Verifying Latency Distribution in a Distributed S3 Architecture: A Moment of Validation

The Message

The subject of this article is a single message from an AI assistant working on a horizontally scalable S3-compatible storage system. The message consists of a shell command and its JSON response:

[bash] echo '{"jsonrpc":"2.0","method":"RIBS.LatencyDistribution","params":["5m"],"id":1}' | websocat -n1 ws://localhost:9010/rpc/v0 2>&1 | jq '.'
{
  "id": 1,
  "jsonrpc": "2.0",
  "result": {
    "timestamps": [
      1769869192,
      1769869203,
      1769869213
    ],
    "p50": [
      0,
      4,
      0
    ],
    "p95": [
      0,
      15,
      0
    ],
    "p99": [
      0,
      15,
      0
    ],
    "byOperation": {}
  }
}

At first glance, this appears to be a routine API test — send a JSON-RPC request, get a JSON response, pipe it through jq for pretty-printing. But in the context of a multi-hour engineering session building a horizontally scalable S3 architecture with real-time cluster monitoring, this message represents a critical verification milestone. It is the moment when the assistant confirms that the latency monitoring infrastructure, which required changes across at least six source files spanning backend metrics collection, RPC wiring, and frontend visualization, is actually producing live data from real operations.

Context: What Led to This Message

To understand why this message was written, one must understand the architecture being built. The project is a horizontally scalable S3-compatible storage system for a Filecoin Gateway. The architecture follows a three-layer design: stateless S3 frontend proxies (port 8078) route requests to independent Kuri storage nodes (ports 7001, 7002), which in turn share object routing metadata via a YugabyteDB cluster. Each Kuri node has its own segregated database keyspace (filecoingw_kuri1, filecoingw_kuri2) while sharing a common S3 keyspace (filecoingw_s3) for object location metadata.

The assistant had just completed a major iteration of the cluster monitoring system. This involved modifying the backend metrics collection in rbstor/cluster_metrics.go, adding JSON serialization tags to all monitoring structs in iface/iface_ribs.go, implementing an IOThroughput() method in the RBSDiag interface, wiring up new RPC endpoints in integrations/web/rpc.go, and building React frontend components including a LatencyDistributionChart component. The latency chart had been changed from displaying "SLA" to "SLO" with a 350ms threshold — a deliberate design decision to reflect the difference between a Service Level Agreement (contractual) and a Service Level Objective (internal target).

The immediate preceding steps involved rebuilding the Docker image, restarting the test cluster, debugging a missing container (kuri-2 had failed due to a configuration validation error: RetrievableRepairThreshold greater than MinimumReplicaCount: 3 > 1), verifying all containers were healthy, generating test traffic (10 PUT operations and 10 GET operations via curl), and confirming that the IOThroughput RPC endpoint was recording bytes. The ClusterTopology endpoint had already been verified, showing both storage nodes with live statistics including storage used, groups count, and requests per second.

Why This Specific Query Was Made

The LatencyDistribution query was the final piece of the verification puzzle. The assistant had already confirmed that:

  1. The Docker image built successfully
  2. All containers were running (kuri-1, kuri-2, s3-proxy, yugabyte, db-init, webui)
  3. The S3 health endpoint responded (curl -s http://localhost:8078/healthz returned "ok")
  4. The web UI served HTML
  5. The IOThroughput endpoint returned data showing 5,120 bytes read and 5,120 bytes written across two time intervals
  6. The ClusterTopology endpoint returned both proxy and storage node information What remained was to verify that the latency distribution tracking was operational. This is a more nuanced metric than simple throughput — it requires the system to track request latency across multiple percentile buckets (p50, p95, p99) over time windows. If this endpoint returned empty data or an error, it would indicate a gap in the metrics collection pipeline, possibly in the responseRecorder middleware in server/s3/server.go or in the metrics aggregation logic.

The Response: What It Reveals

The response contains three timestamps (Unix epoch times 1769869192, 1769869203, 1769869213 — approximately 11-second intervals), with corresponding p50, p95, and p99 latency values. The first timestamp shows all zeros (no requests recorded yet). The second timestamp shows p50=4ms, p95=15ms, p99=15ms. The third timestamp shows zeros again, likely because the metrics window had advanced and no new requests fell into that bucket.

The byOperation field is empty ({}), indicating that latency is not being broken down by S3 operation type (GET, PUT, DELETE, etc.) — this is likely a future enhancement rather than a bug, as the basic infrastructure is working.

The latency values themselves (4ms median, 15ms at the 99th percentile) are reasonable for a test cluster running on localhost with YugabyteDB and two Kuri nodes. The fact that p95 and p99 are identical (15ms) suggests that the sample size was small enough that the 95th and 99th percentiles fell into the same bucket, or that latency was tightly clustered.

Assumptions Made

Several assumptions underpin this message. The assistant assumes that the RIBS.LatencyDistribution RPC method is correctly registered in the JSON-RPC handler and that the method name matches exactly what the frontend will call. It assumes that the websocat tool is available and that the WebSocket connection to ws://localhost:9010/rpc/v0 will succeed (the web UI nginx proxy must be routing RPC requests correctly). It assumes that the test traffic generated moments earlier (10 PUTs and 10 GETs) is sufficient to populate latency metrics — an assumption validated by the non-zero values in the response.

The assistant also assumes that the latency data is being collected in real-time and that the "5m" parameter (indicating a 5-minute window) is correctly interpreted by the backend. The timestamp values suggest the backend is using Unix epoch timestamps, which the frontend chart component must be able to parse and display.

Potential Issues and Observations

One notable observation is that the byOperation field is empty. The struct definition in iface/iface_ribs.go likely includes a field for per-operation latency breakdown, but either the backend isn't populating it or the test traffic didn't trigger operation-specific tracking. This could be a deliberate simplification — per-operation latency tracking adds complexity to the responseRecorder middleware and may be planned for a future iteration.

Another observation is the gap in timestamps: 1769869192 to 1769869203 (11 seconds), then 1769869203 to 1769869213 (10 seconds). The metrics collection appears to sample at roughly 10-second intervals, which is reasonable for a real-time dashboard but worth noting for understanding the refresh behavior.

The zero values in the third timestamp bucket are interesting. They could indicate that the metrics window is rolling and the third bucket captured a period with no new requests, or that the metrics aggregation logic resets counters after reporting. Understanding this behavior is important for the frontend chart to avoid misleading "dips" in the visualization.

Input Knowledge Required

To fully understand this message, one needs knowledge of: JSON-RPC protocol conventions (method, params, id fields), WebSocket communication for real-time APIs, Unix epoch timestamps, percentile-based latency metrics (p50, p95, p99), the jq JSON processor, and the overall architecture of the S3 system being built. One also needs to understand that the "5m" parameter requests a 5-minute lookback window for metrics.

Output Knowledge Created

This message creates verified knowledge that the latency distribution tracking pipeline is functional end-to-end. The backend metrics collection in rbstor/cluster_metrics.go is capturing request latencies. The responseRecorder in server/s3/server.go is recording timing data. The RPC wiring in integrations/web/rpc.go is correctly exposing the data. The data format matches what the frontend LatencyDistributionChart React component expects. This verification de-risks the frontend integration — when the web UI is loaded, the latency chart should display meaningful data rather than an empty or error state.

The Thinking Process

The assistant's reasoning is visible in the sequence of verification steps leading to this message. Rather than testing all endpoints simultaneously, the assistant follows a dependency chain: infrastructure first (containers running), then basic health (HTTP healthz endpoint), then throughput metrics (IOThroughput), then topology (ClusterTopology), and finally latency distribution (LatencyDistribution). This is a textbook debugging and verification strategy — start with the most basic assumption (is the service running?) and progressively validate more complex functionality.

The choice to use websocat with a raw JSON-RPC request rather than relying on the web UI to display the chart is deliberate. Testing at the RPC layer isolates the backend from any frontend rendering issues. If the RPC returns data, the frontend chart should work (assuming the React component correctly parses the response). If the RPC failed, the assistant would know the issue is in the backend or RPC wiring, not in the JavaScript.

The use of jq '.' to pretty-print the JSON response is a small but telling detail. The assistant wants to read the full response structure clearly, not just confirm it's non-empty. This suggests the assistant is checking the shape of the data — ensuring field names match what the frontend expects, that timestamps are present, and that the data types are correct.

Conclusion

This single message, while small in isolation, represents the culmination of a complex engineering effort spanning multiple source files, a Docker-based test infrastructure, and a real-time monitoring dashboard. It is the moment when the assistant confirms that the latency measurement infrastructure — from the responseRecorder middleware intercepting HTTP requests, through the metrics aggregation in cluster_metrics.go, to the RPC serialization and WebSocket delivery — is producing live, meaningful data. For a distributed systems engineer, this is the satisfying click of the last piece fitting into place.