The Moment of Truth: Debugging a Distributed S3 Cluster Deployment

Introduction

In the lifecycle of every complex software system, there comes a pivotal moment when architecture meets reality—when the carefully designed components, the meticulously crafted configuration files, and the thoughtfully planned deployment scripts all come together in an actual running instance. This moment is rarely graceful. More often, it is a cacophony of error messages, failed containers, and unexpected behaviors that collectively tell the story of what the developers thought they built versus what they actually built.

Message 408 in this opencode coding session captures exactly such a moment. It is a user-submitted terminal transcript showing the first real deployment attempt of a horizontally scalable S3 architecture built on top of Kuri storage nodes, YugabyteDB, and a stateless S3 frontend proxy. The transcript is raw, unvarnished, and dense with diagnostic information. It shows a cluster that partially starts, partially fails, and reveals at least four distinct problems that need to be addressed before the system can function as intended.

This article examines that single message in depth—not merely as a bug report, but as a rich artifact of software engineering practice. We will explore why the message was written, what assumptions it exposes, what knowledge it both consumes and produces, and how the thinking process embedded within it reveals the nature of distributed systems debugging. The message is a snapshot of a system in the process of being discovered, and there is much to learn from its contents.

The Message in Full

The user's message is a terminal session transcript spanning several commands and their outputs. It begins with the deployment command and its results, followed by log inspection, container status checking, and network port verification. Here is the complete message as submitted (with sensitive identifiers redacted):

theuser@biryani  ~/gw/test-cluster  pgf-port ● ? ↑2  ./start.sh /data/fgw2

========================================
FGW Test Cluster (2 Storage Nodes)
========================================

Data directory: /data/fgw2

Architecture:
  - kuri-1: S3 API (:8078) + Web UI (:9010)
  - kuri-2: Internal only (no exposed ports)
  - YugabyteDB: Shared metadata

✅ Docker image fgw:local exists

📁 Initializing data directories...
...
🚀 Starting test cluster...
WARN[0000] No services to build                         
[+] up 7/7
 ✔ Network test-cluster_fgw-test     Created                                                                                                                                                         0.0s 
 ✔ Container test-cluster-yugabyte-1 Healthy                                                                                                                                                         6.5s 
 ✔ Container test-cluster-db-init-1  Exited                                                                                                                                                         11.4s 
 ✔ Container test-cluster-kuri-2-1   Created                                                                                                                                                         0.0s 
 ✔ Container test-cluster-kuri-1-1   Created                                                                                                                                                         0.0s 
 ✔ Container test-cluster-webui-1    Created                                                                                                                                                         0.0s 
 ✔ Container test-cluster-s3-proxy-1 Created                                                                                                                                                         0.0s 

⏳ Waiting for services to start...
🔍 Checking YugabyteDB health...
✅ YugabyteDB is ready
🔍 Waiting for database initialization...
✅ Database initialized
🔍 Checking Kuri storage nodes...
✅ kuri-1 is running
❌ kuri-2 is not running. Check logs: docker-compose logs kuri-2

The script reports success for most services but flags kuri-2 as not running. The user then inspects the logs, revealing a cascade of errors across multiple components. The log output shows that kuri-2 failed with a YugabyteDB deadlock during migration, both kuri nodes reported a configuration validation error about "MinimunRetriveable count greater than MinimumReplica: 5 > 1", and the s3-proxy container failed with "invalid log level: info". The user then runs docker-compose ps which confirms only kuri-1 is running, and attempts to access the Web UI on port 9010 from a browser, which fails.

This is not a clean deployment. It is a messy, informative, and ultimately productive failure that reveals the distance between the architecture on paper and the architecture in practice.## Why This Message Was Written

To understand why this message exists, we must understand the context that produced it. The user—a developer working on the Filecoin Gateway project—had been engaged in an extended coding session with an AI assistant, building out a horizontally scalable S3 architecture. The architecture, as defined by the project's roadmap, follows a three-layer hierarchy:

  1. Stateless S3 Frontend Proxies (port 8078) that accept S3 API requests and route them to storage nodes
  2. Kuri Storage Nodes that hold the actual data, each with its own isolated keyspace in the database
  3. A shared YugabyteDB that provides coordination and object placement tracking The session had progressed through multiple phases: building the S3 frontend proxy binary, segregating per-node database keyspaces, updating Docker configurations, changing port mappings from 8443/8444 to 7001/7002, and updating the Filecoin chain API endpoint from the defunct api.chain.love to pac-l-gw.devtty.eu. Each of these changes was a logical step toward a working cluster. But there is a critical difference between writing code and running code. The assistant had been editing files, building binaries, and updating configurations, but until this moment, nobody had actually started the cluster and verified that everything worked together. The user's message represents the first integration test—the first time all the pieces were assembled into a running system. The message is, therefore, a reality check. It is the user saying, "I followed the instructions, I ran the deployment script, and here is what actually happened." It is a report from the field, submitted not as a formal bug report but as raw terminal output, trusting that the assistant (and the reader) can parse the diagnostic information and identify the problems. This is a common pattern in collaborative development: the architect or assistant designs the system, the implementer builds it, and the tester (often the same person in a different role) runs it and reports back. The message is the bridge between theory and practice.

The Architecture Under Test

To fully appreciate the diagnostic information in this message, we need to understand what the system is supposed to look like when it works correctly. The test cluster, as defined in docker-compose.yml and the supporting scripts, consists of several containers:

Problem 1: The Configuration Validation Error

Both kuri-1 and kuri-2 report the same error early in their startup sequence:

Configuration load failed: %w MinimunRetriveable count greater than MinimumReplica: 5 > 1

This error appears twice in each node's log output, suggesting that the configuration is loaded or validated at two different points in the startup process. The error message itself is revealing: it compares a "MinimunRetriveable" count (note the typo—it should be "MinimumRetrievable") against a "MinimumReplica" value. The configured value of 5 exceeds the configured value of 1, and the validation logic rejects this as an invalid configuration.

This is a configuration issue, not a code bug. The gen-config.sh script, which generates the settings.env files for each Kuri node, is producing values that fail validation. The fact that both nodes report the same error suggests that the configuration template or generation logic has a systemic issue—perhaps a default value that was never updated, or a misunderstanding of what these parameters mean.

The error is particularly interesting because it does not prevent kuri-1 from eventually starting successfully. Despite the configuration validation failure, kuri-1 proceeds through its startup sequence, generates a wallet, initializes its IPFS node, and reports itself as ready. This suggests that the configuration validation is either non-fatal (the node continues with fallback values) or that the error is logged but the node proceeds anyway. Kuri-2, on the other hand, never recovers from this and other errors.

Problem 2: The YugabyteDB Deadlock

Kuri-2's log contains a more serious error:

failed to build *rbstor.RbsDB: could not build arguments for function ...
create postgres migrations driver: failed to set migration lock in line 0: 
INSERT INTO migrations_locks (lock_id) VALUES ($1) (details: pq: deadlock detected 
(query layer retry isn't possible because data was already sent...

This is a database deadlock during migration. Both kuri-1 and kuri-2 are attempting to run database migrations simultaneously, and they are conflicting on the migrations_locks table. YugabyteDB, being a distributed SQL database, detects the deadlock and aborts one of the transactions.

The error message is particularly informative: "query layer retry isn't possible because data was already sent." This suggests that YugabyteDB's automatic retry mechanism cannot resolve the conflict because the transaction has already sent data to the client, making retry unsafe. The recommended fix—increasing the ysql_output_buffer_size gflag—is mentioned in the error itself, but the more practical fix for the test cluster is to ensure that the two Kuri nodes start sequentially rather than simultaneously.

This is a classic distributed systems problem: two independent processes both trying to initialize shared state. The migration system uses a lock table to prevent concurrent migrations, but the lock acquisition itself can deadlock when both processes attempt to insert lock records simultaneously. The solution is either to use a single initialization process (like the db-init container that already exists) or to stagger the startup of the Kuri nodes.

Problem 3: The S3 Proxy Configuration

The s3-proxy container reports:

Failed to load configuration: invalid log level: info

This is a simple but blocking error. The S3 proxy binary cannot start because it cannot parse its own log level configuration. The value "info" is being rejected as invalid.

This is almost certainly a configuration generation issue. The docker-compose.yml or the environment variables passed to the s3-proxy container specify a log level that the binary does not recognize. The most likely cause is a mismatch between the configuration system's expectations and the binary's implementation—perhaps the binary expects log levels like "INFO" (uppercase) or "debug"/"warn"/"error" (different naming convention), or perhaps there is a typo in the environment variable name that causes a different default to be used.

The fact that the s3-proxy fails to start at all is significant because it means the entire S3 API layer is unavailable. Even if the Kuri nodes are running, clients cannot interact with the system through the S3 API. This is the most visible failure from a user's perspective.

Problem 4: The Web UI Unavailability

The user's final command attempts to access the Web UI:

sudo ss -tupln | grep 9010
tcp   LISTEN 0      4096                       0.0.0.0:9010       0.0.0.0:*    users:(("docker-proxy",pid=1972008,fd=7))                          
tcp   LISTEN 0      4096                          [::]:9010          [::]:*    users:(("docker-proxy",pid=1972023,fd=7)) 
Can't open :9010 in the browser

The port is listening (Docker proxy is forwarding it), but the browser cannot open it. This could be because the Web UI server on kuri-1 is not actually serving HTTP on that port, or because the service is only listening on 127.0.0.1 inside the container (as suggested by the kuri-1 log line "RIBSWeb at http://127.0.0.1:9010"), making it inaccessible from outside.

This is a networking issue: the Web UI is bound to the container's loopback interface rather than to 0.0.0.0, so Docker's port forwarding cannot reach it. This is a common Docker networking pitfall—services that listen on localhost inside a container are not accessible from the host, even with port mapping.

The Interconnected Nature of the Failures

What makes this message particularly valuable as a diagnostic artifact is that the four problems are not independent. They interact in ways that complicate debugging:

  1. The configuration validation error affects both nodes but is only fatal for kuri-2, suggesting that kuri-2 encounters additional problems that push it over the edge.
  2. The database deadlock only affects kuri-2 because kuri-1 acquires the migration lock first. If the startup order were reversed, kuri-1 would fail instead.
  3. The s3-proxy failure is independent of the Kuri node issues, but it means that even if both Kuri nodes were running, the system would still not be usable through the S3 API.
  4. The Web UI issue is also independent but affects the developer's ability to monitor the cluster. A novice developer might fix these problems one at a time in isolation. An experienced developer recognizes that some of these problems share root causes (the configuration generation script needs fixing) while others are deployment timing issues (sequential startup) or networking configuration problems (binding to the correct interface). The message provides enough information to triage and prioritize these fixes.## Assumptions Exposed by the Message Every deployment failure reveals assumptions that were incorrect or incomplete. The user's message exposes several such assumptions that were baked into the system design and deployment scripts.

Assumption 1: Simultaneous Startup Is Safe

The deployment script starts all containers simultaneously using docker-compose up. This assumes that the Kuri nodes can initialize their database schemas concurrently without conflict. The deadlock error proves this assumption false. The database migration system uses a lock table, but the lock acquisition mechanism itself is vulnerable to deadlock when multiple processes attempt to initialize simultaneously.

This is a subtle issue. The migration system was designed with the assumption that migrations would be run by a single process (the db-init container), but the Kuri nodes also run migrations as part of their startup. The architecture has two independent migration paths—one explicit (db-init) and one implicit (Kuri node startup)—and they conflict.

Assumption 2: Configuration Defaults Are Valid

The configuration generation script produces settings with specific values for parameters like MinimumRetrievable and MinimumReplica. The script assumes that any values it generates will pass validation. The error "MinimunRetriveable count greater than MinimumReplica: 5 > 1" proves this assumption false.

The root cause is likely that the configuration template was written against an earlier version of the validation logic, or that the validation logic was added after the template was created. The assumption of stability in the configuration schema was violated.

Assumption 3: The Log Level Format Is Standard

The s3-proxy binary's configuration system assumes a specific format for log levels. The deployment scripts assume that "info" is a universally recognized log level. The mismatch between these assumptions causes the s3-proxy to fail at startup.

This is a classic integration problem: two components (the configuration library and the deployment script) have different expectations about a shared interface (the log level string). Neither is "wrong" in isolation, but together they produce a failure.

Assumption 4: Internal Services Are Accessible

The Web UI on kuri-1 binds to 127.0.0.1:9010, assuming that only local access is needed. But the deployment exposes port 9010 on the host, assuming that external access is desired. These assumptions conflict: the service is configured for local-only access, but the deployment expects it to be publicly accessible.

This is a configuration oversight rather than a design flaw. The gen-config.sh script or the Kuri node configuration needs to specify 0.0.0.0 as the bind address for the Web UI when running in Docker.

Assumption 5: The Cluster Can Be Debugged Through Logs Alone

The deployment script's error message says "❌ kuri-2 is not running. Check logs: docker-compose logs kuri-2." This assumes that the logs contain sufficient information to diagnose the failure. In this case, they do—the deadlock error is clearly visible. But this assumption is not always valid, and it places the burden of diagnosis on the human operator.

Input Knowledge Required to Understand This Message

To fully parse the information in this message, a reader needs substantial domain knowledge across several areas:

Docker and Docker Compose: Understanding container lifecycle, port mapping, network configuration, and the meaning of container states like "Created," "Exited," and "Healthy." The message uses Docker Compose extensively, and understanding the deployment requires familiarity with its output format.

YugabyteDB and Distributed SQL: Knowledge of distributed database concepts like deadlock detection, migration locks, query layer retry, and the ysql_output_buffer_size gflag. The deadlock error message references YugabyteDB-specific internals that would be opaque to someone unfamiliar with the platform.

Kuri and IPFS: Understanding what Kuri nodes do, how they use the RIBS plugin for S3 storage, and how they interact with IPFS for content addressing. The log mentions IPFS initialization, peer identity, swarm listening addresses, and the Kubo version—all IPFS-specific concepts.

S3 Protocol and Proxies: Knowledge of how S3 API requests are routed, how object placement works in a distributed storage system, and what role a stateless frontend proxy plays.

Go Programming and Dependency Injection: The error messages include stack traces through Go's dependency injection framework (using the fx library). Understanding the chain of "failed to build" messages requires familiarity with how Go's fx constructs objects through dependency graphs.

Filecoin and Blockchain Concepts: References to "gateway wallet," "market balance," "resolve address," and "actor not found" draw on Filecoin-specific terminology. The system interacts with the Filecoin blockchain for deal-making and storage proofs.

Configuration Management: Understanding how environment variables, settings files, and configuration validation work together. The error messages reference specific configuration parameters and their validated ranges.

This is a significant knowledge burden. The message assumes a reader who is either deeply familiar with the project or willing to invest time in understanding its components. In the context of the coding session, this is reasonable—the assistant has been working on this project for many messages and has context for all of these components. But for an external reader, the message would be largely incomprehensible without substantial background research.## Output Knowledge Created by This Message

Despite being a report of failure, this message creates substantial knowledge that advances the project. Every error message, every log line, every failed container adds to the collective understanding of the system.

A Complete Deployment Test

The most obvious output is a verified test of the deployment process. The start.sh script was executed, and its behavior was observed. We now know that:

Diagnostic Signatures

Each error in the logs is a diagnostic signature that can be recognized in future deployments. The "deadlock detected" message, the "MinimunRetriveable count greater than MinimumReplica" message, and the "invalid log level" message are now known failure patterns. When they appear again, developers will know what they mean and what to do about them.

This is how operational knowledge accumulates. The first time you see an error, it is mysterious. The second time, it is familiar. The third time, it is expected and you have a fix ready. This message is the first encounter with several important error signatures.

Timing and Ordering Information

The logs provide precise timing information about the startup sequence. We can see that:

Configuration Validation Feedback

The "MinimunRetriveable count greater than MinimumReplica: 5 > 1" error is direct feedback from the configuration validation system. It tells us exactly which parameters are invalid and what values they have. This is actionable information that can be used to fix the gen-config.sh script.

The error also reveals a potential issue with the validation error message format: "Configuration load failed: %w" suggests that the error message template includes a %w format specifier that is not being properly interpolated. This is a minor bug in the error reporting code that should be fixed.

Network Configuration Verification

The ss -tupln command output confirms that port 9010 is listening on the host, but the browser cannot connect. This is a specific diagnostic that narrows down the Web UI issue to either the service binding address or the application-level HTTP serving, rather than a Docker networking or port mapping problem.

The Thinking Process in the Reasoning

The message itself does not contain explicit reasoning—it is raw terminal output. But the reasoning is embedded in the sequence of commands the user chose to run. The user did not just run start.sh and report success or failure. They ran a deliberate diagnostic sequence:

  1. Run start.sh: Deploy the cluster and observe the overall result
  2. Run logs.sh: When kuri-2 fails, immediately inspect its logs
  3. Run docker-compose ps: Check the status of all containers to understand the full picture
  4. Run ss -tupln: When the Web UI is inaccessible, check network connectivity
  5. Attempt browser access: Confirm the Web UI is truly unreachable This sequence reveals a systematic debugging methodology. The user is not randomly poking at the system. They are following a logical progression: deploy, observe, diagnose, narrow down, confirm. Each command is chosen to answer a specific question raised by the previous command. The choice to run logs.sh for all services (not just kuri-2) is particularly telling. The user could have run docker-compose logs kuri-2 to see only the failed container's logs. Instead, they ran ./logs.sh /data/fgw2 which shows logs from all services. This suggests a holistic debugging approach—the user wants to understand the full system state, not just the obvious failure. The inclusion of the ss -tupln command (and the initial failed attempt without sudo) shows persistence and resourcefulness. The user tried the command without sudo, got a partial result (showing only non-root processes), then tried with sudo to see all processes including Docker proxy processes. When the sudo password was entered incorrectly twice, they tried a third time successfully. This is a small but human detail that speaks to the debugging process: it is rarely clean, often involves repeated attempts, and requires working around small obstacles. The final line—"Can't open :9010 in the browser"—is the most human element of the entire message. It is not a command output or a log line. It is the user's own observation, typed directly into the terminal or added as a comment. This is the moment when the user transitions from automated diagnostics to manual verification, and it is the final piece of evidence that something is wrong with the Web UI.## Mistakes and Incorrect Assumptions in the Message While the message is primarily a report of system failures, it also contains some mistakes and incorrect assumptions on the user's part. These are not failures of the user but rather natural aspects of the debugging process—every diagnosis is provisional, and every interpretation is subject to revision.

The "Probably Start Sequentially" Comment

At the end of the log output, there is a comment: "✅ Data placement constraint successfully verified probably start sequentially to avoid migration race." This appears to be the user's own annotation, added to the log output. The comment suggests that the user has already identified the deadlock issue and formulated a potential fix: starting the Kuri nodes sequentially rather than simultaneously.

This is a correct diagnosis of the symptom but an incomplete understanding of the root cause. Sequential startup would indeed avoid the deadlock in the test cluster, but it does not address the underlying architectural issue: the Kuri nodes should not be running database migrations at all if the db-init container is supposed to handle initialization. The real fix might be to ensure that Kuri nodes skip migration when the schema already exists, or to remove migration logic from the Kuri startup path entirely.

The comment also reveals an assumption that "start sequentially" is a sufficient fix. In a production environment with many nodes, sequential startup is not scalable. The proper fix would be to make the migration system robust to concurrent access, or to centralize migration in a single initialization process.

The Focus on Kuri-2

The user's diagnostic sequence focuses heavily on kuri-2's failure, which is natural because it is the most obvious problem (the deployment script explicitly flags it). But the s3-proxy failure is arguably more important—without the proxy, the entire S3 API is unavailable, regardless of how many Kuri nodes are running. The user does not investigate the s3-proxy failure in the same depth, perhaps because it is not flagged by the deployment script.

This is a common bias in debugging: we focus on the problems that are explicitly called out, even if other problems are more impactful. The deployment script's error message ("❌ kuri-2 is not running") directs attention to kuri-2, while the s3-proxy failure is only visible in the log output. A more experienced operator might check all container statuses first (which the user does with docker-compose ps) and then prioritize based on impact.

The Web UI Browser Test

The user's attempt to open the Web UI in a browser is a reasonable manual verification step, but the conclusion "Can't open :9010 in the browser" is ambiguous. It could mean:

The Message as a Communication Artifact

Beyond its technical content, this message is interesting as a communication artifact. It is a user submitting raw terminal output to an AI assistant, trusting that the assistant can parse and understand it. This is a specific form of communication that has emerged in the context of AI-assisted development: the user acts as an operator, running commands and collecting output, while the assistant acts as an analyst, interpreting the results and suggesting fixes.

This division of labor is efficient because it leverages the strengths of both parties. The user has access to the running system and can execute commands. The assistant has context about the entire codebase and can reason about root causes. Together, they form a debugging partnership.

The message format—raw terminal output with minimal annotation—is optimal for this partnership. It preserves all the information without interpretation or filtering. The user does not need to decide what is relevant or important; they just submit everything and let the assistant find the patterns. This is the opposite of a traditional bug report, which is typically curated and summarized.

The risk of this approach is information overload. The log output in this message contains hundreds of lines, many of which are routine startup messages. The assistant must filter out the noise and identify the signal. In this case, the signal is clear, but in more complex scenarios, the volume of data could overwhelm the analysis.

Conclusion

Message 408 is a snapshot of a distributed system in the process of being discovered. It captures the moment when architecture meets reality, when carefully designed components interact in unexpected ways, and when the gap between theory and practice becomes visible.

The message reveals four distinct problems—a configuration validation error, a database deadlock, an S3 proxy configuration failure, and a Web UI networking issue—each affecting a different component of the system. These problems expose incorrect assumptions about concurrent startup, configuration defaults, log level formats, and service accessibility. Understanding these problems requires substantial domain knowledge across Docker, YugabyteDB, IPFS, S3, Go, Filecoin, and configuration management.

But the message is more than a bug report. It is a record of a systematic debugging process, showing the user's methodology through the sequence of commands they chose to run. It is a communication artifact that demonstrates a specific form of AI-assisted development, where the user operates the system and the assistant analyzes the results. And it is a knowledge creation event, producing diagnostic signatures, timing information, and configuration feedback that will inform future development.

In the broader context of the coding session, this message is the turning point. Before it, the work was theoretical—writing code, updating configurations, building binaries. After it, the work becomes empirical—fixing real failures, addressing observed behaviors, and iterating toward a working system. The message is the bridge between these two modes, and it is rich with information for anyone who knows how to read it.

For the developer who submitted it, the message is a call for help: "I deployed the cluster, and this is what happened. What do I do next?" For the assistant who receives it, the message is a diagnostic challenge: "Parse these logs, identify the problems, and suggest fixes." And for the reader of this article, the message is a case study in distributed systems debugging, revealing the complexity, the frustration, and the satisfaction of making a complex system work.