![]() With manual WLM configurations, you’re responsible for defining the amount of memory allocated to each queue and the maximum number of queries, each of which gets a fraction of that memory, which can run in each of their queues. Today, Amazon Redshift has both automatic and manual configuration types. Workload management allows you to route queries to a set of defined queues to manage the concurrency and resource utilization of the cluster. Overall, we observed 26% lower average response times (runtime + queue wait) with Auto WLM. From a throughput standpoint (queries per hour), Auto WLM was 15% better than the manual workload configuration. In this experiment, Auto WLM configuration outperformed manual configuration by a great margin. We synthesized a mixed read/write workload based on TPC-H to show the performance characteristics of a workload with a highly tuned manual WLM configuration versus one with Auto WLM. ![]() In this post, we discuss what’s new with WLM and the benefits of adaptive concurrency in a typical environment. Amazon Redshift dynamically schedules queries for best performance based on their run characteristics to maximize cluster resource utilization. With the release of Amazon Redshift Auto WLM with adaptive concurrency, Amazon Redshift can now dynamically predict and allocate the amount of memory to queries needed to run optimally. How does Amazon Redshift give you a consistent experience for each of your workloads? Amazon Redshift workload management (WLM) helps you maximize query throughput and get consistent performance for the most demanding analytics workloads, all while optimally using the resources of your existing cluster.Īmazon Redshift has recently made significant improvements to automatic WLM (Auto WLM) to optimize performance for the most demanding analytics workloads. Each workload type has different resource needs and different service level agreements. ![]() We also see more and more data science and machine learning (ML) workloads. For example, frequent data loads run alongside business-critical dashboard queries and complex transformation jobs. With Amazon Redshift, you can run a complex mix of workloads on your data warehouse clusters. ![]()
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