AWS Certified Data Engineer – AssociateData Operations and MonitoringMedium
A data engineering team uses AWS Glue ETL jobs to process large datasets daily. Recently, they observed that some Glue jobs are taking significantly longer to complete, occasionally leading to missed SLAs. Upon investigation, they found that the CPU utilization of the Glue job workers is consistently low, while the I/O Wait time is frequently high, indicating that the jobs are bottlenecked by data access. The data is stored in Amazon S3. What is the most effective approach to optimize these Glue jobs by addressing the I/O bottleneck?
- ARe-partition the data in Amazon S3 based on frequently queried columns and ensure the Glue jobs use predicate pushdown.
- BEnable adaptive query execution and join reordering features within the Glue job scripts.
- CMigrate the data from Amazon S3 to Amazon EBS volumes attached to EC2 instances and process it there.
- DIncrease the number of Glue ETL workers and allocate more DPU (Data Processing Units) to each worker.
Show answer & explanationAnswer & explanation
Correct answer: A. Re-partition the data in Amazon S3 based on frequently queried columns and ensure the Glue jobs use predicate pushdown.
High I/O Wait time with low CPU utilization often indicates that the job is spending too much time reading data. Re-partitioning data in S3 reduces the amount of data scanned, and predicate pushdown ensures that Glue only reads the necessary partitions, significantly improving I/O performance and overall job execution time.
Why the other options are wrong
- B. Adaptive query execution and join reordering optimize CPU-bound query processing and join operations but do not directly address I/O bottlenecks related to data scanning from S3.
- C. Migrating data from S3 to EBS is a significant architectural change and generally not a recommended practice for large-scale data processing in a serverless ETL environment like Glue, which is optimized for S3.
- D. Increasing workers/DPUs would help with CPU-bound tasks or if the job was processing more data in parallel, but it won't resolve an I/O bottleneck if data access is the limiting factor.
Glue I/O Optimization
Optimizing I/O-bound AWS Glue ETL jobs, especially when data is in S3, involves reducing the amount of data read by partitioning and leveraging predicate pushdown.
- High I/O Wait and low CPU indicate I/O bottleneck.
- Partitioning S3 data reduces data scanned.
- Predicate pushdown filters data at the source.
- Improves job performance and reduces costs.
Memory trick: Slow Glue Job? Check your S3 partitions, then push down your predicates to speed up the data flow.