Redshift wlm queue11/28/2023 ![]() 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. New - Deliver Interactive Real-Time Live Streams with Amazon IVS 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. Here are my config files.With Amazon Redshift, you can run a complex mix of workloads on your data warehouse clusters. Similarly, one config file the next set of config and upload to S3. Just copy that and upload it to the S3 bucket. Then you can get the JSON content from the WLM window. I recommend you that instead of manually typing this configuration values, just create a new parameter group with your queues, QMR rules, Concurrency scaling and etc. You can use the same logic for Auto WLM as well to change the priority. So Im my lambda function, I’ll get the current hour, based on that it’ll decide when configuration should be applied. I don’t want to use 2 different lambda functions for this. So I need to trigger the lambda function 2 times in a day. Then After 8 AM to 6 PM, it is heavily used by BI users. ![]() So I want to allocate almost all the memory to the ETL users group. I had a requirement that all of the ETL processes are running from 12 AM to around 6 AM. We are using manual WLM, and we know the workload very well. So we’ll never face any downtime while changing this. At the same time, Amazon Redshift ensures that total memory usage never exceeds 100 percent of available memory. Thus, active queries can run to completion using the currently allocated amount of memory. If you change the memory allocation or concurrency, Amazon Redshift dynamically manages the transition to the new WLM configuration. The amount of memory allocated to a query slot equals the percentage of memory allocated to the queue divided by the slot count. In each queue, WLM creates a number of query slots equal to the queue’s concurrency level. If you want to setup your own dynamic WLM, then this blog will help you. There is a solution already available on AWS’s RedShift utilities, but its not a sperate package. But if you want to dynamically change the Memory and the Concurrency for a manual WLM then you use AWS Lambda. They follow the same pattern as night time ETL, morning BI users, and so on. It’s a very good choice for a standard cluster like not much difference in the workload. ![]() Its using ML algorithms internally to allocate the resources. Auto WLM will be allocating the resources and the concurrency dynamically based on past history. Redshift doesn’t support Dynamic WLM natively. RedShift RedShift Dynamic WLM With Lambda ![]()
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