The Anatomy of a Debugging Moment: Tracing Dependency Failures in a Distributed S3 Cluster

The Message

[assistant] [bash] docker logs test-cluster-kuri-1-1 2>&1 | tail -15
2026-01-31T16:01:42.540Z	ERROR	core	core/builder.go:158	constructing the node: could not build arguments for function "github.com/CIDgravity/filecoin-gateway/server/s3".StartS3Server (/app/server/s3/fx.go:89): failed to build *s3.S3Server: could not build arguments for function "github.com/CIDgravity/filecoin-gateway/integrations/kuri/ribsplugin/s3".MakeS3Server (/app/integrations/kuri/ribsplugin/s3/fx.go:41): failed to build iface.S3ObjectIndex: could not build arguments for function "github.co...

At first glance, this is a straightforward debugging command: the assistant runs docker logs to inspect why a container failed to start. But this single message sits at a critical inflection point in a much larger debugging session, and the error it reveals tells a story about architecture, dependency management, and the nature of distributed system failures.

Context: A Cluster in Crisis

To understand why this message was written, we must step back. The assistant has been building and debugging a test cluster for a horizontally scalable S3 architecture built on top of IPFS and YugabyteDB. The architecture follows a three-layer design: stateless S3 frontend proxies receive client requests and route them to Kuri storage nodes, which in turn store metadata in a shared YugabyteDB instance. The session leading up to this message has been a rollercoaster of configuration fixes, network mode changes, and database migration repairs.

Immediately before this message, the assistant had made a significant decision: revert from host network mode back to bridge networking. The host network experiment had caused cascading port conflicts—the IPFS gateway defaulting to port 8080 clashed with existing services on the host, and the S3 proxy ports interfered with other running containers. After reverting, the assistant cleaned the data directories (/data/fgw2/kuri-1/*, /data/fgw2/kuri-2/*, /data/fgw2/yugabyte/*), regenerated configuration files, and recreated the Docker containers with --force-recreate. The expectation was that a clean slate would resolve the persistent startup failures.

But when the assistant ran docker ps | grep test-cluster in message 1339, only the YugabyteDB container appeared. The Kuri storage nodes and the S3 proxy were absent. Something was still broken.

Why This Message Was Written: The Debugging Imperative

The message exists because of a fundamental debugging principle: when a container fails to start, the first place to look is its logs. The assistant had just recreated the containers with fresh configurations and clean data directories, yet they weren't running. The docker ps output showed only YugabyteDB—healthy, as always—but kuri-1, kuri-2, and s3-proxy were missing. The natural next step was to inspect the logs of one of the failed containers to understand what went wrong.

The choice of tail -15 is deliberate: the assistant wants the last 15 lines of the log, which typically contain the most recent and relevant error information. The 2>&1 redirect ensures stderr is captured alongside stdout, since container startup errors often appear on stderr. This is standard operational practice, but it reflects an assumption that the error will be visible near the end of the log output—an assumption that holds true here.

The Error: A Chain of Failed Dependencies

The log output reveals a deeply nested error from what appears to be a dependency injection framework (likely Uber's Fx library, given the fx.go file references). The error chain reads like a stack trace of construction failures:

1. Top level: constructing the node: could not build arguments for function "StartS3Server" — The node (the Kuri application) failed to construct because the S3 server component couldn't be built.
2. Second level: failed to build *s3.S3Server: could not build arguments for function "MakeS3Server" — The S3 server itself depends on a factory function in the ribsplugin/s3 package.
3. Third level: failed to build iface.S3ObjectIndex: could not build arguments for function "github.co..." — The S3ObjectIndex interface implementation failed to construct, and the error is truncated here. This is a classic dependency injection failure cascade. The framework attempts to construct each component by resolving its dependencies recursively. When one dependency at the bottom of the tree fails, every component that depends on it also fails, producing this layered error message. The truncated final line is particularly frustrating—it cuts off the actual root cause, leaving the assistant (and the reader) with an incomplete picture.

Assumptions and Their Consequences

The assistant made several assumptions in this moment, some explicit and some implicit:

Assumption 1: The data directory cleanup was sufficient. The assistant cleaned the kuri data directories and the yugabyte data directory before regenerating configs. However, as the very next message (1341) reveals, the YugabyteDB container was not actually cleaned—the database still contained a dirty migration flag from a previous run. The docker run --rm -v /data/fgw2:/data alpine sh -c 'rm -rf /data/yugabyte/* /data/kuri-1/* /data/kuri-2/*' command in message 1323 should have cleaned the yugabyte directory, but the migration state persisted. This suggests either the cleanup command didn't execute as expected, or the YugabyteDB container had already written data that wasn't in the volume path being cleaned.

Assumption 2: The error in the logs would directly indicate the root cause. The dependency injection error is a symptom, not the cause. The actual root cause—a dirty database migration—manifests as a failure to build the S3 object index because the database schema is in an inconsistent state. The DI framework faithfully reports the construction failure, but the error message doesn't say "database migration is dirty." It says "could not build arguments for function X." The assistant must infer the connection between these two levels of abstraction.

Assumption 3: Bridge network mode would resolve the startup issues. The revert from host networking was motivated by port conflicts, but the actual problem preventing container startup was unrelated to networking. This is a common debugging trap: fixing one visible symptom (port conflicts) while the underlying problem (dirty migration) remains hidden.

Input Knowledge Required

To understand this message, the reader needs knowledge of:

• Docker container lifecycle: Understanding that docker logs shows output from a container that may have exited, and that docker ps only shows running containers.
• Dependency injection patterns: Recognizing that the nested error format comes from a DI framework that recursively resolves constructor arguments. Each "could not build arguments for function" line represents one level of dependency resolution.
• The architecture being built: Knowing that Kuri nodes have an S3 server component that depends on an S3ObjectIndex, which in turn depends on database state. Without this context, the error chain looks like abstract Go code rather than a specific database connectivity issue.
• The session history: Understanding that the cluster has been through multiple configuration iterations, that database migrations have been a recurring problem, and that the assistant has been fighting dirty migration flags for several messages.

Output Knowledge Created

This message produces several valuable pieces of knowledge:

1. The container is failing during node construction, not at runtime. The error occurs in core/builder.go:158, which is part of the application initialization phase. This means the Kuri binary starts, begins constructing its component graph, and fails before serving any requests.
2. The failure is in the S3 subsystem, not the IPFS or storage subsystem. The error chain specifically mentions StartS3Server, MakeS3Server, and S3ObjectIndex. The IPFS node initialization appears to succeed (as seen in earlier log snippets), but the S3 integration layer fails.
3. The error is truncated. The final line cuts off mid-function-path ("github.co...), which means the complete error message is longer than 15 lines. The assistant may need to fetch more log lines to see the full picture—though in this case, the next message reveals the actual cause without needing the full trace.
4. The DI framework is being used extensively. The presence of fx.go files and the build arguments for function error pattern indicates that the Kuri application uses a dependency injection framework to wire together its components. This is a design choice that affects how errors manifest and how they must be debugged.

The Thinking Process: What the Assistant Was Reasoning

The assistant's thinking process, visible through the sequence of commands, follows a logical debugging progression:

1. Observe symptom: Containers not running after recreate (message 1339).
2. Inspect logs: Check kuri-1's logs for error messages (this message).
3. Identify error pattern: Recognize the DI construction failure.
4. Connect to prior knowledge: The assistant has been fighting dirty migrations throughout this session. In messages 1301-1308, they discovered and fixed dirty migration flags in filecoingw_kuri1, filecoingw_kuri2, and filecoingw_s3 keyspaces. The pattern of "container fails to start → check migrations → fix dirty flag → restart" has been repeated multiple times. The assistant's next action (message 1341) confirms this pattern: "Same dirty migration issue. The YugabyteDB wasn't cleaned - only the kuri data directories were." This shows that the assistant immediately connected the DI construction failure to the dirty migration problem, even though the error message itself doesn't mention migrations.

Mistakes and Lessons

The primary mistake visible in this message is not the error itself, but the incomplete cleanup. The assistant cleaned the kuri data directories and the yugabyte data directory, but the database container (which was still running from a previous session) retained its state. When the new kuri containers tried to initialize, they connected to the existing YugabyteDB instance and found the dirty migration flag from the previous schema version.

This is a classic state management problem in distributed systems: when you have multiple stateful services (YugabyteDB, Kuri nodes, IPFS repos), cleaning one without cleaning the others leaves inconsistent state. The assistant learned this lesson the hard way, and the next message shows them fixing the migration directly in the database rather than trying to clean data directories again.

The truncated error message is also a minor operational mistake—using tail -15 when the error might be longer. A more conservative approach would be tail -30 or tail -50 to capture the full error chain. However, in practice, the assistant already knew what the problem was from context, so the truncation didn't hinder the debugging process.

Conclusion

This message captures a perfect snapshot of real-world distributed systems debugging. It shows how a single docker logs command can reveal a cascade of dependency failures, how assumptions about data cleanup can lead to persistent bugs, and how experience with prior errors enables rapid diagnosis. The error message itself is a textbook example of dependency injection failure propagation—each layer of abstraction adds another level of nesting, and the root cause is buried at the bottom. The assistant's ability to recognize this pattern and connect it to the dirty migration problem (confirmed in the next message) demonstrates the kind of system-level thinking that effective debugging requires.