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# Chained jobs

Contents

- Chained jobs
  - Current state and shortcomings
  - Proposed changes
    - Implementation details
      - Status while waiting for dependencies
      - Cost of deserialization
  - Other discussed solutions
    - Job-level attribute
    - Client-side logic

This is a design document about the innards of Ganeti’s job processing. Readers are advised to study previous design documents on the topic:

- Original job queue
- Job priorities
- LU-generated jobs

## Current state and shortcomings

Ever since the introduction of the job queue with Ganeti 2.0 there have been situations where we wanted to run several jobs in a specific order. Due to the job queue’s current design, such a guarantee can not be given. Jobs are run according to their priority, their ability to acquire all necessary locks and other factors.

One way to work around this limitation is to do some kind of job grouping in the client code. Once all jobs of a group have finished, the next group is submitted and waited for. There are different kinds of clients for Ganeti, some of which don’t share code (e.g. Python clients vs. htools). This design proposes a solution which would be implemented as part of the job queue in the master daemon.

## Proposed changes

With the implementation of job priorities the processing code was re-architectured and became a lot more versatile. It now returns jobs to the queue in case the locks for an opcode can’t be acquired, allowing other jobs/opcodes to be run in the meantime.

The proposal is to add a new, optional property to opcodes to define dependencies on other jobs. Job X could define opcodes with a dependency on the success of job Y and would only be run once job Y is finished. If there’s a dependency on success and job Y failed, job X would fail as well. Since such dependencies would use job IDs, the jobs still need to be submitted in the right order.

The new attribute’s value would be a list of two-valued tuples. Each tuple contains a job ID and a list of requested status for the job depended upon. Only final status are accepted (`canceled, success, error`). An empty list is equivalent to specifying all final status (except `canceled`, which is treated specially). An opcode runs only once all its dependency requirements have been fulfilled.

Any job referring to a cancelled job is also cancelled unless it explicitly lists `canceled` as a requested status.

In case a referenced job can not be found in the normal queue or the archive, referring jobs fail as the status of the referenced job can’t be determined.

With this change, clients can submit all wanted jobs in the right order and proceed to wait for changes on all these jobs (see `cli.JobExecutor`). The master daemon will take care of executing them in the right order, while still presenting the client with a simple interface.

Clients using the `SubmitManyJobs` interface can use relative job IDs (negative integers) to refer to jobs in the same submission.

Example data structures:

    # First job
    {
      "job_id": "6151",
      "ops": [
        { "OP_ID": "OP_INSTANCE_REPLACE_DISKS", ..., },
        { "OP_ID": "OP_INSTANCE_FAILOVER", ..., },
        ],
    }

    # Second job, runs in parallel with first job
    {
      "job_id": "7687",
      "ops": [
        { "OP_ID": "OP_INSTANCE_MIGRATE", ..., },
        ],
    }

    # Third job, depending on success of previous jobs
    {
      "job_id": "9218",
      "ops": [
        { "OP_ID": "OP_NODE_SET_PARAMS",
          "depend": [
            [6151, ["success"]],
            [7687, ["success"]],
            ],
          "offline": True, },
        ],
    }

### Implementation details

#### Status while waiting for dependencies

Jobs waiting for dependencies are certainly not in the queue anymore and therefore need to change their status from “queued”. While waiting for opcode locks the job is in the “waiting” status (the constant is named `JOB_STATUS_WAITLOCK`, but the actual value is `waiting`). There the following possibilities:

1. Introduce a new status, e.g. “waitdeps”.

   Pro:

   - Clients know for sure a job is waiting for dependencies, not locks

   Con:

   - Code and tests would have to be updated/extended for the new status
   - List of possible state transitions certainly wouldn’t get simpler
   - Breaks backwards compatibility, older clients might get confused

2. Use existing “waiting” status.

   Pro:

   - No client changes necessary, less code churn (note that there are clients which don’t live in Ganeti core)
   - Clients don’t need to know the difference between waiting for a job and waiting for a lock; it doesn’t make a difference
   - Fewer state transitions (see commit `5fd6b69479c0`, which removed many state transitions and disk writes)

   Con:

   - Not immediately visible what a job is waiting for, but it’s the same issue with locks; this is the reason why the lock monitor (`gnt-debug locks`) was introduced; job dependencies can be shown as “locks” in the monitor

Based on these arguments, the proposal is to do the following:

- Rename `JOB_STATUS_WAITLOCK` constant to `JOB_STATUS_WAITING` to reflect its actual meanting: the job is waiting for something

- While waiting for dependencies and locks, jobs are in the “waiting” status

- Export dependency information in lock monitor; example output:

      Name      Mode Owner Pending
      job/27491 -    -     success:job/34709,job/21459
      job/21459 -    -     success,error:job/14513

#### Cost of deserialization

To determine the status of a dependency job the job queue must have access to its data structure. Other queue operations already do this, e.g. archiving, watching a job’s progress and querying jobs.

Initially (Ganeti 2.0/2.1) the job queue shared the job objects in memory and protected them using locks. Ganeti 2.2 (see design document) changed the queue to read and deserialize jobs from disk. This significantly reduced locking and code complexity. Nowadays inotify is used to wait for changes on job files when watching a job’s progress.

Reading from disk and deserializing certainly has some cost associated with it, but it’s a significantly simpler architecture than synchronizing in memory with locks. At the stage where dependencies are evaluated the queue lock is held in shared mode, so different workers can read at the same time (deliberately ignoring CPython’s interpreter lock).

It is expected that the majority of executed jobs won’t use dependencies and therefore won’t be affected.

## Other discussed solutions

### Job-level attribute

At a first look it might seem to be better to put dependencies on previous jobs at a job level. However, it turns out that having the option of defining only a single opcode in a job as having such a dependency can be useful as well. The code complexity in the job queue is equivalent if not simpler.

Since opcodes are guaranteed to run in order, clients can just define the dependency on the first opcode.

Another reason for the choice of an opcode-level attribute is that the current LUXI interface for submitting jobs is a bit restricted and would need to be changed to allow the addition of job-level attributes, potentially requiring changes in all LUXI clients and/or breaking backwards compatibility.

### Client-side logic

There’s at least one implementation of a batched job executor twisted into the `burnin` tool’s code. While certainly possible, a client-side solution should be avoided due to the different clients already in use. For one, the remote API client shouldn’t import non-standard modules. htools are written in Haskell and can’t use Python modules. A batched job executor contains quite some logic. Even if cleanly abstracted in a (Python) library, sharing code between different clients is difficult if not impossible.

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