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Automated Workload Management Comes to AWS Redshift via Machine Learning

Machine learning is being used to power the automatic management of workloads for the Amazon Redshift data warehouse.

AWS recently announced Automatic workload management (WLM) for Redshift, providing the ability to dynamically manage memory and query concurrency to boost query throughput.

Users can enable concurrency scaling for a query queue to a virtually unlimited number of concurrent queries, AWS said, and can also prioritize important queries.

"By setting query priorities, you can now ensure that higher priority workloads get preferential treatment in Redshift including more resources during busy times for consistent query performance," AWS said last week. "Automatic WLM uses intelligent algorithms to make sure that lower priority queries don't stall, but continue to make progress. For more information, see Query Priority."

The cloud giant is advising all customers who manually manage their workloads to switch to Automatic WLM. Guidance on doing that is available in "Implementing Automatic WLM."

About the Author

David Ramel is an editor and writer for Converge360.

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