![]() ![]() ![]() Which queue does it get routed to? A default queue helps in such a case. Now, a User from another User group, say UG5, queries Redshift. Let's say you have four queues, each of which has a different User group, say, UG1, UG2, UG3 and UG4. one which doesn't have any User or Query groups) as the last one. However, it is easy to guess why Redshift requires a default queue (i.e. Previously, I had four queues, each with an assigned user group I am not sure about this statement of yours: I didn't see an update on their official blog, I don't see this mentioned in the docs, or the doc updates (, ) This is blocking me from making other desired changes to the existing queues. Now, I have no problem creating this new queue, but I would really like to see an explicit explanation of what it does/what its affects are before I do it. It didn't add up to 100%, and supposedly the rest was dynamically managed by redshift. My main question is where is this need for a new "non-user" queue explained? This is definitely a change because previously, I had four queues, each with an assigned user group, and their collective memory allocation was 87%. That's annoying because adding a new queue requires cluster reboot, but that's not the reason I'm asking this question. Now, the obvious solution is to just create a new queue with no user group specified and give it the remaining amount of memory so that it all adds up to 100%. The final queue may not contain User Groups or Query Groups. The following problems must be corrected before you can save this workload configuration: Here are my config files.I want to update the WLM configuration for my Redshift cluster, but I am unable to make changes and save them due to the following message displayed: 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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