The Case of the Colliding Job IDs: Debugging a Distributed Proving Pipeline

In distributed systems, the most devastating bugs often arise from the simplest assumptions—assumptions about uniqueness, about identity, about how a key is formed. On March 13, 2026, an AI assistant debugging a production Filecoin proving system encountered exactly such a bug: a job ID collision that silently corrupted every proof in a ten-partition pipeline, turning valid GPU computations into garbage outputs. The message at the center of this investigation ([msg 1978]) captures the moment of diagnosis, where scattered evidence—a panic message, a timing log, a user's experimental observation—coalesced into a clear understanding of a fundamental design flaw.

The Context: A Proving System Under Siege

The conversation leading up to this message had been a multi-day saga of production debugging. The system in question was "cuzk," a GPU-accelerated zero-knowledge proving engine written in Rust, integrated with a Go-based Curio node. The ProofShare subsystem allowed multiple parties to contribute GPU power to generate proofs for Filecoin storage challenges. But the system was failing: every single one of ten PoRep (Proof-of-Replication) partitions was producing invalid proofs, despite the GPU computations completing successfully.

Earlier in the session ([msg 1976]), the assistant had deployed a freshly built cuzk binary from a Docker container, replacing a binary built via a minimal rebuild Dockerfile that had been producing bad proofs. But even with the "good" binary, the proofs were still failing. The user reported ([msg 1977]) a cascade of errors: all ten partitions marked INVALID, a panic at pipeline.rs:1910 reading "partition 0 already inserted," and a critical clue—when running with proofshare_max_tasks=1 (serializing all proving tasks), the proofs were correct. With parallelism, everything broke.

This was the puzzle that message 1978 set out to solve.

The Moment of Diagnosis: Connecting the Dots

The assistant's reasoning in [msg 1978] is a masterclass in systematic debugging. It begins by enumerating five key observations:

  1. The job_id for proofshare proofs is ps-porep-1000-1, derived from miner ID and sector ID.
  2. Challenge sectors from the upstream service (cusvc/powsrv) all use the same miner=1000, sector=1.
  3. When multiple PSProve tasks run in parallel, they all share the identical job_id.
  4. With serialized execution (proofshare_max_tasks=1), proofs are correct.
  5. With parallelism, all partitions fail. The crucial leap comes in point 6, where the assistant connects these observations to the internal architecture of the cuzk engine:
"The job_id serves as a key in the engine's JobTracker.assemblers HashMap, so when concurrent proofs share the same job_id, their partition results collide and overwrite each other. The 'partition 0 already inserted' panic confirms this — a previous job with the same job_id already claimed that partition slot."

This is the heart of the diagnosis. The cuzk engine uses a HashMap keyed on job_id to track partition assembly state. When two concurrent proof jobs both submit their results under the same key, the engine cannot distinguish which partition result belongs to which job. Partition 0 from job A collides with partition 0 from job B, triggering the panic. But even without the panic, the non-deterministic interleaving of partition results from different jobs produces corrupted proofs—partition 1 from job A might be paired with partition 2 from job B, and the resulting assembled proof is internally inconsistent.

The Root Cause: A Design Assumption Violated

The root cause traces back to how the RequestId was generated in the Go code. In the proofshare task (task_prove.go), the request ID was formatted as:

fmt.Sprintf("ps-porep-%d-%d", sectorID.Miner, sectorID.Number)

This produced "ps-porep-1000-1" for every single proofshare challenge, because all challenges targeted the same benchmark sector. The design assumed that each RequestId would be unique per proving invocation—a reasonable assumption in normal operation where different sectors have different IDs. But in the ProofShare system, challenges are synthetic: they all target the same sector (miner=1000, sector=1) with different random seeds. The sector ID alone is insufficient to distinguish concurrent invocations.

The assistant correctly identifies that the Snap (SnapDeals) proving path already handles this correctly by including the taskID in its request ID. The PoRep path simply never received this parameter. The fix is to thread the taskID through the call chain from computeProof to computePoRep, making the request ID unique per invocation.

The Evidence Chain: How Each Clue Contributed

The diagnosis in [msg 1978] is remarkable for how it synthesizes multiple independent evidence streams:

The Panic Message: The Rust engine panicked with "partition 0 already inserted" at pipeline.rs:1910. This was the smoking gun—it directly indicated that the assembler HashMap was receiving duplicate partition insertions for the same key. The assistant correctly interpreted this not as a random crash but as a structural symptom of key collision.

The User's Experiment: The user reported that setting proofshare_max_tasks=1 produced correct proofs. This was the critical controlled experiment. By eliminating parallelism, the user eliminated the collision. The same code, same binary, same sector—but with serial execution, the job_id collision never occurred because only one job was active at a time. This proved the bug was in the concurrent pipeline, not in the proving logic itself.

The Timing Logs: The GPU timing logs showed that partition results were being dispatched and completed with overlapping timestamps. Worker 0 picked up partition 1, then the GPU END event showed partition 0 completing—but partition 0's result was already being inserted by a different job. The warning "assembler not found, discarding partition result" for partition 2 further confirmed that the assembler state was being corrupted by concurrent access.

The Benchmark Comparison: The user noted that the benchmark (which also used miner=1000, sector=1) passed. The assistant reasoned that the benchmark likely ran with a single proving invocation, avoiding the collision. The user speculated that "maybe reused seed between challenges = partitions interchangeable," but the assistant correctly identified the real mechanism: the benchmark didn't have concurrent jobs with identical IDs.

Assumptions Made and Validated

The assistant's reasoning in [msg 1978] rests on several assumptions, each of which is either validated by evidence or explicitly checked:

Assumption 1: The engine's assembler uses job_id as a HashMap key. This is confirmed by the panic message and the "assembler not found" warnings. The Rust source code at pipeline.rs:1910 explicitly shows the insert operation failing on a duplicate key.

Assumption 2: The non-proofshare path is safe. The assistant notes that "normal PoRep C2 has unique sector IDs per job so it's safe." This is validated in the subsequent message ([msg 1980]) where a grep finds only two locations using RequestId with porep: the proofshare path and the normal FFI path. The normal path uses real sector IDs which are inherently unique per job.

Assumption 3: The Snap path already handles uniqueness correctly. The assistant references that computeSnap already receives and uses taskID. This is confirmed by the code structure—the Snap path was designed with uniqueness from the start, while the PoRep path was an oversight.

Assumption 4: The fix is in the Go side, not the Rust side. The assistant correctly identifies that making the RequestId unique on the caller side is sufficient. The Rust engine's HashMap behavior is correct—it should reject duplicate keys. The bug is that the Go side was generating duplicate keys.

The Thinking Process: A Window into Debugging Methodology

The reasoning section of [msg 1978] reveals a structured debugging methodology. The assistant begins by enumerating facts, then forms a hypothesis, then validates it against the available evidence. The thought process is iterative:

  1. Observation: All partitions fail with parallelism, none fail serialized.
  2. Hypothesis: The job_id is the same for all concurrent jobs.
  3. Verification: The job_id format is ps-porep-{miner}-{sector}, and all challenges use miner=1000, sector=1.
  4. Mechanism: The engine's assembler HashMap keys on job_id, so concurrent jobs collide.
  5. Confirmation: The "partition 0 already inserted" panic is a direct consequence.
  6. Fix identification: Include taskID in the RequestId, mirroring the Snap path. The assistant also demonstrates awareness of the broader system architecture. It notes that computeProof already receives taskID and passes it to computeSnap but not to computePoRep—this is a clear code path analysis that identifies exactly where the fix needs to be applied.

Input Knowledge Required

To understand [msg 1978], the reader needs knowledge of:

Output Knowledge Created

This message creates several valuable outputs:

The Broader Significance

The bug diagnosed in [msg 1978] is a classic example of a "key collision" failure in distributed systems—a pattern that appears in everything from database sharding to cache invalidation to job scheduling. The fundamental lesson is that keys must be globally unique across all concurrent invocations, not just unique within a single invocation's scope. The sector ID was a perfectly good unique identifier in a system where each sector was processed once, but it became a collision source when the same sector was processed concurrently by multiple jobs.

The fix—including the task ID in the request ID—is a textbook solution: add a monotonically increasing or randomly generated component to the key to ensure uniqueness. The assistant's recognition that the Snap path already does this correctly demonstrates the value of studying existing patterns in the codebase before inventing new solutions.

Conclusion

Message [msg 1978] represents a pivotal moment in a complex debugging session. In a few paragraphs of reasoning, the assistant transformed a baffling production failure—ten partitions all producing invalid proofs despite a working binary and correct serial execution—into a clear, actionable diagnosis. The thinking is methodical, the evidence is carefully weighed, and the fix is precisely targeted. It's a reminder that in distributed systems, the most elusive bugs are often the simplest: a key that wasn't quite unique enough.