Questions and Answers
The maximum number of allocated executors in AWS Glue is represented by a vertical blue line on the graph.
False
Amazon CloudWatch Logs insights can help optimize the appropriate number of DPUs needed for a Glue job.
False
Using job bookmarks in AWS Glue is effective for tracking DPU consumption.
False
To determine the appropriate DPU capacity, one should examine past job runs in the Job run monitoring section of the AWS Glue console.
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Monitoring the AWS Glue performance can only be done through the Glue console and not via Amazon CloudWatch.
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Study Notes
AWS Glue Executor Allocation
- The maximum number of allocated executors is shown as a horizontal red line on the graph, corresponding to the number of designated Data Processing Units (DPUs).
- In the scenario described, 10 DPUs are allocated for the job.
Job Metrics and Monitoring
- The AWS Glue console provides metrics on job performance, including executor allocation based on DPUs.
- To analyze job performance in detail, select a job run and choose ‘View run metrics’ in the Glue console.
DPU Capacity Assessment
- The Job Run Monitoring section allows users to assess the necessary DPU capacity by reviewing previous job runs.
- Analyzing past job runs provides insights into the optimal DPU allocation for future jobs.
Common Misconceptions
- Using Amazon CloudWatch Logs to review job logs for “DPU” is not suitable for optimizing DPU numbers, as it examines aggregate job details rather than specific capacity needs.
- The ETL section in the AWS Glue console offers only a high-level overview; it lacks the in-depth analysis required for optimizing DPU usage.
- Job bookmarks are not intended for tracking DPU consumption; they are designed to ensure jobs only process new or updated data by maintaining state information about previously processed data.
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Description
This quiz focuses on interpreting the AWS Glue console's job metrics, specifically regarding executor allocation and Data Processing Units (DPUs). Learn how to analyze job performance by selecting and viewing run metrics for a deeper understanding of the job execution details.
