["python","google-cloud-platform","airflow","google-cloud-composer"]
How to control the parallelism or concurrency of an Airflow installation?

In some of my Apache Airflow installations, DAGs or tasks that are scheduled to run do not run even when the scheduler doesn't appear to be fully loaded. How can I increase the number of DAGs or tasks that can run concurrently? Similarly, if my installation is under high load and I want to limit how quickly my Airflow workers pull queued tasks (such as to reduce resource consumption), what can I adjust to reduce the average load?
Here's an expanded list of configuration options that are available since Airflow v1.10.2. Some can be set on a per-DAG or per-operator basis, but may also fall back to the setup-wide defaults when they are not specified. Options that can be specified on a per-DAG basis: concurrency: the number of task instances allowed to run concurrently across all active runs of the DAG this is set on. Defaults to core.dag_concurrency if not set max_active_runs: maximum number of active runs for this DAG. The scheduler will not create new active DAG runs once this limit is hit. Defaults to core.max_active_runs_per_dag if not set Examples: # Only allow one run of this DAG to be running at any given time dag = DAG('my_dag_id', max_active_runs=1) # Allow a maximum of 10 tasks to be running across a max of 2 active DAG runs dag = DAG('example2', concurrency=10, max_active_runs=2) Options that can be specified on a per-operator basis: pool: the pool to execute the task in. Pools can be used to limit parallelism for only a subset of tasks max_active_tis_per_dag: controls the number of concurrent running task instances across dag_runs per task. Example: t1 = BaseOperator(pool='my_custom_pool', max_active_tis_per_dag=12) Options that are specified across an entire Airflow setup: core.parallelism: maximum number of tasks running across an entire Airflow installation core.dag_concurrency: max number of tasks that can be running per DAG (across multiple DAG runs) core.non_pooled_task_slot_count: number of task slots allocated to tasks not running in a pool core.max_active_runs_per_dag: maximum number of active DAG runs, per DAG scheduler.max_threads: how many threads the scheduler process should use to use to schedule DAGs celery.worker_concurrency: max number of task instances that a worker will process at a time if using CeleryExecutor celery.sync_parallelism: number of processes CeleryExecutor should use to sync task state