Hello @yit337 !

I don't think there is a job level max_concurrent_task_runs setting for normal DAG tasks.

But there are 2 different concepts:

1. you can limit concurrent runs of the same job

resources:
  jobs:
    my_job:
      name: my_job
      max_concurrent_runs: 1

here you can limit how many runs of the same job can overlap. It does not limit how many tasks run in parallel inside one job run. https://docs.databricks.com/aws/en/jobs/configure-job

2. you can limit parallel iterations of a repeated task and use a For each task and set concurrency:

tasks:
  - task_key: process_items
    for_each_task:
      inputs: '["A", "B", "C", "D"]'
      concurrency: 2
      task:
        task_key: process_one_item
        notebook_task:
          notebook_path: ../src/process_one_item.py

 https://docs.databricks.com/aws/en/dev-tools/bundles/job-task-types

For normal separate tasks in the same job, concurrency is controlled by the DAG since tasks without dependencies can run in parallel and tasks with depends_on run after their dependencies. So to limit parallelism, you can group tasks into waves using dependencies. https://docs.databricks.com/aws/en/jobs/control-flow