#REDSHIFT WLM QUEUE MANUAL#

With adaptive concurrency, Amazon Redshift uses ML to predict and assign memory to the queries on demand, which improves the overall throughput of the system by maximizing resource utilization and reducing waste.Įlectronic Arts, Inc. One of our main innovations is adaptive concurrency.

#REDSHIFT WLM QUEUE MANUAL#

Over the past 12 months, we worked closely with those customers to enhance Auto WLM technology with the goal of improving performance beyond the highly tuned manual configuration. However, in a small number of situations, some customers with highly demanding workloads had developed highly tuned manual WLM configurations for which Auto WLM didn’t demonstrate a significant improvement. Our initial release of Auto WLM in 2019 greatly improved the out-of-the-box experience and throughput for the majority of customers. Query priorities lets you define priorities for workloads so they can get preferential treatment in Amazon Redshift, including more resources during busy times for consistent query performance, and query monitoring rules offer ways to manage unexpected situations like detecting and preventing runaway or expensive queries from consuming system resources. Optionally, you can define queue priorities in order to provide queries preferential resource allocation based on your business priority.Īuto WLM also provides powerful tools to let you manage your workload. Auto WLM adjusts the concurrency dynamically to optimize for throughput. Amazon Redshift Auto WLM doesn’t require you to define the memory utilization or concurrency for queues. Manual WLM configurations don’t adapt to changes in your workload and require an intimate knowledge of your queries’ resource utilization to get right. 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.

Redshift wlm queue