# RayJob that demonstrates endpoints used by the "persist metadata" workaround:
# - Placement groups (api/v0/placement_groups)
# - Serve applications (api/serve/applications/)
# - Datasets (api/jobs/ + api/data/datasets/{job_id})
#
# Use this with the History Server setup guide (Step 6). Ensure the collector
# has SUPPORT_RAY_EVENT_UNSUPPORTED_DATA enabled so it persists these to blob storage.
# After the job runs, you can view the data in Ray Dashboard, and after cluster
# teardown, via History Server (from persisted meta).
#
apiVersion: ray.io/v1
kind: RayJob
metadata:
  name: rayjob-workaround
spec:
  entrypoint: |
    python -c "
    import ray
    import time

    ray.init()

    # --- 1. Placement group (visible under api/v0/placement_groups) ---
    pg = ray.util.placement_group(
        name='workaround_pg',
        strategy='PACK',
        bundles=[{'CPU': 0.2}]
    )
    ray.get(pg.ready())
    print('Placement group created:', pg.id.hex())

    # --- 2. Serve application (visible under api/serve/applications/) ---
    from ray import serve

    @serve.deployment(num_replicas=1)
    def hello(request):
        return {'message': 'hello from workaround'}

    serve.run(hello.bind(), name='workaround_app')
    print('Serve application deployed: workaround_app')

    # --- 3. Dataset (creates job + dataset; visible under api/jobs/ and api/data/datasets/) ---
    ds = ray.data.range(10)
    n = ds.count()
    print('Dataset created, count:', n)

    # Keep running so the collector can poll Dashboard API and persist meta (e.g. every 5s).
    # Wait long enough for at least one full persist cycle.
    print('Waiting 15s for collector to persist metadata...')
    time.sleep(15)

    # Align with existing guide: allow events to be sent before shutdown.
    time.sleep(5)
    print('Done.')
    "
  clusterSelector:
    ray.io/cluster: raycluster-historyserver