# Quick Tip : Validate runMultiple DAG In Fabric

First, if you haven't noticed `mssparkutils` has been officially renamed to `notebookutils`. Check out the [official documentation](https://learn.microsoft.com/en-us/fabric/data-engineering/microsoft-spark-utilities) for details. Be sure to use/update your notebooks to `notebookutils`.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1724426022583/4fdb817b-43aa-4431-910d-f55ded2be9fc.png align="center")

I have [written about runMultiple](https://fabric.guru/using-runmultiple-to-orchastrate-notebook-execution-in-microsoft-fabric) before. It allows you to run multiple notebooks in parallel with a defined orchestration pattern including dependencies. `notebookutils` now also has `.validateDAG` method to check if the DAG has been defined per the expected JSON structure. It can be helpful check before executing `runMultiple`.

## Example:

I will use the same DAG I used in my previous blog.

```json
DAG = {
    "activities": [
        {
            "name": "extract_customers", 
            "path": "extract_customers", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 1000},
        },
        {
            "name": "extract_products", 
            "path": "extract_products", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 5000},
        },
        {
            "name": "extract_offers", 
            "path": "extract_offers", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 1000},
        },
        {
            "name": "extract_leads", 
            "path": "extract_leads", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 100000},
        },
        {
            "name": "customer_table",
            "path": "customer_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_customers"]
        },
        {
            "name": "products_table",
            "path": "products_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_products"]
        },
                {
            "name": "leads_table",
            "path": "leads_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_leads","customer_table", "products_table"]
        },
           {
            "name": "offers_table",
            "path": "offers_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_offers","customer_table", "products_table"]
        },
                   {
            "name": "refresh_dataset",
            "path": "refresh_dataset",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["customer_table","products_table","leads_table","offers_table"]
        }

    ],
    "timeoutInSeconds": 3600, # max 1 hour for the entire pipeline
    "concurrency": 5 # max 5 notebooks in parallel
}

notebookutils.notebook.validateDAG(DAG)
#Output True
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1724427235788/6296da59-362d-4021-9001-6cf8ee6d480a.png align="center")

If I add a dependency that doesn't exist, validation will fail.

```json
INVALID_DAG = {
    "activities": [
        {
            "name": "extract_customers", 
            "path": "extract_customers", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 1000},
        },
        {
            "name": "extract_products", 
            "path": "extract_products", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 5000},
        },
        {
            "name": "extract_offers", 
            "path": "extract_offers", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 1000},
        },
        {
            "name": "extract_leads", 
            "path": "extract_leads", 
            "timeoutPerCellInSeconds": 120,
            "args": {"rows": 100000},
        },
        {
            "name": "customer_table",
            "path": "customer_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["THIS_NOTEBOOK_DOES_NOT_EXIST"] ###INVALID
        },
        {
            "name": "products_table",
            "path": "products_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_products"]
        },
                {
            "name": "leads_table",
            "path": "leads_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_leads","customer_table", "products_table"]
        },
           {
            "name": "offers_table",
            "path": "offers_table",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["extract_offers","customer_table", "products_table"]
        },
                   {
            "name": "refresh_dataset",
            "path": "refresh_dataset",
            "timeoutPerCellInSeconds": 90,
            "retry": 1,
            "retryIntervalInSeconds": 10,
            "dependencies": ["customer_table","products_table","leads_table","offers_table"]
        }

    ],
    "timeoutInSeconds": 3600, # max 1 hour for the entire pipeline
    "concurrency": 5 # max 5 notebooks in parallel
}

notebookutils.notebook.validateDAG(INVALID_DAG)
#Returns error
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1724427390159/56209348-31f0-4e17-a41c-afa6a6d89ed0.png align="center")

Note that validation is not exhaustive. For example, you could enter concurrency as -5 which is invalid as it has to be a positive number but `validateDAG` will not flag it as an error. But this is still very handy.