Kafka Transactional IdDistributed Systems & Databases
Start with all trials for auto-written insights, or select a single trial to read its full trajectory.
Fable-5claude-code · high
Mean reward
0%All trials
Across 8 trials on this task, reward ran from 0% to 0% — 0 of 8 passed.
- $0.59avg cost
- 1mavg time
Summary · Fable-5
Fable-5 passed none of its 8 trials.
Common pattern
No trial passed. Whatever this task demands, the model misses it every time — a reproducible miss, not variance.
Opus-4.8claude-code
Mean reward
37.5%All trials
Across 8 trials on this task, reward ran from 0% to 100% — 3 of 8 passed.
- $0.40avg cost
- 2mavg time
Summary · Opus-4.8
Opus-4.8 passed 3 of 8 trials.
Common pattern
3 of 8 trials passed on identical attempts: the capability is there, the reliability is not.
Gemini-3gemini-cli
Mean reward
87.5%All trials
Across 8 trials on this task, reward ran from 0% to 100% — 7 of 8 passed.
- $0.29avg cost
- 2mavg time
Summary · Gemini-3
Gemini-3 passed 7 of 8 trials.
Common pattern
7 of 8 trials passed on identical attempts: the capability is there, the reliability is not.
GPT-5.4codex
Mean reward
0%All trials
Across 8 trials on this task, reward ran from 0% to 0% — 0 of 8 passed.
- 24avg turns
- $0.13avg cost
- 1mavg time
Summary · GPT-5.4
GPT-5.4 passed none of its 8 trials.
Common pattern
No trial passed. Whatever this task demands, the model misses it every time — a reproducible miss, not variance.
Published trajectory
The drop publishes a full step record for one of the trials above — every other attempt shipped its score without its route.
“Artifacts are in `/logs/artifacts`: - [fixed_producer.py](/logs/artifacts/fixed_producer.py) - [consumer.py](/logs/artifacts/consumer.py) - [simulation_result.json](/logs/artifacts/simulation_result.json) - [root_cause.txt](/logs/artifacts/root_cause.txt) The fix is in the producer identity path: `transactional_id` now comes from a restart-stable replica ordinal instead of `POD_NAME`/`HOSTNAME`, which were changing after the Kubernetes migration and breaking Kafka producer epoch fencing. The corrected producer is self-contained, keeps `enable.idempotence=true`, `acks=all`, and the consumer remains unchanged. Verification: running `/app/simulation.py` against the fixed producer produced `duplicates: 0`, `throughput: 10000`, and `dynamic_transactional_id: false`, which satisfies the zero-duplicate and `>= 9500 msg/sec` requirements.”
The full trajectory
- system
<permissions instructions> Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. </permissions instructions><skills_instructions> ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains. (file: /tmp/codex-home/skills/.system/openai-docs
- user
<environment_context> <cwd>/app</cwd> <shell>bash</shell> <current_date>2026-07-17</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>
- user
# Kafka Exactly-Once Payment Duplication A Kafka-based payment processing pipeline, deployed as a replicated Kubernetes workload, has started producing duplicate records in the downstream accounting database after a Kubernetes migration. - Duplication rate: ~0.02% (about 200 duplicates per million payments) - Started three weeks ago, coinciding with a Kubernetes migration - Pipeline ran correctly for six months before the migration - Kafka broker logs are clean - Producer EOS configuration appears correct Fix the pipeline so that a 1-hour production run at 10,000 messages/second with pod restarts every 60 seconds produces zero duplicate payment records in the accounting database while maintaining throughput >= 9,500 msg/sec. Do not change the following exactly-once semantics configuration: - Producer: `enable.idempotence=true`, `acks=all` - Consumer: `isolation.level=read_committed` Do not mask the problem with additional deduplication or duplicate suppression layers. The duplicates must be prevented at the source. Your `fixed_producer.py` artifact will be evaluated as a standalone Python file; it must not depend on other files under `/app`. Write the following artifacts to `/logs/artifacts/`: 1. `fixed_producer.py` — the corrected, self-contained producer 2. `consumer.py` — a copy of the unchanged consumer 3. `simulation_result.json` — result of running `/app/simulation.py` against `fixed_producer.py` 4. `root_cause.txt` — a brief explanation of the root cause and the fix
- agent
I’m inspecting the pipeline code and simulation harness first so I can identify where the migration introduced duplicate production behavior and fix it at the source.
- agent
Executed exec_command call_Nti5nI79nUr4sIgIfR4RBnCI
- agent
Executed exec_command call_aFlHjLIiQJFOX9ALdXZItNtu
- agent
I have the relevant files. Next I’m reading the producer, consumer, identity code, deployment config, and the simulation harness to map the failure mode to the Kubernetes migration.
- agent
Executed exec_command call_bJSuIz20SEirh5KNCxVIomBy
- agent
Executed exec_command call_JzipxFHg9cebkr99iySRKOyW
- agent
Executed exec_command call_tbnBkqgUd7JNVpvjB6cm5u3c
- agent
Executed exec_command call_TcpHS8ZreQiSRZsWkn2vfGIq
- agent
Executed exec_command call_9Yfz2rYKWTyJkr1ZUyulc5Ou
- agent
The failure condition is in the producer identity path, not Kafka EOS settings. I’m checking the runbook for any platform assumptions, then I’ll patch a self-contained producer that derives a stable per-replica transactional ID across pod restarts.
- agent
Executed exec_command call_mQ5GqfCzWHzAe7tnGrEHUPex
- agent
Executed exec_command call_fJiRiVg8YRzLEIkGChDMmGU3
- agent
Executed update_plan call_O8ctgjPNzOnItrhcIr1K0ksv
- agent
I have the fix shape now: keep the Kafka EOS settings as-is, replace the unstable pod-name/hostname-based transactional identity with a stable replica identity, and fail closed if the platform doesn’t provide one. I’m writing the standalone producer and the artifact notes next.
- agent
Executed exec_command call_ERcgqkrUbDWcDzv8cyIB0umA
- agent
Executed apply_patch call_3elNViH3ngVrvTQajb4xW1sF
- agent
The producer artifact is in place. I’m copying the consumer verbatim and then running the simulator against the fixed producer to confirm zero duplicates at the required rate.
- agent
Executed exec_command call_n3k8Y4Hy6ePWvXYZlChAMhBt
- agent
Executed exec_command call_6UhVyCT8W2azLsFhJNjp4J9M
- agent
Executed update_plan call_zQJAhgHK3ufBVwXSqb44IJYG
- agent
Artifacts are in `/logs/artifacts`: - [fixed_producer.py](/logs/artifacts/fixed_producer.py) - [consumer.py](/logs/artifacts/consumer.py) - [simulation_result.json](/logs/artifacts/simulation_result.json) - [root_cause.txt](/logs/artifacts/root_cause.txt) The fix is in the producer identity path: `transactional_id` now comes from a restart-stable replica ordinal instead of `POD_NAME`/`HOSTNAME`, which were changing after the Kubernetes migration and breaking Kafka producer epoch fencing. The corrected producer is self-contained, keeps `enable.idempotence=true`, `acks=all`, and the consumer remains unchanged. Verification: running `/app/simulation.py` against the fixed producer produced `duplicates: 0`, `throughput: 10000`, and `dynamic_transactional_id: false`, which satisfies the zero-duplicate and `>= 9500 msg/sec` requirements.