The Job ID Collision: Debugging a Distributed Proving System's Silent Data Corruption

Introduction

In distributed systems, few bugs are as insidious as the silent data corruption that occurs when concurrent operations inadvertently share a common key, overwriting each other's state without any explicit synchronization failure. Message 1996 in this opencode session captures the moment when an AI assistant formally documented the root cause and fix for exactly such a bug—a job ID collision in the ProofShare subsystem of a Filecoin Curio proving engine. The message reads:

Deployed. Summary of the fix:

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Root cause: RequestId was fmt.Sprintf("ps-porep-%d-%d", miner, sector) — all proofshare challenges target miner=1000, sector=1, so concurrent PSProve tasks sent identical job_ids to cuzk. The engine's partition assembler HashMap keyed on job_id, causing partition results from different proofs to collide (confirmed by "partition 0 already inserted" panic).

>

Fix: Changed to fmt.Sprintf("ps-porep-%d-%d-%d", miner, sector, taskID) in task_prove.go:338, matching the pattern already used by computeSnap.

>

Deployed: curio version ...+_psfix2 running as PID 207971. The lib/ffi/cuzk_funcs.go path (normal PoRep C2) is fine — real sectors always have unique miner+sector combinations.

This seemingly simple three-line change belies a debugging journey that spanned multiple hours, crossed language boundaries between Go and Rust, required Docker-based binary rebuilds on a remote GPU host, and ultimately revealed a fundamental design flaw in how concurrent proof challenges were identified. This article examines the reasoning, decisions, assumptions, and knowledge embedded in this single message.

The Context: A Production System Under Stress

To understand why this message was written, one must first understand the system it describes. The ProofShare protocol is a mechanism in the Filecoin Curio ecosystem that allows proving capacity to be distributed across multiple workers. When a proof challenge arrives, the system dispatches it to a GPU-backed proving engine called "cuzk" (CUDA-based Zero-Knowledge proving). The cuzk engine processes proofs in a partitioned pipeline: a single proof job is split into multiple partitions (typically 10), each synthesized independently and then assembled into a final proof.

The debugging journey began when the user deployed a new cuzk binary and observed that all ten partitions of every PoRep (Proof-of-Replication) proof were producing invalid results. The logs showed a consistent pattern: "partition 0 proof INVALID" through "partition 9 proof INVALID" — a 0/10 validity score. Yet when the user set proofshare_max_tasks=1 (disabling parallelism), proofs became correct. This was the critical clue: the bug was concurrency-related.

The user's observation in message 1977 was prescient: "job-id is derived from minerid-sectorid? Challenge sectors are all the same sector... So possibly some pipeline partition keying issue, where the keys need to be more unique like request uuid?" This hypothesis proved exactly correct.

The Root Cause: A HashMap Collision

The assistant's reasoning in the preceding messages (1978-1983) traced the issue to its source. The RequestId for proofshare challenges was formatted as ps-porep-%d-%d, taking only the miner ID and sector number. In production, all ProofShare challenges targeted the same benchmark sector: miner=1000, sector=1. This meant every concurrent PSProve task sent the identical job_id = "ps-porep-1000-1" to the cuzk engine.

Inside the Rust-based cuzk engine, a JobTracker maintained an assemblers HashMap keyed on job_id. When multiple concurrent proofs with the same job_id arrived, their partition results collided. Partition 0 from proof A would be inserted into the assembler, then partition 0 from proof B would trigger the "partition 0 already inserted" panic — exactly the error seen in production. Even when the panic didn't occur, the collision caused partition results from different proofs to be mixed together, producing garbage proofs that failed verification.

This was a classic case of a distributed systems bug where a shared mutable data structure (the HashMap) was keyed on an identifier that was not actually unique across concurrent invocations. The system had no mechanism to detect or prevent this collision because the Go-side code that generated the RequestId assumed it was unique — an assumption that held for normal PoRep (where every sector has a unique miner+sector combination) but catastrophically failed for ProofShare (where all challenges target the same synthetic sector).

The Fix: Adding Task ID to the Request ID

The fix was remarkably straightforward once the root cause was understood. The assistant modified task_prove.go line 338 to change the format string from "ps-porep-%d-%d" to "ps-porep-%d-%d-%d", adding the harmony task ID as a third component. This pattern already existed in the computeSnap path, which correctly included the task ID for uniqueness.

The assistant also audited the other call site in lib/ffi/cuzk_funcs.go (line 67), which used "porep-%d-%d" for normal PoRep C2 proofs. This path was confirmed safe because real sectors always have unique miner+sector combinations — no two concurrent proofs would ever target the same real sector. This audit demonstrates a thorough understanding of the system's trust model: the bug was not in the key format itself, but in the assumption that miner+sector was always unique.

The Deployment: Docker Builds and Remote Binary Replacement

Deploying the fix required a multi-step process that reveals the operational complexity of GPU-proving systems. The assistant built a new Curio binary inside a Docker container using --volumes-from to access the builder image's Go module cache, then copied the modified source files into the container. The build command set a custom version string (_psfix2) to distinguish the binary.

The deployment to the remote host involved:

  1. Copying the binary via scp over SSH
  2. Killing the running Curio process
  3. Renaming the old binary as a backup
  4. Moving the new binary into place
  5. Verifying the version string and process startup This workflow was necessitated by the environment: a remote GPU host with CUDA dependencies that couldn't easily be replicated locally. Each iteration of the fix required a full Docker build (~163MB binary) and a remote deployment cycle.

Assumptions and Their Consequences

Several assumptions shaped this debugging process. The assistant initially assumed the bad proofs were caused by a GPU proving bug (the num_circuits=1 theory), having just resolved a different cuzk binary quality issue in the preceding messages. The user's observation about parallelism was the key that shifted the investigation toward concurrency.

The assistant also assumed that the lib/ffi/cuzk_funcs.go path was safe — an assumption that was verified but could have been wrong if the normal PoRep path ever received duplicate sector IDs (e.g., during testing or benchmark scenarios). This assumption was reasonable given the system's design, but it highlights the brittleness of relying on implicit uniqueness.

Knowledge Created

This message created several important pieces of knowledge:

  1. The specific bug pattern: Concurrent proofs with identical job IDs cause partition result collisions in the cuzk engine's assembler HashMap.
  2. The fix pattern: Task IDs must be included in request IDs for any path where the base identifiers (miner+sector) may not be unique.
  3. The audit scope: Only the ProofShare PoRep path was vulnerable; the Snap path already included task ID, and the normal PoRep path had naturally unique identifiers.
  4. The deployment workflow: A repeatable process for patching the Curio binary on a remote GPU host using Docker builds and SSH-based binary replacement.

Broader Implications

This bug represents a class of concurrency errors that are notoriously difficult to diagnose in distributed proving systems. The symptom (invalid proofs) could have been attributed to any number of causes: GPU hardware flakiness, driver issues, memory corruption, or algorithmic bugs in the proving code. The key insight — that the same job_id was being reused across concurrent invocations — required connecting the Go-side request ID generation to the Rust-side HashMap keying, a cross-language debugging effort.

The fix also highlights an important design principle for distributed systems: identifiers must be globally unique across all concurrent operations, not just unique within a single operation's context. The original design assumed that miner+sector was a sufficient identifier because it was unique in the storage layer, but the ProofShare protocol violated this assumption by using a synthetic sector for all challenges.

Conclusion

Message 1996 is a deceptively concise summary of a complex debugging journey. In three bullet points, it captures the root cause, the fix, and the deployment status of a production bug that caused silent data corruption in a GPU-backed proving system. The message serves as both a post-mortem and a knowledge artifact — documenting not just what was changed, but why the change was necessary and how the fix was verified.

The real value of this message lies not in the three lines of code it describes, but in the debugging methodology it represents: systematic elimination of hypotheses, cross-language root cause analysis, careful auditing of all affected code paths, and meticulous deployment verification. These are the practices that transform a confusing production incident into a permanent improvement in system reliability.