git:20260709.ffd6488 to git:20260803.a6107fb

---

name: greptimedb-fuzz-ci-failure-investigation

description: Investigate a failed GreptimeDB fuzz CI target link by downloading GitHub Actions job logs plus fuzz artifacts such as kind logs, monitor dumps, and CSV dumps, then correlate the failure with local GreptimeDB source code. Use when the user provides a failed fuzz CI target/job URL or asks to diagnose GreptimeDB fuzz CI failures.

---

# GreptimeDB Fuzz CI Failure Investigation

Investigate failed fuzz-test jobs for **`GreptimeTeam/greptimedb`** from a local

checkout. The purpose is to download the failed job's CI output and fuzz artifacts,

then explain the likely cause by correlating the evidence with GreptimeDB source.

Always pass `--repo GreptimeTeam/greptimedb` to `gh`; local remotes may point to

forks. Keep the workflow read-only: do not rerun jobs, cancel workflows, push,

comment on PRs, or delete artifacts unless the user explicitly asks.

## Scope

are the CI jobs that use `.github/actions/fuzz-test`:

- `fuzztest` — standalone fuzz targets.

- `unstable-fuzztest` — unstable standalone fuzz target.

- `distributed-fuzztest` — distributed cluster fuzz targets.

- `distributed-fuzztest-with-chaos` — distributed fuzz targets with Chaos Mesh.

```bash

```

```text

```

## Inputs

The normal user input is a **CI target link**: a GitHub Actions job URL for one

failed matrix target. Treat that URL as the primary source of truth. Example:

```text

https://github.com/GreptimeTeam/greptimedb/actions/runs/29000666156/job/86062894741

```

Also accept these fallback inputs:

- A workflow run URL, e.g.

`https://github.com/GreptimeTeam/greptimedb/actions/runs/29000666156`.

- A run id plus a fuzz target, mode name, artifact name, PR, branch, or commit SHA.

Parse ids from URLs:

```bash

REPO=GreptimeTeam/greptimedb

RUN_ID=29000666156

JOB_ID=86062894741

```

When the input is a job URL, do not ask for the target name first. Use the job id

```text

```

If only a run URL is provided, list failed jobs and choose the fuzz job matching the

mode, artifact, or job-url clue.

## Prerequisites

- `gh` must be installed and authenticated (`gh auth status`). Actions logs and

artifacts commonly require authentication even for a public repo.

- `jq`, `unzip`, and standard shell tools are useful for reducing logs.

- Use the local GreptimeDB checkout for source analysis. If the run's head SHA is

not the current checkout, inspect the code at the run's SHA before making claims.

- Use a scratch directory outside the source tree, such as `/tmp/greptimedb-fuzz-ci`.

## 1. Capture run and job metadata

Create a scratch directory:

```bash

mkdir -p /tmp/greptimedb-fuzz-ci/$RUN_ID

cd /tmp/greptimedb-fuzz-ci/$RUN_ID

```

Fetch run metadata:

```bash

gh run view "$RUN_ID" --repo "$REPO" \

--json databaseId,name,displayTitle,event,headBranch,headSha,status,conclusion,createdAt,updatedAt,url,workflowName \

> run.json

```

Fetch all jobs through the REST API:

```bash

gh api "repos/$REPO/actions/runs/$RUN_ID/jobs?per_page=100" \

--paginate --jq '.jobs[]' \

| jq -s '{jobs: .}' > jobs.json

```

List failed fuzz-like jobs:

```bash

jq -r '

.jobs[]

| select(.conclusion != null and .conclusion != "success")

| select(.name | test("Fuzz Test|fuzz"; "i"))

| [.id, .name, .status, .conclusion, .html_url] | @tsv

' jobs.json

```

For a known `JOB_ID`, capture job name and step outcomes:

```bash

jq -r --arg id "$JOB_ID" '

.jobs[] | select((.id | tostring) == $id) |

"job=\(.name) conclusion=\(.conclusion) url=\(.html_url)",

(.steps[]? | [.number, .name, .status, .conclusion, .started_at, .completed_at] | @tsv)

' jobs.json > job-summary.txt

```

## 2. Download CI output for the failed job

Download the selected job's log; this is the authoritative target CI output for the

`cargo fuzz run` command:

```bash

gh run view "$RUN_ID" --repo "$REPO" --job "$JOB_ID" --log > job-$JOB_ID.log

```

Download the full workflow log only when setup/build context matters:

```bash

gh run view "$RUN_ID" --repo "$REPO" --log > run-$RUN_ID.log

```

Fallback if `gh run view --log` is incomplete. The job logs endpoint returns a

plain text stream, while the full run logs endpoint returns a zip archive:

```bash

gh api "repos/$REPO/actions/jobs/$JOB_ID/logs" > job-$JOB_ID.log

gh api "repos/$REPO/actions/runs/$RUN_ID/logs" > run-$RUN_ID-logs.zip

unzip -oq run-$RUN_ID-logs.zip -d run-logs

```

In the job log, inspect the `Run Fuzz Test` step first. Capture:

- exact target name;

- `max_total_time`;

- panic/assertion/backtrace or libFuzzer crash output;

- generated crash/reproducer path if printed;

- timeout or resource symptoms.

## 3. Find and download fuzz artifacts

List artifacts:

```bash

gh api "repos/$REPO/actions/runs/$RUN_ID/artifacts?per_page=100" \

--paginate --jq '.artifacts[]' \

| jq -s '{artifacts: .}' > artifacts.json

jq -r '.artifacts[] | [.id, .name, .expired, .size_in_bytes] | @tsv' artifacts.json

```

Download exact artifacts when possible:

```bash

gh run download "$RUN_ID" --repo "$REPO" --name "$ARTIFACT" --dir artifacts/"$ARTIFACT"

```

Fallback by artifact id:

```bash

ARTIFACT_ID=$(jq -r --arg name "$ARTIFACT" '.artifacts[] | select(.name == $name and (.expired | not)) | .id' artifacts.json | head -n 1)

if [ -z "$ARTIFACT_ID" ]; then

echo "Artifact not found or expired: $ARTIFACT"

exit 1

fi

gh api "repos/$REPO/actions/artifacts/$ARTIFACT_ID/zip" > artifact-$ARTIFACT_ID.zip

mkdir -p artifacts/"$ARTIFACT"

unzip -oq artifact-$ARTIFACT_ID.zip -d artifacts/"$ARTIFACT"

```

## 4. Understand artifact contents

- GreptimeDB component logs: frontend, datanode, metasrv, flownode, monitor.

- Kubernetes pod descriptions/events: restarts, OOMKilled, scheduling, image pull,

volume, DNS, and network issues.

- External service logs: etcd, Kafka, Minio, Chaos Mesh, Kafka WAL helper.

- `state.log`, `sql.log`, `copy.log`, `port-forward.log`;

- `*.show_create_table.sql` for `_gt_logs` and OpenTelemetry trace tables;

- parquet dumps for `_gt_logs`, `opentelemetry_traces`,

`opentelemetry_traces_operations`, and `opentelemetry_traces_services`.

## 5. Build a layered diagnosis model

Before drilling into a specific error string, build a small macro-level model of

the failure. This prevents overfitting on the loudest symptom, such as

`libFuzzer: deadly signal`, `Internal error`, or a cleanup warning.

### 5.1 Create a failure timeline

Record the key timestamps in order:

- job start and setup steps;

- external dependencies ready, such as etcd, Kafka, Minio, and Chaos Mesh;

- GreptimeDB cluster ready;

- fuzz binary starts;

- first generated operation or SQL visible in the target log;

- first suspicious component log line;

- user-visible fuzz failure or panic;

- artifact collection and cleanup.

Use the timeline to classify the failure phase: setup, workload execution,

storage/WAL/write path, query path, cleanup, or runner infrastructure.

### 5.2 Trace the error propagation chain

For distributed fuzz failures, identify the chain from outer symptom to inner

cause candidate. Prefer this shape:

```text

fuzz panic/assertion

-> client-visible SQL/gRPC/HTTP error

-> frontend or monitor error

-> meta/datanode/flownode component error

-> storage/WAL/object-store/Kafka/etcd/k8s dependency error

```

At each boundary, decide whether that layer produced the error or merely wrapped

and forwarded a downstream error. Do not stop at wrapper errors unless there is no

deeper evidence.

### 5.3 Investigate by component boundary

For distributed targets, scan evidence in this order unless the timeline points

elsewhere:

1. fuzz target and generated operations;

2. frontend request handling and SQL/error response;

3. metasrv metadata/procedure/routing;

4. datanode/mito2/metric-engine region execution;

5. WAL, Kafka, object store, Minio, etcd, or other dependencies;

6. Kubernetes pod state, restarts, scheduling, DNS, network, and runner resource

signals.

For each layer, answer two questions: "is this the first causal error?" and "is

this layer producing or propagating the failure?"

### 5.4 Verify intended config against runtime behavior

When a failure appears configuration-related, compare all three levels before

claiming a config did or did not take effect:

1. the workflow/action/Helm/YAML config at the tested SHA;

2. the actual pod/container runtime config or startup logs, if available;

3. the source path that consumes the config, including hard-coded defaults and

library retry/backoff settings.

If a logged value differs from the expected config, explicitly state whether the

config was not applied, was overridden by another layer, or refers to a different

kind of timeout/retry/deadline.

### 5.5 Keep competing hypotheses until evidence rules them out

Before final classification, list at least two plausible causes and the evidence

that supports or weakens each, for example:

- product bug;

- fuzz/test invalid assumption or missing wait;

- config not applied or overridden;

- dependency/infra flake;

- timeout, retry, or resource pressure.

Then choose the most likely cause and keep the confidence scoped. It is often

valid to have high confidence in the observed error chain but only medium

confidence in the ultimate cause category.

### 5.6 State what would disprove the diagnosis

Include one or two falsifiable checks, such as a same-SHA rerun, a fixed

reproducer, a cross-target pattern in the same dependency mode, or one missing

artifact/log that would change the conclusion.

## 6. Reduce evidence before reading source

Extract high-signal lines from job logs and artifacts:

```bash

grep -RInE '(^|[^[:alpha:]])(ERROR|Error|WARN|panic|panicked|FAILED|failure|timeout|Timeout|assert|backtrace|SIG[A-Z]+|OOMKilled|CrashLoopBackOff|ImagePullBackOff|libFuzzer|AddressSanitizer|UndefinedBehaviorSanitizer)' \

job-$JOB_ID.log run-$RUN_ID.log job-logs run-logs artifacts 2>/dev/null > signals.txt

```

Then identify the first causal failure, not the last cleanup error. A typical order:

1. `cargo fuzz run` output in `job-$JOB_ID.log`.

2. GreptimeDB component logs around the same timestamp.

3. Kubernetes events/restarts/OOMs.

4. Monitor/CSV dumps that show the generated SQL or cluster state.

Avoid common mistakes:

- Do not blame cleanup errors after the fuzz failure.

- Do not treat expected retry warnings as root cause unless they align with the

assertion, crash, or timeout.

- Do not assume an artifact is relevant only because it contains `fuzz`; match mode

and target.

- Do not diagnose from current local `main` if the run used a different SHA.

## 7. Inspect the tested source revision

Read the tested SHA:

```bash

HEAD_SHA=$(jq -r '.headSha // empty' run.json)

if [ -z "$HEAD_SHA" ]; then

echo "Error: HEAD_SHA is empty or null"

exit 1

fi

UPSTREAM_REMOTE=$(git remote -v \

| awk '/GreptimeTeam\/greptimedb(\.git)?\/?([[:space:]])/ { print $1; exit }')

UPSTREAM_REMOTE=${UPSTREAM_REMOTE:-origin}

git rev-parse --verify "$HEAD_SHA^{commit}" >/dev/null 2>&1 \

|| git fetch "$UPSTREAM_REMOTE" "$HEAD_SHA"

```

If local `HEAD` differs, inspect files at that SHA or create a temporary worktree:

```bash

git worktree add --detach /tmp/greptimedb-fuzz-ci/$RUN_ID/source "$HEAD_SHA"

```

Remove temporary worktrees after investigation with `git worktree remove <path>`.

Never overwrite the user's current branch.

## 8. Map logs to GreptimeDB code

Start from concrete strings: fuzz target name, panic text, assertion message,

error variant, SQL statement, or log message. Search/read the matching source at

the run SHA.

Useful entry points:

- `tests-fuzz/` — fuzz targets, operation generators, checkers, dump paths.

- `.github/scripts/collect-fuzz-monitor-artifacts.sh` — monitor dump contents.

- `src/mito2/AGENTS.md` — storage/WAL/region failures.

- `src/metric-engine/AGENTS.md` — metric/logical table failures.

- `src/frontend/AGENTS.md` — SQL/protocol/query orchestration failures.

- `src/meta-srv/AGENTS.md` — metadata/procedure/routing failures.

- `src/flow/AGENTS.md` — flow failures.

When reporting code evidence, cite the file/function/line and quote the log line

that reaches it. Separate confirmed facts from hypotheses.

## 9. Classify the cause

Use this taxonomy:

- **Product bug**: GreptimeDB panics, returns wrong results, loses data, or violates

invariants under generated fuzz operations.

- **Fuzz/test bug**: the fuzz target/generator/checker makes an invalid assumption

or fails to wait for an asynchronous condition it requires.

- **Flake/race**: timing/order/resource issue, usually supported by timeouts,

retries, pod restarts, or prior similar failures.

- **Environment/infra**: runner disk/OOM, Docker/kind, network, dependency install,

Kafka/Minio/etcd startup, or GitHub service issue.

- **Unknown**: logs/artifacts are expired or evidence conflicts.

## 10. Final response format

Use this structure:

```markdown

## Summary

- Likely cause: <one sentence>

- Confidence: high|medium|low

- Classification: product bug|fuzz/test bug|flake/race|environment/infra|unknown

- Confidence basis: <which parts are confirmed vs inferred>

## Evidence

- `<job log or artifact path>`: quoted line(s)

- `<source-file:line>`: relevant code path

## Reasoning

<short chain from fuzz target -> CI output -> artifact logs -> source code>

## Alternative hypotheses

- <hypothesis>: <supporting and weakening evidence>

- <hypothesis>: <supporting and weakening evidence>

## Next steps

1. <minimal fix, reproduction command, or verification command>

2. <rerun command or extra log needed, if any>

## What would disprove this

- <specific rerun, artifact, log line, or reproducer result that would change the diagnosis>

```

If logs or artifacts cannot be downloaded because `gh` is not authenticated or the

artifacts expired, say so directly and include the exact command/error.