cancel
Showing results forย 
Search instead forย 
Did you mean:ย 
Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
cancel
Showing results forย 
Search instead forย 
Did you mean:ย 

Run failed with error message Failed to resolve references: Parameter values exceed the size limit

jeremy98
Honored Contributor

Hi community,

My team and I are facing an issue with the Parameter Values (see the title of this discussion) being passed through each task of a job. Unfortunately, this causes our job run to fail.

Do you have any suggestions on how to handle parameters that are too large during execution?

I was considering storing the JSON file in a volume and deleting it afterward, but perhaps you know of a better solution.

Kind regards,

2 REPLIES 2

Advika
Databricks Employee
Databricks Employee

Hello @jeremy98!

Yes, agree with your workaround. Persist the data in a file and pass the path instead of the full JSON to avoid parameter size issues.

szymon_dybczak
Esteemed Contributor III

We're dealing with this issue on our project in following way:

- we have defined config JSON file (could also be YAML - doesn't matter)

- and now let's say that you have param that has really long value - for the sake of example let's consider parameters related to tables that we want to load

So, we can definde our json config 

 

 

config = [
{
    "really_important_table": { 
        "table_name": "some_table_name", 
        "source_file_format": "json", 
        "data_lake_target_folder_name": "sample_target", 
        "data_source_path": "source_path", 
        "transform_function_name": 
            "function_name", 
            "autoloader_options": { 
                "cloudFiles.resourceGroup": "rg_name" 
            }, 
        "clean_bronze": False 
    }
},
{
        "table2": { 
        "table_name": "some_table_name2", 
        "source_file_format": "json", 
        "data_lake_target_folder_name": "folder_name", 
        "data_source_path": "src_path", 
        "transform_function_name": "transform_function", 
        "autoloader_options": { 
            "cloudFiles.resourceGroup": "rg_2" 
        }, 
        "clean_bronze": False 
    }     
}    
]

Now you need to define python module that will read the content of this config file and will return config based on a provided key.

So for example, let's say you need to process really_important_table config. Then in your workflow you need to just pass really_important_table key and in your notebook/code use your module to get you a proper value associated with this key. 

Join Us as a Local Community Builder!

Passionate about hosting events and connecting people? Help us grow a vibrant local communityโ€”sign up today to get started!

Sign Up Now