AWS Glue Resources Allocation

AWS Glue Auto Scaling can indeed provide advantages for your use case, even with a relatively low-volume workload of processing one CSV file per day and inserting records into a MySQL RDS table. Here are some potential benefits:

1. Resource Optimization: Auto Scaling dynamically adjusts the number of workers based on the workload. This means that for your daily job, it will allocate resources as needed during the processing of the CSV file and inserting data into MySQL, potentially using fewer resources than a fixed DPU setup.

2. Cost Efficiency: With Auto Scaling, you only pay for the resources you actually use. If your job doesn't require the full capacity of your current fixed DPU setup, Auto Scaling could help reduce costs by scaling down when the full capacity isn't needed.

3. Handling Variability: If the size of your daily CSV file varies, Auto Scaling can adapt to these changes automatically. On days with larger files or more complex data, it can scale up to maintain performance, while scaling down on days with smaller or simpler files.

4. Future-Proofing: As your data processing needs grow or become more complex over time, Auto Scaling can automatically accommodate these changes without you needing to manually adjust your resource allocation.

However, there are some considerations and potential disadvantages to keep in mind:

1. Minimum Resource Allocation: For very small workloads, you might still end up using more resources than strictly necessary due to the minimum allocation of Auto Scaling.

2. Potential for Slower Start: There might be a slight delay in processing as Auto Scaling determines the initial resource allocation, which could be noticeable for short-running jobs.

3. Complexity: Auto Scaling adds another layer of complexity to your ETL process, which might not be necessary for very predictable, low-volume workloads.

4. Monitoring Challenges: It may be more difficult to predict or control exact resource usage and costs, requiring more careful monitoring.

You might want to avoid using Auto Scaling in scenarios where:

1. You have very consistent, predictable, and low-volume workloads where a fixed DPU setup is already optimized.
2. You need precise control over resource allocation for compliance or performance reasons.
3. Your jobs are very short-running, and the potential delay in resource allocation could impact overall job duration significantly.

In your specific case, given the low volume (one file per day), you should carefully evaluate whether the benefits of Auto Scaling outweigh the potential complexities. It might be worth testing both approaches to compare performance, resource utilization, and cost to make an informed decision.

Sources
Using auto scaling for AWS Glue - AWS Glue
Auto Scaling in AWS Glue interactive sessions is now generally available - AWS
Serverless Data Integration – AWS Glue Features – AWS
AWS Glue streaming autoscaling - AWS Glue

Hello Saiyantan, In the scenario you described fixed DPU would work fine. However if you are doing large aggregates and transformations that will have multiple spark stages, then having Auto scaling will help to do aggregations during these large transformations and scale down after the aggregation. Also look at using Flex execution if the process is not SLA sensitive to optimize cost.