The Verification That Confirms a System Is Alive: Analyzing a Single RPC Test in a Distributed S3 Architecture

The Message

echo '{"jsonrpc":"2.0","method":"RIBS.ErrorRates","params":[],"id":1}' | websocat ws://localhost:9010/rpc/v0 2>&1
{"id":1,"jsonrpc":"2.0","result":{"Nodes":{"local":{"ErrorRate":0,"ByType":{},"Trend":"stable"}}}}

This deceptively simple message—a single bash command piped through websocat to a WebSocket RPC endpoint—represents the culmination of a significant debugging and implementation cycle in a horizontally scalable S3 architecture built on the Filecoin Gateway. At first glance, it appears to be nothing more than a routine verification step: fire a JSON-RPC request, confirm the response looks reasonable. But beneath this surface lies a dense web of architectural decisions, debugging detective work, and the satisfaction of watching a system transition from "broken and empty" to "functioning and observable."

Context: The Long Road to a Working Cluster

To understand why this message matters, one must understand what preceded it. The assistant had been building a three-layer distributed S3 architecture: stateless S3 frontend proxies (port 8078) that route requests to Kuri storage nodes, which in turn store data in a shared YugabyteDB backend. The user had recently reported that every metrics endpoint in the cluster monitoring system was returning empty data. The RIBS.ErrorRates method, along with RequestThroughput, LatencyDistribution, ActiveRequests, and ClusterEvents, all returned hollow shells—structs with empty maps, zeroed counters, and no timestamps. The ClusterTopology endpoint at least returned node lists, but all the operational metrics that make monitoring meaningful were absent.

The user's message (index 691) had shown the raw JSON-RPC responses: {"Nodes":{}} for ErrorRates, empty timestamp arrays for throughput, empty latency distributions. The monitoring dashboard, if it existed at that point, would have displayed nothing but zeros and blank charts. A cluster that cannot be observed is, for operational purposes, a cluster that might as well not exist.

The assistant's response was not to patch a single file but to build an entirely new metrics subsystem. This meant creating rbstor/cluster_metrics.go—a dedicated metrics collector that tracks throughput, latency, error rates, active requests, and I/O bytes with a rolling 10-minute window. It meant modifying the S3 server handlers in server/s3/server.go to record every request and response. It meant wiring the new collector into the existing diagnostic RPC handlers in rbstor/diag.go. It meant adding a startup event so the system could report when nodes came online. And it meant rebuilding the Docker image, recreating the containers, uploading test objects, reading them back, and then—finally—testing each RPC endpoint one by one.

Why This Message Was Written: The Motivation of Verification

The message exists because the assistant was performing a systematic verification of every metrics endpoint after the implementation. The sequence of tests tells a story:

1. RequestThroughput was tested first (message 713), returning {"Timestamps":[1769867189],"Total":[0.5],"Reads":[0],"Writes":[0.5]}—real data with a real Unix timestamp and a measured throughput of 0.5 requests per second. The system was alive.
2. ClusterEvents was tested next (message 714), returning a node_started event with timestamp 1769867179 and the message "S3 server started." The system was not only alive but self-aware.
3. ActiveRequests was tested (message 715), returning zeros but with a properly structured response including ByProxy and ByStorage maps. The system was reporting accurately even when there was nothing to report.
4. ErrorRates was tested in the target message (716), returning {"Nodes":{"local":{"ErrorRate":0,"ByType":{},"Trend":"stable"}}}. The system was healthy. This ordering is deliberate. The assistant started with the most informative metric (throughput with timestamps) to get the quickest confirmation that the metrics pipeline was working end-to-end. Then moved to events, which validated the startup instrumentation. Then ActiveRequests, which validated the zero-state reporting. Finally ErrorRates, which validated the error tracking infrastructure even when no errors had occurred. The motivation was not mere curiosity. The assistant needed to confirm that the entire chain—from HTTP request recording in the S3 handler, through the rolling window aggregation in the metrics collector, to the JSON-RPC serialization and WebSocket delivery—was functioning correctly. A failure at any link in this chain would produce empty or malformed responses. Each successful test narrowed the possibility space of bugs.## How Decisions Were Made: The Architecture of the Metrics Collector The implementation choices visible in this message reveal several key decisions. The first is the choice of a rolling time window for metrics aggregation. Rather than storing every individual request event indefinitely—which would consume unbounded memory—the assistant implemented a collector that maintains a sliding window of data points, keeping only the most recent 10 minutes of activity. This is evident from the RequestThroughput response showing a single data point at timestamp 1769867189, which corresponds to the recent test uploads. The window-based approach trades perfect historical fidelity for bounded memory consumption, a pragmatic choice for a monitoring system that is meant to show "what is happening now" rather than serve as a permanent audit log. The second decision is the use of a global singleton metrics collector accessed through a package-level variable. The assistant's code in server/s3/server.go calls rbstor.GetClusterMetrics() to obtain the collector instance, then records request data on it. This design avoids the complexity of dependency injection through the existing Fx framework (which the project uses for lifecycle management) while still providing a single point of access. It is a pragmatic shortcut—one that works for a single-process S3 proxy but would need rethinking if the system ever scaled horizontally with multiple proxy instances sharing metrics. For the test cluster, it was the right trade-off. The third decision is the separation of concerns between rbstor/cluster_metrics.go (the data structure and aggregation logic) and rbstor/diag.go (the RPC handler that serves the data). The metrics collector is a standalone type that knows nothing about JSON-RPC or WebSocket transport. The diagnostic handlers are thin wrappers that call GetClusterMetrics().GetErrorRates() and serialize the result. This separation means the metrics system can be tested independently and could be reused for other purposes (e.g., a Prometheus exporter) without touching the RPC layer.

Assumptions Made and Their Implications

The assistant made several assumptions in this work. The most significant is that a single metrics collector instance, shared across all request handlers, is sufficient for the test cluster. This assumes that the S3 proxy runs as a single process and that all requests flow through the same collector. In the current architecture, this holds true: each Kuri storage node runs its own S3 proxy process, and each process has its own metrics collector. The ClusterTopology response confirms this by showing per-node metrics. But if the architecture evolved to run multiple proxy instances behind a load balancer, each instance would have its own partial view of metrics, and the cluster-level aggregation would need to be centralized.

Another assumption is that the Unix timestamp in the metrics data is meaningful for the client. The RequestThroughput response includes Timestamps:[1769867189], which is a Unix epoch timestamp. The assistant assumed the frontend would interpret this correctly, but the React components may need to convert it to a human-readable format or align it with the browser's local timezone. This is a minor detail but one that could cause confusion if the frontend displays raw epoch numbers.

The assistant also assumed that an empty ByType map in the ErrorRates response is acceptable. The response shows {"ErrorRate":0,"ByType":{},"Trend":"stable"}—the ByType map is empty because no errors have been recorded. This is technically correct but could be misleading if a frontend component expects keys like "5xx", "4xx", or "timeout" to always be present. A more robust design might pre-populate the map with zero-valued entries for known error types. However, for a system with zero errors, an empty map is semantically accurate.

Input Knowledge Required to Understand This Message

To fully grasp what this message means, a reader needs to understand several layers of context. First, the JSON-RPC protocol over WebSocket: the request is a standard JSON-RPC 2.0 call with method name RIBS.ErrorRates, empty params, and an ID of 1. The response follows the same protocol with a result field containing the data. The use of websocat as the client tool indicates that the RPC endpoint requires WebSocket transport rather than plain HTTP POST—a detail that caused earlier confusion when the assistant tried curl and received "Bad Request."

Second, the reader needs to know the project's naming conventions. RIBS is the name of the RPC subsystem (likely standing for "RPC Interface for Backend Services" or similar). The method names follow a pattern: ErrorRates, RequestThroughput, LatencyDistribution, ActiveRequests, ClusterTopology, ClusterEvents. Each corresponds to a monitoring dimension. The "local" key in the Nodes map refers to the local node—the one serving the RPC request—rather than a cluster-wide view.

Third, the reader needs to understand the architecture of the test cluster: port 9010 serves the web UI for kuri-1, port 9011 serves kuri-2's UI, and port 8078 is the S3 API proxy. The WebSocket RPC endpoint is at /rpc/v0 on the web UI ports. The assistant is querying kuri-1's RPC endpoint, which returns metrics from that node's perspective.

Output Knowledge Created by This Message

This message produces several pieces of knowledge. Most immediately, it confirms that the RIBS.ErrorRates RPC method is implemented and returning valid JSON. The response structure—a Nodes map containing a "local" key with ErrorRate, ByType, and Trend fields—defines the schema that frontend components must consume. Any React component that displays error rates must match this structure.

The message also establishes a baseline: at the time of the test, the cluster had zero errors. This is not just an absence of data; it is a positive signal that the S3 proxy is functioning correctly, that the Kuri storage nodes are reachable, that the database schema is intact, and that the request pipeline is not generating HTTP 5xx errors or timeouts. The Trend:"stable" field indicates that the error rate is not increasing, which is the expected state for a cluster with no load.

Furthermore, the message validates the entire metrics pipeline. The fact that ErrorRates returns a non-empty result means that the metrics collector was initialized, that it registered the error tracking structures, and that the RPC handler successfully serialized the data. This is a stronger signal than simply checking that the server is running—it confirms that the instrumentation code is executing correctly.## The Thinking Process Visible in the Verification Sequence

Although the target message itself contains no explicit reasoning—it is a bare bash command and its output—the thinking process is visible in the pattern of tests that surround it. The assistant did not test all endpoints simultaneously or in random order. The sequence reveals a deliberate, diagnostic mindset:

Test the richest signal first. RequestThroughput was the first endpoint tested because its response includes timestamps and numeric values—the most information-dense output. If this endpoint returned empty data, the metrics collector was fundamentally broken. If it returned data, the core pipeline was working.

Test lifecycle events second. ClusterEvents was tested next because it validates a different code path: the startup instrumentation that fires when the S3 server begins listening. This is a one-shot event, not a rolling metric. Its presence confirms that the event recording mechanism is wired correctly.

Test zero-state reporting third. ActiveRequests was tested after the event, even though it returned all zeros. This might seem pointless, but it validates that the data structure is properly initialized and serialized even when empty. A bug in the serialization code could cause a panic or an error response for zero-state data.

Test error tracking last. ErrorRates was the final test because it is the least likely to produce interesting data in a healthy cluster. The assistant saved it for last, knowing that if all other endpoints worked, this one almost certainly would too. Its successful response was the capstone, not the foundation.

This ordering reflects a principle of debugging: test the most informative, most likely-to-fail components first, and work toward the least informative, least likely-to-fail components last. It is a pattern that experienced engineers use instinctively, and it is visible here even though no explicit reasoning was recorded.

Potential Mistakes and Subtle Issues

While the message confirms a working system, several subtle issues deserve examination. The most notable is the use of "local" as the node identifier in the ErrorRates response. In a multi-node cluster, having each node report its errors under the key "local" creates ambiguity. If the frontend queries both kuri-1 and kuri-2 and receives {"local": {...}} from each, it cannot distinguish which node's error rate is which without additional context from the transport layer. The ClusterTopology response avoids this by using node IDs like "kuri-1" and "kuri-2" as identifiers. The inconsistency between the two RPC methods could cause confusion in the frontend code that aggregates data from multiple nodes.

Another subtle issue is the Trend field. The response shows "Trend":"stable", but this is computed from an error rate of zero with no historical data. Is a system with zero errors and zero history truly "stable"? The trend calculation logic in the metrics collector likely compares the current error rate to a previous window. If both are zero, "stable" is a reasonable classification. But if the trend calculation had a bug—for example, dividing by zero when there are no data points—the response could have been an error or a misleading value. The fact that it returned "stable" suggests the trend logic handles the zero-data edge case gracefully, but it also means the trend field is not providing actionable information until the system has been running long enough to generate meaningful comparisons.

The assistant also did not verify that the ErrorRates endpoint returns data for all nodes, not just the local one. The Nodes map in the response schema suggests it could contain multiple entries (one per node in the cluster), but the actual response contains only "local". This could mean the metrics collector only tracks local errors and has not implemented cross-node error aggregation, or it could mean the other nodes simply have no errors to report. The ClusterTopology endpoint, which was tested earlier, shows both kuri-1 and kuri-2 as storage nodes. If the ErrorRates endpoint is supposed to aggregate errors across the cluster, the absence of kuri-2's error data is a gap. If it is intentionally local-only, the schema is misleading.

Conclusion: The Weight of a Single Successful Response

The message {"id":1,"jsonrpc":"2.0","result":{"Nodes":{"local":{"ErrorRate":0,"ByType":{},"Trend":"stable"}}}} is, on its surface, a trivial confirmation that a system has no errors. But in the context of the development session, it represents the successful completion of a significant engineering effort. The assistant built a metrics subsystem from scratch, wired it into existing code, deployed it to a Docker-based test cluster, uploaded and retrieved test data, and then systematically verified every endpoint. The ErrorRates test was the final check in a sequence that confirmed the entire monitoring pipeline was operational.

For the reader, this message serves as a case study in the importance of verification in distributed systems development. A metrics system is not finished when the code compiles; it is finished when the RPC responses contain real data. The assistant's methodical approach—test the richest signal first, validate lifecycle events, confirm zero-state reporting, and finally check error tracking—is a template that any developer building observable systems can follow. And the successful response, however modest, is the reward: the system is not just running, it is reporting. It is alive, and it can be watched.