amoghrajesh opened a new pull request, #45106:
URL: https://github.com/apache/airflow/pull/45106
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closes: #44351
"Retries" are majorly handled in airflow 2.x in here:
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L3082-L3101.
The idea here is that in case a task is retry able, defined by
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1054-L1073,
the task is marked as "up_for_retry". Rest of the part is taken care by the
scheduler loop normally if the ti state is marked correctly.
Coming to task sdk, we cannot perform validations such as
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1054-L1073
in the task runner / sdk side because we do not have/ should not have access
to the database. Instead of having API handler and states for "up_for_retry",
we can handle it when we are handling failures - which we do by calling the
https://github.com/apache/airflow/blob/main/airflow/api_fastapi/execution_api/routes/task_instances.py#L160-L212
API endpoint. If we can send in enough data to the api handler in the
execution API, we should be able to handle the cases of retry well.
### What needs to be done for porting this to `task_sdk`?
1. Defining "try_number", "max_retries" for task instances ---> not needed
because this is handled already in the scheduler side of things / parsing time
and not at execution time, so we do not need to handle it. It is handled here
https://github.com/apache/airflow/blob/main/airflow/models/dagrun.py#L1445-L1471
when a dag run is created and it is initialised with the initial values:
max_tries(https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1809)
and
try_number(https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1808)
2. We need to have a mechanism that can send a signal from the task runner
if retries are defined. We will send this in this fashion:
task runner informs the supervisor while failing that it needs to retry ->
supervisor sends a normal request to the client (but with task_retries defined)
-> client sends a normal API request (TITerminalStatePayload) to the execution
API but with task_retries
3. At the execution API, we receive the request and perform a check to check
if the Ti is eligible for retry, if it is, we mark it as "up_for_retry", the
rest of things are taken care by the scheduler.
### Testing results
Right now the PR is meant to handle `BaseException` -- will extend to all
other eligible TI exceptions in follow ups.
#### Scenario 1: With retries = 3 defined.
DAG:
```
import sys
from time import sleep
from airflow import DAG
from airflow.providers.standard.operators.python import PythonOperator
from datetime import datetime, timedelta
from airflow.exceptions import AirflowTaskTimeout
def print_hello():
1//0
with DAG(
dag_id="abcd",
schedule=None,
catchup=False,
tags=["demo"],
) as dag:
hello_task = PythonOperator(
task_id="say_hello",
python_callable=print_hello,
retries=3
)
```
Rightly marked as "up_for_retry"
TI details with max_tries
Try number in grid view
#### Scenario 2: With retries not defined.
DAG:
```
import sys
from time import sleep
from airflow import DAG
from airflow.providers.standard.operators.python import PythonOperator
from datetime import datetime, timedelta
from airflow.exceptions import AirflowTaskTimeout
def print_hello():
1//0
with DAG(
dag_id="abcd",
schedule=None,
catchup=False,
tags=["demo"],
) as dag:
hello_task = PythonOperator(
task_id="say_hello",
python_callable=print_hello,
)
```
Rightly marked as "failed"
<img width="1724" alt="image"
src="https://github.com/user-attachments/assets/d5cce08c-39b1-499d-a87d-43d2cc2e074b";
/>
Ti detiails with 0 max_tries:
<img width="1724" alt="image"
src="https://github.com/user-attachments/assets/9a862cc5-6310-4f33-a5cc-a0759bc72b1f";
/>
Try number in grid view
<img width="1724" alt="image"
src="https://github.com/user-attachments/assets/6b0811d7-d01e-4bb4-92e4-98f679e6f935";
/>
============
### Pending:
- [ ] UT coverage for execution API for various scenarios
- [ ] UT coverage for supervisor and task_runner, client
- [ ] Extending to various other scenarios when retry is needed -- eg:
AirflowTaskTimeout / AirflowException
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