Thanks, Vincent. Dashboard visibility is the main point here, so it's good
to hear it lands.

On Fri, 23 Oct 2026 at 08:29, Vincent Beck <[email protected]> wrote:

> Nonetheless I support this idea :) I think it would be great to add
> executor health status on the dashboard. That would give more visibility to
> Airflow users.
>
> On 2026/10/22 19:24:50 Joel Emmanuel wrote:
> > Thanks Vincent, that makes sense. Dropping the 72-hour framing, let's
> just
> > treat this as an open discussion. Appreciate the correction.
> >
> > Joel
> >
> > On Thu, 22 Oct 2026 at 15:17, Vincent Beck <[email protected]> wrote:
> >
> > > It is either a discuss thread or a lazy consensus but not both :) If
> you
> > > want to start a lazy consensus, then the thread subject should call it
> out
> > > with [LAZY CONSENSUS] instead of [DISCUSS]. Though, in this case I
> think a
> > > discuss thread makes more sense since many people do not have the
> context
> > > and 72 hours is a bit rough to make an opinion :)
> > >
> > > On 2026-10-22 18:56 UTC Joel Emmanuel wrote:
> > > > Hi all,
> > > >
> > > > I'm an MLH Fellowship participant working on this with Dennis
> Ferruzzi
> > > > (mentor), posting here per his guidance before any implementation
> starts.
> > > >
> > > > Problem
> > > >
> > > > /api/v2/monitor/health already reports per-instance liveness for
> > > Scheduler,
> > > > Triggerer, and DagProcessor. Each registers itself as a row in the
> Jobs
> > > > table (airflow.jobs.job.Job) with a job_type, hostname, and a
> > > > latest_heartbeat that updates on a timer; the health check queries
> > > > unfinished rows of that job_type and reports which ones are still
> > > > heartbeating. This is the per-instance shape PR #70416 (Jung-Hyun
> Andrew
> > > > Kim) added, with Vincent Beck's #73222 building on top of that work.
> > > >
> > > > Executors don't participate in this at all. There's no
> ExecutorJobRunner
> > > > and no executor job_type anywhere in airflow-core/src/airflow/jobs/.
> > > > BaseExecutor has a heartbeat() method, but it's purely internal: it
> > > drives
> > > > the executor's own sync() loop, and none of that is written anywhere
> the
> > > > health endpoint can read.
> > > >
> > > > In practice, if an executor crashes, gets wedged, or loses its
> connection
> > > > to its backend, there's no dedicated signal for that. Metrics like
> > > > scheduler.executor_heartbeat_duration and executor.open_slots exist
> if
> > > > enabled (they're off by default) and could in theory be pieced
> together
> > > by
> > > > someone watching them, but none of that surfaces a dead executor as
> > > clearly
> > > > as heartbeat staleness already does for Scheduler, Triggerer, and
> > > > DagProcessor. What actually reaches the health endpoint today can
> even be
> > > > misleading: if an executor's heartbeat() call hangs, it shows up as
> "the
> > > > scheduler is hung," which might be true, but isn't necessarily the
> real
> > > > cause.
> > > >
> > > > Proposed approach
> > > >
> > > > The goal is to make a stuck or dead executor show up the same way a
> stuck
> > > > Scheduler or Triggerer already does: through the existing health
> > > endpoint,
> > > > using the existing status / detailed_status / instances shape, so
> > > operators
> > > > have one place to check instead of a special case just for executors.
> > > This
> > > > is meant as a first step, not the whole fix. The longer-term goal is
> > > > letting executors eventually run as independent processes, and this
> > > health
> > > > check is the visibility work that needs to exist before that
> > > restructuring
> > > > can happen safely.
> > > >
> > > > Concretely:
> > > >
> > > > - Dashboard placement: executor health is a new top-level entry
> alongside
> > > > Scheduler/Triggerer/DagProcessor, not nested under Scheduler.
> > > > - Storage: reuse the Jobs table instead of adding a new one. A new
> > > > ExecutorJob row reports state=RUNNING for liveness, the same
> bookkeeping
> > > > pattern the other three components already use, plus a new column on
> the
> > > > Jobs table holding the actual health verdict (ExecutorHealth:
> > > > healthy/unhealthy (reason) / unknown, default unknown). That avoids
> > > forcing
> > > > JobState (RUNNING/SUCCESS/RESTARTING/FAILED) to carry meaning it was
> > > never
> > > > designed for, since none of those values maps cleanly onto executor
> > > status.
> > > > - Mechanism: the scheduler polls each executor on a timer via
> > > > get_executor_health(), a timed call to executor.get_health() that
> runs
> > > in a
> > > > thread with a timeout and skips a poll if the previous one is still
> > > > pending. It's registered next to the existing
> > > _handle_tasks_stuck_in_queued
> > > > timer. Executors reply healthy, unhealthy (with a reason), or
> unknown,
> > > > where unknown is the default so an executor that hasn't implemented a
> > > > health check doesn't show up as dead. No reply at all, though,
> counts as
> > > > unhealthy. Three things here protect the main scheduler loop, which
> must
> > > > never be delayed: the worker thread, the timeout, and the in-flight
> > > guard.
> > > > The scheduler's other periodic actions, like
> > > _handle_tasks_stuck_in_queued,
> > > > run inline on the main loop thread because they only touch the local
> > > > database and return fast. get_executor_health() can't assume that: it
> > > calls
> > > > out to the executor itself, a broker or a cluster API, and that call
> can
> > > > hang for reasons the scheduler doesn't control, so it gets its own
> worker
> > > > thread rather than running inline. The timeout also lives on the
> > > > scheduler's side rather than inside each executor's get_health(). The
> > > > scheduler can't guarantee every executor implementation bounds its
> own
> > > call
> > > > correctly, so a slow or hung executor should never be the one
> deciding
> > > how
> > > > long the main loop waits. Finally, the in-flight guard skips a new
> poll
> > > if
> > > > the previous one hasn't returned yet, so one stuck executor can't
> pile up
> > > > threads by never responding.
> > > > - Executor-instance identity: for Celery, the broker counts as the
> > > > instance. Celery workers are ephemeral and scale dynamically, so
> there's
> > > no
> > > > stable per-worker identity to key off (worker concurrency settings
> cap
> > > how
> > > > many processes run; they don't define an expected worker count). For
> > > > Kubernetes, the pod manager/cluster counts as the instance for the
> same
> > > > reason: pod counts aren't fixed in config either. LocalExecutor is
> out of
> > > > scope for now, since its liveness is just the scheduler's own
> liveness,
> > > so
> > > > a dedicated check wouldn't show anything new.
> > > > - Scope split: PR1 is core scaffolding only, covering the Alembic
> > > migration
> > > > for the new column, the storage and endpoint changes, the
> ExecutorHealth
> > > > enum aggregating into detailed_status, and tests. PR2 adds the
> > > > CeleryExecutor-specific get_health() override, which is the PoC
> target
> > > for
> > > > this round. LocalExecutor and Kubernetes each get a named follow-up
> issue
> > > > instead of a design we'd just be guessing at right now.
> > > >
> > > > What this deliberately doesn't do
> > > >
> > > > - Doesn't design LocalExecutor's or KubernetesExecutor's checks yet.
> > > That's
> > > > a deliberate follow-up, not an oversight.
> > > > - Doesn't pretend the Job-row pattern is a perfect fit just because
> it's
> > > > precedent. It's reused because it avoids duplicating liveness
> bookkeeping
> > > > that Scheduler, Triggerer, and DagProcessor already do, not because
> > > > JobState was ever designed with executors in mind.
> > > > - Doesn't try to solve process separation. This is the visibility
> layer a
> > > > later restructuring would depend on, and it's scoped narrowly on
> purpose.
> > > >
> > > > Posting this under lazy consensus. Will plan to open the first PR
> (core
> > > > scaffolding, no behavior change for existing executors) if there's no
> > > > objection in 72 hours.
> > > >
> > > > Joel
> > > >
> > >
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> > >
> > >
> >
>
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