The Goroutine That Cried Wolf: Debugging Write Failures Through Go's Pprof Lens

Introduction

In the midst of a high-velocity development session on the Filecoin Gateway (FGW) project, a seemingly routine configuration change—enabling parallel writes with two sectors per node in the QA environment—triggered an unexpected failure mode. The user reported that writes were failing, and the investigation that followed produced a single, dense message that reveals much about the art of debugging distributed systems. This article examines message index 2860 from the conversation, in which the assistant runs a Go pprof goroutine dump against a live node and discovers a striking pattern of blocked goroutines. The message is deceptively simple—a curl command and its output—but it encapsulates a pivotal moment in a debugging session where performance profiling meets architectural intuition.

The Message

The subject message, in its entirety, is the output of a goroutine profile fetched from a running Kuri node:

[assistant] [bash] curl -s "http://10.1.232.83:9010/debug/pprof/goroutine?debug=1" 2>&1 | head -150
goroutine profile: total 879
128 @ 0x4818ce 0x45e0b7 0x2476f69 0x48a161
#	0x2476f68	github.com/ipfs/boxo/bitswap/server/internal/decision.(*blockstoreManager).worker+0x108	/home/theuser/.opt/go/pkg/mod/github.com/ipfs/boxo@v0.33.0/bitswap/server/internal/decision/blockstoremanager.go:64

85 @ 0x4818ce 0x443937 0x480aa5 0x5070c7 0x5083b9 0x5083a7 0x6081a5 0x61ac45 0x68c71b 0x539058 0x68c8fe 0x68997b 0x690225 0x690226 0x4d5aae 0x238e5c5 0x238e59d 0x238e498 0x48a161
#	0x480aa4	internal/poll.runtime...

The output shows 879 total goroutines, with two dominant stacks: 128 goroutines blocked in blockstoreManager.worker inside the Bitswap library, and 85 goroutines stuck in internal/poll.runtime—the Go runtime's network polling layer. No further goroutine stacks are shown because the head -150 truncated the output, but these two entries alone tell a compelling story.

Context: The Road to Failure

To understand why this message was written, we must trace the events that led to it. In the preceding messages, the user had directed the assistant to enable parallel write support in the QA environment with two sectors per node. The assistant dutifully updated the Ansible configuration templates, deployed the settings to both Kuri nodes (kuri1 at 10.1.232.83 and kuri2 at 10.1.232.84), and verified that the RIBS.ParallelWriteStats RPC endpoint returned Enabled: true. The deployment appeared successful.

But then the user reported: "Writes failing now, investigate." The assistant initially checked the systemd journal logs, looking for error messages, panics, or failures. The logs showed only benign noise—Bitswap connection resets, a missing CIDGravity API token (expected), and routine S3 requests. No obvious write failures appeared in the logs. The assistant then attempted a direct S3 write using rclone rcat, but that failed with an endpoint URI error unrelated to the parallel write feature—a red herring caused by incorrect command syntax.

At this point, the user offered a crucial diagnostic hint: "Probably check goroutines seems like some timeout?" This suggestion reveals the user's deep familiarity with Go runtime behavior. When writes are slow or failing in a Go service, one of the first places to look is the goroutine profile, which can reveal whether goroutines are blocked, leaked, or stuck in contention. The user suspected a timeout-related issue—something causing goroutines to pile up waiting for a resource that never becomes available.

The Debugging Process: From Metrics Port to Debug Endpoint

The assistant's first attempt to check goroutines used port 2112, the Prometheus metrics endpoint, which returned a 404 error. This is a common stumbling block: Go applications often expose metrics on one port and debug profiling endpoints on another. The assistant quickly corrected course, targeting port 9010—the Kuri node's RPC and debug port—with the correct pprof URL path.

The choice of curl -s "http://10.1.232.83:9010/debug/pprof/goroutine?debug=1" is significant. The debug=1 parameter requests a human-readable text dump of all goroutine stacks, rather than the binary protobuf format that go tool pprof consumes. This is the quickest way to get a snapshot of goroutine state without needing external tooling—a deliberate choice for rapid diagnosis. The head -150 further indicates the assistant wanted only a summary, not the full (potentially enormous) output.

What the Goroutine Profile Reveals

The profile exposes two critical clusters of blocked goroutines:

128 goroutines in blockstoreManager.worker: This is the most alarming finding. The blockstoreManager is a component of IPFS's Bitswap protocol, responsible for managing the blockstore—the local storage of content-addressed blocks. Each worker goroutine processes tasks from a queue, and having 128 of them simultaneously blocked suggests that either the worker pool is oversized (unlikely, as Bitswap typically uses a bounded pool) or the workers are all waiting on a shared resource that has become a bottleneck. The stack trace points to blockstoremanager.go:64, which in the Bitswap source code is the worker's main loop, likely waiting to receive from a channel or acquire a lock.

85 goroutines in internal/poll.runtime: These goroutines are blocked in Go's network poller, waiting for I/O events. This is normal for any networked service—goroutines waiting for incoming connections, outbound requests, or file descriptor events will appear here. However, 85 is a non-trivial number, and when combined with the 128 Bitswap workers, it paints a picture of a system where a significant fraction of goroutines (about 24% of the total 879) are in a waiting state.

The critical insight is that the Bitswap blockstore workers should not be piling up. In normal operation, these workers process blocks quickly and return to their idle state. A pile-up of 128 workers strongly suggests that the blockstore has become a bottleneck—perhaps the underlying storage (YugabyteDB or local disk) is saturated, or a lock contention issue has emerged. This directly correlates with the user's report of write failures: if the blockstore cannot accept new blocks because its workers are stuck, writes will time out or fail.

Assumptions and Mistakes

Several assumptions underpin this debugging step. The assistant assumed that the goroutine profile would reveal the root cause of write failures—a reasonable assumption given the user's suggestion. The assistant also assumed that port 9010 was the correct endpoint for pprof, which turned out to be correct, but only after a failed attempt on port 2112. This initial mistake (trying the metrics port) is a minor but instructive error: it highlights the common confusion between Prometheus metrics endpoints and Go's net/http/pprof debug endpoints, which are often served on separate ports or paths.

Another implicit assumption is that the parallel write feature itself might be responsible for the goroutine pile-up. The timing is suspicious: writes were working before parallel writes were enabled, and they started failing immediately after. However, the goroutine profile shows the blockage in Bitswap's blockstore, not in the parallel write path. This could mean either that the parallel write feature exposed a pre-existing bottleneck in the blockstore, or that the blockage is coincidental and unrelated. The profile alone cannot distinguish these possibilities, but it provides a clear direction for further investigation.

Input Knowledge Required

To fully understand this message, the reader needs knowledge of several domains:

Output Knowledge Created

This message produces several valuable pieces of knowledge:

  1. A diagnostic data point: The goroutine profile provides a snapshot of system state at the time of failure, which can be compared with profiles taken during normal operation to identify anomalies.
  2. A hypothesis about the root cause: The 128 blocked Bitswap workers strongly suggest a bottleneck in the blockstore, pointing the investigation toward storage I/O, lock contention, or channel saturation.
  3. A confirmation of the user's intuition: The user's suspicion about goroutines and timeouts is validated by the profile, which shows exactly the kind of goroutine pile-up that would cause timeouts.
  4. A baseline for further investigation: The profile narrows the search space. Instead of guessing at random configuration issues, the team can now focus on the Bitswap blockstore and its interaction with the parallel write path.

The Thinking Process Visible in the Message

The assistant's reasoning is visible in the sequence of actions leading to this message. When the user reports write failures, the assistant first checks logs—the most obvious diagnostic step. When logs reveal nothing conclusive, the assistant attempts a direct S3 write to reproduce the failure, but gets sidetracked by a command syntax error. The user then provides the critical hint about goroutines and timeouts.

The assistant's response to this hint is methodical: first check the Prometheus metrics for goroutine count (a quick gauge), then attempt to get the full pprof profile. The initial failure on port 2112 shows the assistant learning—trying a known endpoint, failing, and pivoting to the correct port. The use of debug=1 and head -150 shows an understanding that the full profile might be too large, and that the summary (total count + top stacks) is sufficient for a quick assessment.

The decision to present the raw profile output rather than a summary or interpretation is also telling. In a debugging session, raw data often speaks louder than interpretation. The assistant is letting the evidence speak for itself, inviting the user to draw conclusions from the same data. This collaborative debugging style—sharing the raw output and reasoning together—is characteristic of high-trust, high-velocity engineering teams.

Conclusion

Message 2860 is a small but dense artifact of a live debugging session. A single curl command against a Go pprof endpoint reveals that 128 goroutines are stuck in Bitswap's blockstore manager, providing the first concrete clue in a write-failure investigation that began with a configuration change. The message demonstrates the power of Go's runtime profiling tools for diagnosing production issues, the importance of following diagnostic hints from experienced team members, and the value of sharing raw evidence rather than filtered interpretations. For the FGW project, this goroutine profile would become the starting point for a deeper investigation into the interaction between parallel writes and the Bitswap blockstore—a debugging journey that began with a single, well-placed curl command.