A Mule application is being designed to receive nightly a CSV file containing millions of records from an external vendor over SFTP. The records from the file need to be validated, transformed, and then written to a database. Records can be inserted into the database in any order.
In this use case, what combination of Mule components provides the most effective and performant way to write these records to the database?
In this use case, the best approach is to use a Batch Job scope to bulk insert records into the database. Batch jobs are specifically designed to handle large datasets in an efficient manner. They provide reliability through persistent queues, ensuring that even in the event of a runtime crash, the processing can resume from where it left off. Furthermore, bulk inserts are significantly more efficient than inserting records one by one, which is crucial when dealing with millions of records. Given that the order of records does not matter, the Batch Job scope is the most suitable choice for optimal performance and reliability.
A. Use a Batch Job scope to bulk insert records into the database Reliability: If you want reliability while processing the records, i.e should the processing survive a runtime crash or other unhappy scenarios, and when restarted process all the remaining records, if yes then go for batch as it uses persistent queues. Memory footprint: Since question said that there are millions of records to process, parallel for each will aggregate all the processed records at the end and can possibly cause Out Of Memory. Batch job instead provides a BatchResult in the on complete phase where you can get the count of failures and success. For huge file processing if order is not a concern definitely go ahead with Batch Job
Correct Answer: A. Use a Batch Job scope to bulk insert records into the database
A is correct according to official practice exam
A is correct answer. Bulk insert in database is more efficient than multiple database insert.
Should be A.