Manual physical snapshots start additional snapshot and retention schedulers

Open
  • Is something wrong?
#403 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
45/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Active
Tech stack
go, postgresql
Domain
backend, databases

Research direction

Read engine/internal/srv/routes.go and engine/internal/retrieval/retrieval.go around the named createSnapshot and SnapshotData paths, then inspect engine/internal/retrieval/engine/postgres/snapshot/physical.go for scheduler startup and lifecycle. Reproduce the reported sequence of startup followed by four manual requests and observe scheduled callbacks. Done means manual snapshots do not add recurring schedulers, and snapshot and retention jobs run once per pool with operations coordinated.

Written by the indexing model from the issue text.

Description

While testing physical snapshots on DBLab Engine v4.2.0, I noticed that each manual POST /snapshot request starts another recurring scheduler. After four manual snapshots, the scheduled snapshot and retention jobs run five times instead of once.

I also reproduced the scheduler behavior against the v4.2.2 retrieval code in an isolated Go test. That test skipped storage operations; it was not a full v4.2.2 database restore test.

Steps to reproduce

  1. Start an engine with a ZFS pool and physicalRestore / physicalSnapshot configured.

  2. Enable scheduled snapshots and retention. For example, this is the relevant configuration section:

    retrieval:
  jobs: [physicalRestore, physicalSnapshot]
  spec:
    physicalSnapshot:
      options:
        scheduler:
          snapshot:
            timetable: "0 * * * *"
          retention:
            timetable: "30 * * * *"
            limit: 2

  3. Wait for initialization to finish, then request four manual snapshots sequentially, letting each request finish before starting the next. For a pool named test_pool:

    curl --fail-with-body --request POST \
  --header 'Verification-Token: <token>' \
  --header 'Content-Type: application/json' \
  --data '{"poolName":"test_pool"}' \
  http://127.0.0.1:2345/snapshot

  4. Check the logs at the next scheduled snapshot and retention times.

Actual behavior

  • Startup logs Snapshot scheduler has been started once. Each manual request adds another occurrence, without stopping the previous scheduler.
  • After four manual requests, five snapshot jobs start at the scheduled time. One succeeds; the others collide with it and report that the dataset already exists.
  • The retention callbacks also run concurrently against the same targets. I observed dataset is busy errors during these overlapping cleanup attempts.

The isolated test exercised the real SnapshotData and scheduler startup path. The number of active schedulers increased from 1 -> 2 -> 3 -> 4 -> 5, and callbacks from all five fired. Cancelling the startup context stopped only the first scheduler; the four created with context.Background() continued running. The test explicitly stopped all schedulers afterward.

Expected behavior

A manual snapshot should take one snapshot without adding another recurring schedule. Scheduled snapshot creation and retention should continue running once per pool, with manual and scheduled operations coordinated.

Relevant code

  • createSnapshot calls SnapshotData(context.Background(), poolName).
  • SnapshotData constructs new jobs and replaces statefulJobs without stopping the previous jobs.
  • PhysicalInitial.Run starts a scheduler on return. Each instance registers both snapshot and retention callbacks and has its own mutex, so the instances do not serialize their operations with each other.

Workaround

Restarting the engine clears the extra schedulers. Avoiding manual POST /snapshot requests afterward prevents them from accumulating again.

Dominant language
Go
Stars
2.8k
Forks
90
No merged PRs in 30d

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from postgres-ai/database-lab-engine

All issues in postgres-ai/database-lab-engine

Similar issues

More Go issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.