Google Sheets: Internal error encountered. Error code: 500

The error "Internal error encountered. Error code: 500" is returned by the Google Sheets API and is typically caused by processing a large volume of data.

To resolve this issue, we recommend adjusting the Batch Size value in your integration. For example, you can set it to 500:

You may also try other values to find the optimal setting for your scenario.

After updating the Batch Size value, please run your integration again and check if the issue is resolved.

For more information, please check this link.

• Related Articles

• Zoho CRM: An error occurred during login operation. Error: invalid_code

  If you encounter the login error An error occurred during login operation. Error: invalid_code while connecting to Zoho CRM, it means the wrong Domain is selected in your Zoho CRM connection. To resolve the issue, open your connection settings, ...

• Mapping the RowNo Column for UPDATE/UPSERT Operations for Google Sheets

  When implementing UPDATE/UPSERT operations in Google Sheets, you may need to set up mapping for the RowNo mandatory field. RowNo is a unique auto-generated row number in the Sheet. It is essential for performing UPSERT/UPDATE operations, as it ...

• Salesforce Connect: Can't Sync the Schema Metadata for Your External System – Check That the Schema Metadata Is Valid. Error Code: Null

  This error occurs because the object used in your endpoint does not have a primary key column. Salesforce requires a primary key for external objects, which is why you encounter an error when validating and syncing your External Data Source in ...

• Zoho CRM: Integration failed: inactive user cannot access the API error

  This error message is returned by the Zoho CRM API. Skyvia connects to data sources through their APIs and therefore is subject to its features and limitations. Unfortunately, there are no additional details, as this is all the information received ...

• Query execution timeout exceeded error

  This error occurs when a query takes too long to execute, typically because it processes a large dataset or uses inefficient filters. To resolve the issue: Apply WHERE filters to reduce the amount of data being queried. Review and optimize JOINs and ...