The Deployment That Brought a Cluster to Life: Restarting Kuri Nodes After Implementing Real-Time Metrics

In a distributed systems development session focused on building a horizontally scalable S3 architecture, one seemingly mundane message marks a pivotal transition from theory to observation. Message 710 in the conversation is a single bash command and its truncated output:

cd /home/theuser/gw/test-cluster && FGW_DATA_DIR=/data/fgw2 docker compose up -d --force-recreate kuri-1 kuri-2 2>&1 | tail -20

 Container test-cluster-kuri-1-1 Recreate 
 Container test-cluster-kuri-2-1 Recreate 
 Container test-cluster-kuri-1-1 Recreated 
 Container test-cluster-kuri-2-1 Recreated 
 Container test-cluster-yugabyte-1 Waiting 
 Container test-cluster-yugabyte-1 Healthy 
 Container test-cluster-db-init-1 Starting 
 Container test-cluster-db-init-1 Started 
 Container test-cluster-db-init-1 Waiting 
 Container test-cluster-yugabyte-1 Waiting 
 Container test-cluster-db-init-1 Waiting 
 Container test-clust...

On its face, this is routine: restart two Docker containers in a test cluster. But in the full context of the session, this command represents the culmination of a significant debugging and implementation effort—the moment when newly written metrics collection code is deployed into a live cluster, transforming a dashboard that showed only empty arrays into one that can display real operational data. This article examines why this message was written, the decisions embedded within it, the assumptions it carries, and what it reveals about the development process for distributed infrastructure software.## The Context: From Empty Dashboards to Live Data

To understand why this restart command was issued, we must look at the state of the system moments earlier. The assistant had just verified that the cluster's RPC endpoints—RequestThroughput, LatencyDistribution, ErrorRates, ActiveRequests, and ClusterTopology—were returning data. However, the data was essentially empty: timestamps were empty arrays, throughput values were zero, error rates showed no nodes, and active requests were all zero. The ClusterTopology endpoint was the only one returning meaningful structural information, listing two healthy storage nodes (kuri-1 and kuri-2) and one proxy node, but with all metrics fields set to zero.

The user's message (msg 691) had explicitly demonstrated this problem by pasting the raw JSON-RPC responses, showing that metrics like "Timestamps":[], "Nodes":{}, and "Total":0 were the norm. The comment "still empty" at the end of that message made the pain point clear: the cluster monitoring dashboard existed, it was making the right RPC calls, but it had nothing interesting to display.

This triggered a focused implementation effort. The assistant identified that the metrics endpoints in rbstor/diag.go were still stub implementations—they returned the correct data structures but with empty or zeroed fields. The real work of tracking request throughput, latency distributions, error rates, and active connections had not been wired up. The assistant then created a new file, rbstor/cluster_metrics.go, which implemented a proper ClusterMetrics collector that tracks metrics with a rolling 10-minute window, recording request counts, bytes read/written, latency samples, error counts, and active connection counts. This collector was then integrated into the S3 server handlers in server/s3/server.go and the lifecycle hooks in server/s3/fx.go.

Message 710 is the deployment step: taking that newly compiled code (the Docker image built in msg 709 with sha256 ebfbc4114604...) and restarting the two Kuri storage nodes so they begin running the updated binary with live metrics collection enabled.## Why --force-recreate? A Deliberate Decision

The command uses --force-recreate, which tells Docker Compose to destroy and recreate the containers even if nothing about their configuration or image has changed. This is a deliberate choice worth examining. In a development workflow, one might use docker compose up -d (which only recreates containers if their configuration changed) or docker compose up -d --build (which rebuilds images before recreating). The assistant chose --force-recreate without --build because the Docker image had already been built in the previous step (msg 709). The --force-recreate flag ensures that the containers are stopped, removed, and started fresh, picking up the newly built image that was already tagged as fgw:local.

This decision reveals an assumption: that the image tag fgw:local would be resolved to the newly built image, not a cached older version. Docker Compose, by default, does not pull images if they exist locally, so --force-recreate combined with the pre-built image is the correct sequence. The assistant could have used docker compose up -d --force-recreate without the separate docker build step if they had included --build, but the two-step approach (build first, then restart) provides clearer separation—if the build fails, the restart never happens, and the developer can inspect build errors without the noise of container logs.

Targeting Only Kuri Nodes: Selective Restart

Another notable decision is the selective targeting of kuri-1 and kuri-2 rather than restarting the entire stack (docker compose up -d --force-recreate without service names). The test cluster at this point consisted of multiple services: yugabyte (the distributed SQL database), db-init (a one-shot initialization container), s3-proxy (the frontend proxy), webui (the Nginx reverse proxy), and the two kuri storage nodes. By specifying only kuri-1 and kuri-2, the assistant avoided restarting the database, the proxy, or the web UI—services that had not been modified and whose restart would add unnecessary downtime and potential disruption.

This selective restart reflects a mature understanding of the system architecture. The metrics collector was implemented in the Kuri storage node code (the rbstor package), not in the S3 proxy or the web UI. Restarting the database would have been wasteful (YugabyteDB had no code changes), and restarting the proxy would have been irrelevant (the proxy's metrics are collected separately via the ClusterTopology health checks). The assistant correctly identified the minimal set of containers that needed to be recycled to pick up the new code.

The Output: Reading Between the Lines

The truncated output shows the container lifecycle events. Docker Compose first recreates both Kuri containers, then reports them as "Recreated." It then shows the yugabyte-1 container being checked for readiness ("Waiting" then "Healthy"), followed by the db-init-1 container starting. This sequence reveals the dependency chain defined in the Docker Compose file: the Kuri nodes depend on the database being healthy, and the db-init container (which sets up the initial database schema) must run before the Kuri nodes can fully start.

The output is truncated at "Container test-clust..." which means the full startup sequence was cut off. We don't see the final "Started" status for the Kuri nodes. This truncation is a practical artifact of using tail -20—the assistant wanted to see only the tail end of the output to confirm the containers were recreated and the dependency chain was progressing, without scrolling through hundreds of lines of Docker Compose orchestration logs.## Potential Pitfalls and Missed Signals

While the restart command was successful, there are subtle risks worth noting. The use of tail -20 truncates the output, meaning the assistant never saw the final "Started" confirmation for the Kuri nodes. If one of the containers had failed to start (due to a configuration error, port conflict, or crash loop), the truncated output would have hidden that failure. The assistant would only discover the problem later when attempting to query the RPC endpoints. A more robust approach would have been to run the command without tail -20 and scroll through the output, or to follow up with docker compose ps to verify the containers' actual state.

Additionally, the command does not check whether the newly built image (fgw:local) was actually pulled by Docker Compose. If the image build had failed silently (e.g., the docker build command in msg 709 succeeded but produced an image with a different tag), the --force-recreate would have restarted the containers with the old image. The assistant's subsequent verification steps (testing the ClusterTopology endpoint with websocat) serve as a safety net, but a more defensive workflow might include explicit image tagging and verification before deployment.

Assumptions Embedded in the Restart Command

Every deployment command carries assumptions about the state of the system, and this one is no exception. The assistant assumed that the newly built Docker image (fgw:local, sha256 ebfbc4114604...) was correctly tagged and would be picked up by Docker Compose without explicit image versioning. In a more production-oriented workflow, one might use a unique tag (e.g., fgw:build-20260131-1438) to ensure the correct image is deployed, but in a development loop with a single test cluster, the mutable local tag is a pragmatic shortcut.

The assistant also assumed that the database schema changes (if any were needed for the new metrics tables) were already handled. The db-init container's appearance in the startup sequence suggests that the initialization script runs on every startup, but the metrics collector in cluster_metrics.go uses in-memory data structures with a rolling window, not persistent database tables. This was a correct architectural decision: metrics are ephemeral and node-local, stored in Go maps and slices rather than in YugabyteDB, avoiding the need for schema migrations.

Another assumption concerns the FGW_DATA_DIR=/data/fgw2 environment variable. This variable tells Docker Compose which data directory to use for configuration files and persistent storage. The assistant assumed that this directory still contained valid configuration files from the earlier gen-config.sh invocation (msg 673) and that the Kuri nodes would find their settings.env files at the expected paths. If the data directory had been corrupted or the configuration files had been edited manually, the restart could have failed silently.

Input Knowledge Required

To fully understand this message, a reader needs knowledge of several domains. First, familiarity with Docker Compose's lifecycle management—the difference between --force-recreate, --build, and plain up -d—is essential to grasp why this particular flag combination was chosen. Second, understanding the three-layer architecture of the system (S3 proxy → Kuri storage nodes → YugabyteDB) explains why only the Kuri nodes were restarted. Third, knowledge of Go's JSON-RPC implementation and the React frontend's polling mechanism helps connect the restart to the user's complaint about "still empty" metrics.

The reader also needs to understand the development workflow: the assistant is iterating in a tight loop of edit → build → deploy → verify. Message 710 is the "deploy" step in this cycle, preceded by code edits (msgs 693–707) and a Docker build (msg 709), and followed by verification steps (the subsequent messages show the assistant testing the ClusterTopology endpoint and checking that metrics are now populated).

Output Knowledge Created

This message produces several forms of knowledge. Most immediately, it confirms that the Docker Compose orchestration is functioning correctly—containers are being recreated, the database health check passes, and the initialization container runs. The truncated output also provides a partial dependency graph: the Kuri nodes wait for YugabyteDB to be healthy, and db-init runs after the database is ready.

At a higher level, the message documents a successful deployment of the metrics collection system. After this restart, the previously empty RPC responses should begin returning real data as S3 requests flow through the system. The subsequent messages in the conversation confirm this: the assistant uses websocat to query the ClusterTopology endpoint and sees populated fields like StorageUsed, GroupsCount, RequestsPerSecond, and IOThroughput data.

The Thinking Process Visible in the Reasoning

While message 710 itself contains no explicit reasoning (it is a straightforward bash command), the thinking process is visible in the sequence of messages leading up to it. The assistant's chain of reasoning can be reconstructed as:

Problem identification: The user showed that RPC endpoints return empty data (msg 691).
Root cause analysis: The assistant read rbstor/diag.go and confirmed the metrics endpoints were stubs (msg 692).
Solution design: The assistant created rbstor/cluster_metrics.go with a proper metrics collector (msg 693), then wired it into the S3 server handlers (msgs 700–704) and the lifecycle hooks (msgs 705–707).
Build verification: The Go code compiled successfully (msg 708) and the Docker image built (msg 709).
Deployment: Message 710—restart the Kuri nodes to activate the new code. This sequence demonstrates a systematic debugging approach: observe the symptom, locate the stub code, implement the real logic, integrate it at the appropriate layers, build, deploy, and verify. The selective restart of only Kuri nodes (not the proxy or database) shows an understanding of which components needed the new code.

Conclusion

Message 710 is a seemingly mundane deployment command that, in context, represents the inflection point where a monitoring dashboard transforms from a skeleton of empty data structures into a live observability tool. The command's details—--force-recreate, targeting only Kuri nodes, the truncated output showing dependency chains—reveal a developer's deep understanding of the system architecture and the deployment workflow. For the reader, this message is a case study in how distributed systems development proceeds: not through grand architectural pronouncements, but through a disciplined cycle of identifying empty responses, tracing them to stub implementations, writing real collection logic, and deploying with surgical precision. The restart of two Docker containers is, in this context, the moment the cluster truly comes to life.