classic distributed systems problem: each node has perfect knowledge of itself but limited visibility into its peers. The assistant recognized that the solution required either a new lightweight RPC endpoint that each node could expose for peer-to-peer metric collection, or an aggregation layer that could query all nodes and combine results. The chosen approach was to add a /api/stats HTTP endpoint to each node's web server, returning local metrics as JSON, and then modify ClusterTopology to make HTTP calls to each remote node to fetch their stats.

The implementation touched several files. In integrations/web/ribsweb.go, a new StatsHandler was added to serve a JSON response with the node's current metrics. In rbstor/diag.go, the ClusterTopology method was extended to iterate over the list of backend nodes (parsed from the FGW_BACKEND_NODES environment variable), fetch each remote node's stats via HTTP, and merge them into the topology response. The node's identity was obtained from the FGW_NODE_ID environment variable rather than from the RIBS interface, which lacked a NodeID field. These were small, focused edits, but they fundamentally changed the system's self-awareness: the cluster could now see itself.

Why This Message Exists: The Deployment Step

Message 892 is the deployment step that makes those code changes real. The assistant had just finished rebuilding the Docker image with docker build -t fgw:local . (visible in the preceding messages). The --force-recreate flag ensures that even if the container configuration hasn't changed, the containers are destroyed and recreated from the new image. This is essential because Docker Compose, by default, will reuse existing containers if their configuration is unchanged, which would leave the old code running. The --force-recreate flag overrides this optimization, guaranteeing that the freshly built binary is loaded.

The choice to restart all three services—kuri-1, kuri-2, and s3-proxy—rather than just the kuri nodes is deliberate. The s3-proxy doesn't need the new /api/stats endpoint itself, but it's included in the restart to ensure a clean, consistent state across the entire stack. The YugabyteDB container is intentionally left untouched (it shows as "Running" and "Healthy" in the output), because database schema changes are not part of this deployment—only application-level monitoring logic is being updated.

Assumptions and Implicit Knowledge

This message makes several assumptions that are worth examining. First, it assumes that the Docker image has been successfully built with the correct tag (fgw:local). The preceding build output confirms this—the image was built with sha256 542637acf3efe17633dbd008c6759ff54cd610b692f7c94c05b098762e13c19c. But there is an implicit assumption that the build captured all the file changes correctly. The Go build process compiles the binary inside a multi-stage Docker build; if any file was not properly copied into the builder stage, the fix would be absent from the running container.

Second, the message assumes that the container recreation will proceed without errors. The output shows warnings about "No services to build" (expected, since the image was pre-built) and then the recreation sequence. The assistant does not verify that the containers actually started successfully after the command—there is no subsequent health check or log inspection in this message. The assumption is that docker compose up -d will either succeed or fail visibly, and the assistant will catch failures in subsequent steps.

Third, there is an assumption about network topology within the Docker Compose environment. The /api/stats endpoint that kuri-1 will call on kuri-2 relies on Docker's internal DNS resolution (container names as hostnames). The assistant assumes that http://kuri-2:9010/api/stats will resolve correctly from within the kuri-1 container. This is a reasonable assumption given Docker Compose's default networking, but it is not explicitly verified.## Mistakes and Incorrect Assumptions in the Chain

While message 892 itself is a straightforward command, it is the product of a debugging chain that contained several missteps worth analyzing. The most significant was the initial assumption that the round-robin load balancing was broken. The assistant spent several messages (860–873) adding debug logging, rebuilding the image, and restarting the proxy—only to discover that the round-robin was working perfectly. The real problem was in the monitoring layer, not the data plane. This is a classic debugging trap: when the UI shows incorrect data, the instinct is to suspect the data path, but sometimes the measurement instrument itself is flawed.

Another subtle mistake was in the initial implementation of the /api/stats endpoint. The assistant first tried to access ri.ribs.NodeID on the RIBS interface, but that field didn't exist—the RIBS interface has no NodeID method. The LSP (Language Server Protocol) errors caught this immediately, and the assistant corrected it by reading the node ID from the FGW_NODE_ID environment variable instead. This is a good example of how static analysis tools can catch type errors before runtime, but it also reveals an assumption that the RIBS interface would expose node identity—an assumption that was incorrect.

The assistant also initially imported encoding/json in ribsweb.go without using it, triggering another LSP error. This was a minor oversight from copying boilerplate, but it highlights the iterative nature of the development process: write, detect errors, fix, rebuild, deploy.

Input Knowledge Required

To understand this message fully, one needs knowledge of several domains. Docker Compose basics are essential: the -d flag for detached mode, --force-recreate for ensuring fresh containers, and the concept of service dependencies (YugabyteDB must be healthy before kuri nodes start). Knowledge of Go's http.ServeMux routing is needed to understand why the order of route registration doesn't matter (the most specific pattern wins, regardless of registration order). Understanding the S3 architecture—stateless frontend proxies routing to independent storage nodes—is necessary to grasp why monitoring aggregation is non-trivial. And familiarity with the project's environment variable conventions (FGW_BACKEND_NODES, FGW_NODE_ID, FGW_NODE_TYPE) is required to follow the configuration logic.

The message also assumes familiarity with the project's build pipeline: a multi-stage Docker build that compiles Go binaries in a builder stage and copies them to a minimal runtime image. The "No services to build" warning indicates that the image was pre-built externally, and Docker Compose is not attempting to rebuild it—a detail that matters for understanding the deployment flow.

Output Knowledge Created

This message, combined with the preceding edits, creates a cluster that can self-report its aggregate health. Before these changes, an operator looking at the web UI would see kuri-1's metrics and assume kuri-2 was idle or broken. After the deployment, the ClusterTopology RPC response includes live metrics from all storage nodes, fetched via HTTP from each node's /api/stats endpoint. The web UI can now display a true picture of cluster utilization.

More broadly, this message creates a template for how monitoring data flows in this architecture: the frontend proxy distributes requests via round-robin, each storage node records its own metrics locally, and the monitoring layer (currently served by kuri-1) aggregates data by querying peers. This is a pull-based monitoring model, as opposed to a push-based model where nodes would send metrics to a central collector. The choice has implications for scalability—pulling from N nodes adds O(N) HTTP requests per topology refresh—but it avoids the complexity of a separate metrics pipeline.

The Thinking Process Visible in the Reasoning

The assistant's reasoning, visible across the preceding messages, follows a clear pattern: observe a symptom, form a hypothesis, test it, refine. The initial symptom was "UI shows no traffic on kuri-2." Hypothesis 1: round-robin isn't working. Test: add debug logging, run test uploads, inspect logs. Result: round-robin works fine. Hypothesis 2: the monitoring code only reports local stats. Test: read the ClusterTopology implementation. Result: confirmed—remote nodes get zero values. Fix: add a stats endpoint and cross-node HTTP queries.

This is textbook debugging methodology, but it's worth noting the discipline involved. The assistant did not jump to the conclusion that the monitoring code was broken; it first verified the data path (round-robin) with concrete evidence (logs showing alternating backend selections). Only when the data path was proven correct did the assistant shift focus to the monitoring layer. This separation of concerns—data plane vs. control plane—is a hallmark of systematic debugging.

Conclusion: A Small Command, A Big Fix

Message 892 is, on its face, a mundane Docker Compose command. But it is the final step in a chain of reasoning that diagnosed and fixed a fundamental architectural gap: the inability of a distributed storage cluster to see its own distributed state. The restart of kuri-1, kuri-2, and s3-proxy with --force-recreate is the moment when code becomes behavior, when the fixes in ribsweb.go and diag.go transition from text files to live system properties. It is a reminder that in distributed systems, monitoring is not a separate concern bolted on after the fact—it is an architectural problem that must be designed alongside the data flow. A cluster that cannot accurately report its own health is, in a very real sense, not fully operational. This message marks the point at which the cluster gained the ability to see itself clearly.