engine-debugging

name

engine-debugging

description

Run Dagger repo tests and debug Dagger engine, core, dagql, filesync, cache, CI trace, panic, hang, leak, and performance issues. Use whenever an agent needs to run tests, choose a test command, or interpret test output in this repository, even before a failure is diagnosed; also use for engine-dev tests, Dagger Cloud trace replay, debug endpoints or pprof, goroutine dumps, panics, hangs, leaks, performance issues, and /debug/dagql/cache snapshots.

Engine Debugging

This is the default guide for running Dagger engine/core tests and for debugging the failures those tests expose.

CLI Version Compatibility

[!IMPORTANT] Commands in this guide use Dagger v1.0 or later. If your installed Dagger version is earlier than v1.0, replace every dagger api call ... invocation below with dagger call ....

Start from evidence, not broad guesses.

Core Loop

1. Write down the expected flow through the subsystem being debugged.
2. Log actual values at each boundary.
3. Find the first divergence.
4. Decide whether the bug is in identity construction, lookup, lifecycle, compatibility behavior, or an external integration boundary.

Use focused repros, recorded traces, and small log windows. Avoid dumping full test output into the conversation.

Use Dagger's default progress UI. Do not pass --progress=plain.

Prefer small, high-signal log lines over broad dumps. Good debug logs identify the boundary being checked and include the relevant stable IDs, digests, keys, hit path, or lifecycle state needed to compare expected and actual behavior.

Repro First

Use a tight test repro before adding logs.

Recommended integration command format:

dagger api call engine-dev test --pkg ./core/integration --run='<TestSuiteName>/<SubtestName>'

This command rebuilds the dev engine, runs it as an ephemeral service, and then runs tests against it. Output includes:

• dev engine build output
• test runner output
• engine logs/printlns
• test logs, such as t.Logf

Capture output to a file under /tmp to avoid overwhelming terminal context:

dagger api call engine-dev test --pkg ./core/integration --run='<TestSuiteName>/<SubtestName>' > /tmp/engine-debug.log 2>&1
rg -n "panic:|--- FAIL:|^FAIL\s" /tmp/engine-debug.log

During long runs, periodically grep for panics. If the engine panics, tests may hang indefinitely:

rg -n "panic:|fatal error:|SIGSEGV|stack trace" /tmp/engine-debug.log

If a test appears hung, capture a goroutine dump from the inner dev engine process with SIGQUIT. Follow this closely so SIGQUIT is not sent to the wrong process:

engine_ctr="$(docker ps --format '{{.Names}}' | rg '^dagger-engine-v' | head -n1)"
docker exec "$engine_ctr" sh -lc '
for p in /proc/[0-9]*; do
  pid=${p#/proc/}
  [ "$pid" = "1" ] && continue
  cmd="$(tr "\0" " " < "$p/cmdline" 2>/dev/null || true)"
  case "$cmd" in
    *"/usr/local/bin/dagger-engine"*)
      echo "sending SIGQUIT to inner dagger-engine pid=$pid" >&2
      kill -QUIT "$pid"
      exit 0
      ;;
  esac
done
echo "no inner dagger-engine process found" >&2
exit 1
'

Then inspect the same run log for the dump:

rg -n "goroutine [0-9]+|fatal error:|SIGQUIT|chan receive|chan send|semacquire|sync\\.Mutex|deadlock" /tmp/engine-debug.log

After sending SIGQUIT, the tests may hang. Once you confirm the log has SIGQUIT stack traces, you are done and do not need to wait for the test hang to end.

To compare behavior against an engine from another git ref:

dagger api call engine-dev --source 'https://github.com/dagger/dagger#main' test --pkg ./core/integration --run='TestSomeSuite/TestSomeSubtestYouWant'

Do not run multiple suites in parallel unless necessary. Each suite is CPU-heavy and concurrent runs significantly degrade performance.

Do not use broad ./... when running tests during engine-debug loops. You can accidentally capture integration tests or other tests you did not mean to run.

./core/integration, ./dagql/idtui, and ./dagql/idtui/multiprefixw are integration-style test packages, not quick unit loops. Avoid running them during tight debug cycles unless you explicitly need those integration paths.

CI Trace Replay

When a failure happens in CI, start from the trace if one is available. The user may provide either a raw trace ID or a command copied from the web UI, such as:

dagger trace <trace-id>

Replay that trace locally and capture it to a temp file:

dagger trace <trace-id> > /tmp/ci-trace-<trace-id>.log 2>&1

This does not rerun the CI job. It fetches and prints the recorded trace. Keep the full output in /tmp, inspect it with rg, and avoid dumping the whole trace into the conversation.

If the user gives a GitHub PR URL instead of a trace ID, start by checking the PR status with GitHub CLI:

gh pr checks <pr-url>

If a failed check points to a Dagger trace, replay it with dagger trace ... and capture output to /tmp as described above.

Start with the usual failure scan:

rg -n "panic:|fatal error:|SIGSEGV|--- FAIL:|^FAIL\s|Error:|error:" /tmp/ci-trace-<trace-id>.log

Then inspect around the interesting spans:

rg -n "TestName|FieldName|module name|command text" /tmp/ci-trace-<trace-id>.log
sed -n '<start>,<end>p' /tmp/ci-trace-<trace-id>.log

Use the replayed trace to identify the exact failing call, subtest, generated command, or engine error. Once the failing surface is clear, decide whether to reproduce it locally with a tight dagger api call engine-dev ... command or debug directly from the recorded CI trace.

Performance Debugging With Persistent Dev Engine

For most testing/debugging flows, prefer ephemeral engines via:

dagger api call engine-dev ...

For performance debugging, such as pprof snapshots, repeated profiling loops, or endpoint inspection, use a persistent dev engine running in Docker.

Start Persistent Dev Engine

docker rm -fv dagger-engine.dev
docker volume rm dagger-engine.dev
./hack/dev

Notes:

• The container is named dagger-engine.dev.
• This engine persists across commands/runs, so it is better for iterative perf investigation.
• A clean reset is often desirable for consistent baselines, but is not always required; it depends on whether cache/warm state is part of what you are measuring.

Run Commands Against Persistent Engine

Use ./hack/with-dev to target the running dagger-engine.dev:

./hack/with-dev go test -v -count=1 -run='TestWorkspace/TestWorkspaceContentAddressed/storing_a_Directory' ./core/integration/

You can also run Dagger commands through the same wrapper:

./hack/with-dev ./bin/dagger ...

Important CLI gotcha:

• If you do ./hack/with-dev bash -c 'dagger ...', you may accidentally pick up a non-dev dagger binary from PATH.
• In shell-wrapped commands, explicitly use ./bin/dagger to avoid ambiguity.

Docker-Level Debugging

Because the engine is a normal Docker container, you can use standard Docker tools:

• docker logs dagger-engine.dev
• docker exec -it dagger-engine.dev sh
• docker kill -s <SIGNAL> dagger-engine.dev

pprof and Debug Endpoints

The dev engine exposes debug endpoints on localhost:6060.

• Current routes are defined in cmd/engine/debug.go.
• Use whichever endpoint/tooling fits the question: point-in-time snapshots, time-window captures, pprof profiles, or debug endpoint snapshots.

Example heap profile capture over 15 seconds:

curl 'http://localhost:6060/debug/pprof/heap?seconds=15' > /tmp/heap.pprof

Then inspect with:

go tool pprof /tmp/heap.pprof

General profiling guidance:

• Choose profile type and capture window based on the symptom.
• For long-running or phase-specific regressions, align profile capture timing with the relevant test phase.
• Keep artifacts organized by run so diffs/comparisons are straightforward.

Metrics-First Leak Triage

When debugging leaked dagql cache refs, start with Prometheus metrics before adding deep logs.

Enable metrics on the target engine:

_EXPERIMENTAL_DAGGER_METRICS_ADDR=0.0.0.0:9090
_EXPERIMENTAL_DAGGER_METRICS_CACHE_UPDATE_INTERVAL=1s

Current high-signal metrics:

• dagger_connected_clients
• dagger_dagql_cache_entries

Interpretation:

1. If dagger_connected_clients is 0 but dagger_dagql_cache_entries stays above the warmed baseline, refs may still be retained.
2. dagger_dagql_cache_entries is an index-entry count, not a unique-result count. The same shared result may appear in multiple indexes.

Practical scrape tip for nested-engine integration tests:

• Prefer scraping via a container bound to the engine service, such as curl http://dev-engine:9090/metrics.
• Scraping from the test process via endpoint hostname may fail DNS resolution in some test networks.

Useful correlation log during session teardown:

• engine/server/session.go logs released dagql cache refs for session with beforeEntries and afterEntries.
• If afterEntries trends upward across completed sessions, session close may not be releasing all refs.

Internal Docs

Detailed implementation docs live in ../../internal-docs/. These docs are useful when debugging a specific subsystem and needing the current mental model:

• cachebasics.md: result model, GetOrInitCall, dependencies, public cache APIs
• egraph.md: symbolic equivalence, terms, eq-classes, hit selection
• cache_persistence.md: startup/shutdown persistence model
• cache_pruning.md: retention roots, persisted-edge pruning, size accounting
• lazy_evaluation.md: lazy result evaluation and object materialization
• session_resources.md: secret/socket handle model and session-compatible hits
• filesync.md: host/engine sync protocol and mirror/change-cache behavior
• mutablecache.md: mutable-backed objects such as HTTP, git mirrors, filesync mirrors, cache volumes
• typedefs.md: typedef identity and caching hot paths
• dynamicinputs.md: dynamic inputs and implicit cache scoping
• dagqltypes.md: nullable/list cache behavior
• writingcoreapis.md: practical guide for cache-aware core/schema APIs
• version-gating.md: schema views, engineVersion gates, workspace v1 test fixtures

Treat internal docs as context, not authority over the code. If you are changing the implementation, your edits may make the docs stale; verify behavior against the current code and tests.

Cache Snapshot Analyzer

Use the bundled analyzer for streamed /debug/dagql/cache snapshots:

go run ./skills/engine-debugging/scripts/dagql-cache-analyzer.go /tmp/dagql.cache.1

It summarizes retained roots, result categories, and approximate cumulative closures so large cache snapshots can be inspected offline.