amoghrajesh opened a new pull request, #46860:
URL: https://github.com/apache/airflow/pull/46860
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closes: #45427
## Why?
- As part of porting all executors to use task SDK and cut down on the tasks
from using DB access, KubernetesExecutor is the last one.
- We want the K8s workers (pods) to use the new supervisor task runner
machinery instead of running "airflow tasks run..."
- The workers will now communicate with the api server which can be deployed
anywhere instead of talking to the DB directly.
## Approach
### Changes to base executor
The interface for "CommandType" has been changed to accept either a sequence
string or workloads.ExecuteTask which will be useful for KubernetesExecutor.
### Changes to K8sExecutor
- Every "task" is recognised as a workload and not a command for airflow 3.
So, the queued tasks list now contains mapping of [TI KEY, workload].
- We override `queue_workload` and `_process_workloads` in lieu of what was
done for celery executor: https://github.com/apache/airflow/pull/46265
The queue_workload is simple, it just enqueues the "workload"
`_process_workloads` calls `execute_async` for every workload present. And
it also pops it from `queued_tasks` and adds it to `running`
- `execute_async` does the usual as it did earlier.
### Changes to K8sExecutor utils
- We have a utility: `run_next` which is supposed to run the "next" task in
the queue in a pod.
- We continue to do that, but now we have a workload and we need to run it
in a pod.
```
token='dummy-token'
ti=TaskInstance(id=UUID('0195178d-1863-7be6-b504-6563a746c9e5'),
task_id='print_date', dag_id='tutorial',
run_id='manual__2025-02-18T05:34:37.633253+00:00_Uvod17gj', try_number=1,
map_index=-1, pool_slots=1, queue='default', priority_weight=3)
dag_rel_path=PurePosixPath('tutorial.py')
bundle_info=BundleInfo(name='dags-folder', version=None)
log_path='dag_id=tutorial/run_id=manual__2025-02-18T05:34:37.633253+00:00_Uvod17gj/task_id=print_date/attempt=1.log'
kind='ExecuteTask'
```
- We need to send this down to a pod and then it needs to run it using the
new task sdk machinery using supervisor.
- Writing this to CLI is not a great option, as it has a token in it which
is not the best option to write to CLI.
- Instead, we serialise the input and mount it in the filesystem of the
created pod. And run something like:
```
command = [
"python",
"-m",
"airflow.sdk.execution_time.execute_workload",
"/tmp/execute/input.json",
]
```
Where `execute_workload` is a new module (discussed later)
### Changes to the pod generator
- We now accept an input: `content_json_for_volume`
- If populated, we create:
EmptyDir volume
A volume mount at: `/tmp/execute/input.json`
Dump the content into this file as received from the K8sExecutor utils
- We also modify the entrypoint now not to run the `/entrypoint`
### New module: `airflow.sdk.execution_time.execute_workload`
- Simple new module that executes an Airflow task using the workload json
provided by a input file.
- It deserialises the json mount, converts to python json object
- Passes it down to supervise function:
```
supervise(
# This is the "wrong" ti type, but it duck types the same. TODO:
Create a protocol for this.
ti=workload.ti, # type: ignore[arg-type]
dag_rel_path=workload.dag_rel_path,
bundle_info=workload.bundle_info,
token=workload.token,
# fallback to internal cluster service for api server
server=conf.get(
"workers",
"execution_api_server_url",
fallback="http://airflow-api-server.airflow.svc.cluster.local:9091/execution/";,
),
log_path=workload.log_path,
)
```
## TODO
- [ ] Update the description with more details about testing etc
- [ ] Discuss populating: `server` in supervisor
- [ ] Testing details
- [ ] Check with Jed for logging solution for K8s pod logs currently used.
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