5

I am trying to figure out why I get the following error, when I use the Databricks Job API.

{ "error_code": "INVALID_PARAMETER_VALUE", "message": "Cluster validation error: Missing required field: settings.cluster_spec.new_cluster.size" }

What I did:

  1. I created a Job running on a single node cluster using the Databricks UI.
  2. I copy& pasted the job config json from the UI.
  3. I deleted my job and tried to recreate it by sending a POST using the Job API with the copied json that looks like this:
{
    "new_cluster": {
        "spark_version": "7.5.x-scala2.12",
        "spark_conf": {
            "spark.master": "local[*]",
            "spark.databricks.cluster.profile": "singleNode"
        },
        "azure_attributes": {
            "availability": "ON_DEMAND_AZURE",
            "first_on_demand": 1,
            "spot_bid_max_price": -1
        },
        "node_type_id": "Standard_DS3_v2",
        "driver_node_type_id": "Standard_DS3_v2",
        "custom_tags": {
            "ResourceClass": "SingleNode"
        },
        "enable_elastic_disk": true
    },
    "libraries": [
        {
            "pypi": {
                "package": "koalas==1.5.0"
            }
        }
    ],
    "notebook_task": {
        "notebook_path": "/pathtoNotebook/TheNotebook",
        "base_parameters": {
            "param1": "test"
           
        }
    },
    "email_notifications": {},
    "name": " jobName",
    "max_concurrent_runs": 1
}

The documentation of the API does not help (can't find anything about settings.cluster_spec.new_cluster.size). The json is copied from the UI, so I guess it should be correct.

Thanks for your help.

user7454972
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1 Answers1

7

Source: https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/clusters#--create

To create a Single Node cluster, include the spark_conf and custom_tags entries shown in the example and set num_workers to 0.

{
  "cluster_name": "single-node-cluster",
  "spark_version": "7.6.x-scala2.12",
  "node_type_id": "Standard_DS3_v2",
  "num_workers": 0,
  "spark_conf": {
    "spark.databricks.cluster.profile": "singleNode",
    "spark.master": "local[*]"
  },
  "custom_tags": {
    "ResourceClass": "SingleNode"
  }
}
memoryz
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