jiaweigoh opened a new issue, #29690:
URL: https://github.com/apache/airflow/issues/29690
### Apache Airflow version
Other Airflow 2 version (please specify below)
### What happened
Hi guys, we noticed that our Airflow DB (GCP CloudSQL Postgres) is often at
100%, with 70% consumed by a particular query. Checked via pg_stat_statements,
Google's Query Insight tells us the same thing.
We are using the most basic set-up, 1 vcpu core, 3.75gb ram.
While we figure out what Airflow configs to tune, and whether to scale up
our DB instance, i wanted to figure out what this query achieves. I checked
that the filter conditions are indexed in Airflow DB, so its shocking for a
single query type to consume so much CPU.
```
SELECT task_instance.try_number AS task_instance_try_number,
task_instance.task_id AS task_instance_task_id, task_instance.dag_id AS
task_instance_dag_id, task_instance.run_id AS task_instance_run_id,
task_instance.start_date AS task_instance_start_date, task_instance.end_date AS
task_instance_end_date, task_instance.duration AS task_instance_duration,
task_instance.state AS task_instance_state, task_instance.max_tries AS
task_instance_max_tries, task_instance.hostname AS task_instance_hostname,
task_instance.unixname AS task_instance_unixname, task_instance.job_id AS
task_instance_job_id, task_instance.pool AS task_instance_pool,
task_instance.pool_slots AS task_instance_pool_slots, task_instance.queue AS
task_instance_queue, task_instance.priority_weight AS
task_instance_priority_weight, task_instance.operator AS
task_instance_operator, task_instance.queued_dttm AS task_instance_queued_dttm,
task_instance.queued_by_job_id AS task_instance_queued_by_job_id,
task_instance.pid AS t
ask_instance_pid, task_instance.executor_config AS
task_instance_executor_config, task_instance.external_executor_id AS
task_instance_external_executor_id, task_instance.trigger_id AS
task_instance_trigger_id, task_instance.trigger_timeout AS
task_instance_trigger_timeout, task_instance.next_method AS
task_instance_next_method, task_instance.next_kwargs AS
task_instance_next_kwargs, dag_run_1.state AS dag_run_1_state, dag_run_1.id AS
dag_run_1_id, dag_run_1.dag_id AS dag_run_1_dag_id, dag_run_1.queued_at AS
dag_run_1_queued_at, dag_run_1.execution_date AS dag_run_1_execution_date,
dag_run_1.start_date AS dag_run_1_start_date, dag_run_1.end_date AS
dag_run_1_end_date, dag_run_1.run_id AS dag_run_1_run_id,
dag_run_1.creating_job_id AS dag_run_1_creating_job_id,
dag_run_1.external_trigger AS dag_run_1_external_trigger, dag_run_1.run_type AS
dag_run_1_run_type, dag_run_1.conf AS dag_run_1_conf,
dag_run_1.data_interval_start AS dag_run_1_data_interval_start,
dag_run_1.data_interval_end A
S dag_run_1_data_interval_end, dag_run_1.last_scheduling_decision AS
dag_run_1_last_scheduling_decision, dag_run_1.dag_hash AS dag_run_1_dag_hash
FROM task_instance JOIN dag_run AS dag_run_1 ON dag_run_1.dag_id =
task_instance.dag_id AND dag_run_1.run_id = task_instance.run_id
WHERE task_instance.dag_id = ? AND task_instance.task_id = ? AND
task_instance.run_id = ?
LIMIT ? FOR UPDATE
```
Thanks!
### What you think should happen instead
_No response_
### How to reproduce
NA
### Operating System
Ubuntu
### Versions of Apache Airflow Providers
Airflow version: 2.2.3
Postgres: 9.6
### Deployment
Other Docker-based deployment
### Deployment details
_No response_
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of
Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
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