o-nikolas opened a new pull request, #40017:
URL: https://github.com/apache/airflow/pull/40017

   This change delivers the main portion of hybrid executors to the Airflow 
scheduler. Each loop the query for TIs from the DB may contain TIs tagged with 
any executor (or no specific executor, which means run on the default 
executor). We now sort those TIs by executor after the query and queue tasks 
for the executors which have space to take on more TIs.
   
   We now also heartbeat all executors and process their events each scheduler 
loop.
   
   This is a second version of these changes which are fairly concise in source 
code diff. Most of the PR is testing changes/additions.
   
   ## Benchmarking
   Here is some benchmarking to show performance of the scheduler with the 
changes.
   Please refer to the orange (90th quantile) and yellow/green (50th quantile) 
plots, blue is the 99th quantile which leads to very spikey data.
   
   Important plots include the bottom two (which are the critical sections of 
the main loop), as well as the loop duration as a whole.
   
   ### Baseline from main
     
   
![baseline_1](https://github.com/apache/airflow/assets/65743084/20bd7b2b-c4cd-46e9-bed2-ef744720ae8e)
   
   
![baseline_2](https://github.com/apache/airflow/assets/65743084/a6df2f79-698e-4299-8165-d6a9a3fb0942)
   
   ### Hybrid Scheduler
   
   
![hybrid_1](https://github.com/apache/airflow/assets/65743084/69936280-9b13-472d-9866-7d0f7011042a)
   
   
![hybrid_2](https://github.com/apache/airflow/assets/65743084/d9797d8f-aa75-445e-b4dc-d3be11e643dc)
   
   ### Method:
   This data was collected while running the below DAG. Leveraging the standard 
Airflow metrics along with some metric math (for example the scheduler loop 
time minus the executor heartbeat time, middle-left plot), using statsd and 
grafana within the same breeze environment for all data collection.
   
   More samples were taken than just the runs included above, but a 
representative selection was chosen.
   
   ### DAG code:
   
   ```
   from datetime import datetime
   from airflow.models import DAG
   from airflow.operators.bash import BashOperator
   from airflow.operators.empty import EmptyOperator
   
   initial_scale = 4
   max_scale = 5
   scaling_factor = 2
   dag_id = "for_loop_tasks_80"
   with DAG(
       dag_id=dag_id,
       catchup=False,
       schedule_interval="@once",
       start_date=datetime(2023, 1, 1),
       max_active_runs=1,
   ) as dag:
       start = EmptyOperator(task_id="start")
       end = EmptyOperator(task_id="end")
       for i in range(80):
           task = BashOperator(task_id=f"task_{i}", bash_command="date")
           start >> task >> end
   ```
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