Complex variable cant reference complex variable

[dinjazelena]

Hey, so i have a job with a lot of tasks and its same for every target, only difference between targets is the parameters of first task. Since i want to put tasks definitions as a complex variable i have to find a way to pass parameters of task as variable as well.

Will referencing complex variable within complex variable be possible? Do u have idea how to tackle this problem?

Is it possible to do smth like this?

    resources:
      jobs:
        inference:
          name: "${bundle.name}-${var.segment}-inference"
          job_clusters:
            - job_cluster_key: inference
              new_cluster: ${var.inference_cluster}
          tasks: 
               - task_key: sensor
                   ....
                ${var.inference_tasks}

[ribugent]

Similar issue here, before the complex variables we wrote a lot of boilerplate code to declare all job clusters.

We wanted to reduce as much as possible the boilerplate and we tried this before discovering this limitation:

variables:
  cluster_xxl:
    description: Spark big cluster
    type: complex
    default:
      spark_version: ${var.spark_version}
      spark_conf: ${var.spark_conf}
      num_workers: 1
      aws_attributes: ${var.aws_attributes}
      node_type_id: m6g.2xlarge
      spark_env_vars: ${var.spark_env_vars}
      enable_local_disk_encryption: true

  cluster_xl:
    description: Spark medium cluster
    type: complex
    default:
      spark_version: ${var.spark_version}
      spark_conf: ${var.spark_conf}
      num_workers: 1
      aws_attributes: ${var.aws_attributes}
      node_type_id: m6g.xlarge
      spark_env_vars: ${var.spark_env_vars}
      enable_local_disk_encryption: true

# and we have more ....

We fixed it by duplicating the values of referenced complex variables, but it would be nice if you could remove this limitation.

Thanks

[pietern]

@dinjazelena

i have a job with a lot of tasks and its same for every target

If the job is the same for every target, can't it be defined without variables? You could still use a complex variable for the parameters of the first task and specify overrides for this variable to customize it per target.

resources:
  jobs:
    job_with_parameters:
      name: job_with_parameters

      tasks:
        - task_key: task_a
          spark_python_task:
            python_file: ../src/task_a.py
            parameters: ${var.first_task_parameters}

        - task_key: task_b
          depends_on:
            - task_key: task_a
          spark_python_task:
            python_file: ../src/task_a.py
            parameters:
              - "--foo=bar"

To customize the parameters, you can define a different value per target:

targets:
  dev:
    variables:
      first_task_parameters:
        - "--mode=dev"
        - "--something=else"

  prod:
    variables:
      first_task_parameters:
        - "--mode=prod"
        - "--hello=world"

[pietern]

@ribugent Thanks for commenting on your use case, we'll take it into consideration.

I agree it would be nice and in line with expectations, but it takes a bit of an investment to make it possible, and as such we need to trade off the priority. First in line was getting complex variables out in their current form.

[dinjazelena]

Hi,@pietern, thanks for help. I need to isolate each job as a target since i have different jobs that are same for each target but deployed with different owners, and since DAB tries to deploy everything from resources, i have to isolate each job as target.

[andrewnester]

One other potential option here can be to use YAML anchors in combination with complex variables. YAML anchors can help with reusing duplication of configuration for common parts of complex variables

[dgarridoa]

Same issue here,

I am trying to create two cluster that share most of their definition except the runtime. These are used in different jobs. As an example:

variables:
  catalog:
    default: hive_metastore
  spark_conf:
    default:
      spark.databricks.sql.initial.catalog.name: ${var.catalog}
  etl_cluster_config:
    type: complex
    default:
      spark_version: 14.3.x-scala2.12
      runtime_engine: PHOTON
      spark_conf: ${var.spark_conf}
  ml_cluster_config:
    type: complex
    default:
      spark_version: 14.3.x-cpu-ml-scala2.12
      spark_conf: ${var.spark_conf}

If is there another way to do these let me know.

Thanks!