Amazon Redshift announces automatic workload management and query priorities
Amazon Redshift now makes it easy to maximize query throughput and get consistent performance for your most demanding analytics workloads. Automatic workload management (WLM) uses machine learning to dynamically manage memory and concurrency helping maximize query throughput. In addition, you can now easily set the priority of your most important queries, even when hundreds of queries are being submitted.
Amazon Redshift now makes it easy to maximize query throughput and get consistent performance for your most demanding analytics workloads. Automatic workload management (WLM) uses machine learning to dynamically manage memory and concurrency helping maximize query throughput. In addition, you can now easily set the priority of your most important queries, even when hundreds of queries are being submitted.
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. 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.
If you manually manage your workloads, we recommend that you switch to automatic WLM. Start by creating a new parameter group for automatic WLM. For more information, see Implementing Automatic WLM.
You can also enable concurrency scaling for any query queue to scale to a virtually unlimited number of concurrent queries, with consistently fast query performance. To learn more about concurrency scaling, see Working with Concurrency Scaling.
Automatic WLM with query priority is now available with cluster version 1.0.9459, or later. Refer to the AWS Region Table for Amazon Redshift availability.