redshift wlm queue

By default, Amazon Redshift defines two queues One Superuser queue, which is reserved only for the superuser role and it cannot be configured. Intermediate Storage. From AWS docs, In each queue, WLM creates a number of query slots equal to the queue’s concurrency level. Our initial recommendation is for 2 WLM queues: a queue for the segment query group with a concurrency of 10. leave the default queue with a concurrency of 5 Though, it doesn't say that the default queue can't have any User or Query groups. SELECT * FROM wlm_queue_state_vw; This is the default queue setup and what you would see on a stock Redshift cluster: Here is what a custom queue would look like: The custom queue is a three-queue setup that allows for a timeout (max_time) on the fastest queue and moves anything slower to a second queue. A few notable things about Redshift Workload Management, The principal mechanism that allows the management of resources is the definition of priorities queues for the execution of queries. Query throughput per WLM queue – The average number of queries completed per second for a WLM queue. The closest I could find was this. Be sure to keep enough space on disk so those queries can complete successfully. It comes with many other things. Amazon Redshift WLM Queue Time and Execution Time Breakdown - Further Investigation Broken Down by Hour Posted by Tim Miller Once you have determined a day that has shown significant load on your WLM Queue, let’s break it down further to determine a time of the day. In Redshift, when scanning a lot of data or when running in a WLM queue with a small amount of memory, some queries might need to use the disk. For us, the sweet spot was under 75% of disk used. The default queue must be the last queue in the WLM configuration. you have the option of changing the percentage of memory assigned to each queue by setting wlm memory percent parameter. the rate for all the queues adds up to 100%. It resulted in multiplying the concurrent connections to Redshift by two, and a high load on the queue dedicated to analysts, neither fitting the current WLM strategy, therefore breaking our SLAs. How AWS handles this dynamic WLM? The Redshift WLM has two fundamental modes, automatic and manual. But yeah, I agree that the Redshift documentation lacks clarity on this. Design a better WLM strategy and monitor it thoroughly. The maximum concurrency that Redshift supports is 50 across all query groups, and resources like memory are distributed evenly across all those queries. There is a solution already available on AWS’s RedShift utilities, but its not a sperate package. The automatic mode provides some tuning functionality, like setting priority levels for different queues, but Redshift tries to automate the processing characteristics for workloads as much as possible. Within the intermix.io dashboard, viewing Recommendations for an individual query will surface the exact touched tables and how to update them, as well as how much memory the query used and the amount of memory capacity in the WLM queue. Improve our schema design: Query duration per WLM queue – The average length of time to complete a query for a WLM queue. Usage limit for Redshift Spectrum – Redshift Spectrum usage limit. We identified a few levers. Usage limit for concurrency scaling – Concurrency scaling usage limit. Any queries that are not routed to other queues run in the default queue. If you want to setup your own dynamic WLM, then this blog will help you. This blog will help you the default queue keep enough space on disk so those queries lacks... 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