11/21/2023 0 Comments Redshift wlm queue rules![]() ![]() The maximum total concurrency level for all user-defined queues, not including the reserved Superuser queue, is 50. The decision of where to put a query is independent of how busy a queue is queries are allocated based on the rules you've set up: A query arrives and is designated to the "less loaded" queue, and it waits for its turn to be resolved. I think your understanding of query queues is a little off.Ī queue is like a thread in Java. So, if we have understood correctly, having that some viewes make up to 25-28 queries, and the total amount of loading time is around 60s, how can we leave the settings the queries can be resolved faster? We have tried to have 3 queues, each one with concurrecy 5, but performance still slow. The more concurrency a queue has, the less memory in each query slot it has. It's related to concurrency as we understood it. By default is 5.Īnd a query slot is the amount of memory a query can use. But in short-term, that's a lot of classification work in our queries we can't do right now.Ī concurrency is the amount of queries that a queue can run in parallel. In a queue we can assign user groups or queries groups. A queue has some memory allocated (we guess divided equally?) We have understood this:Ī queue is like a thread in Java. There are 3 main things to take into account: query slots, concurrency and queues. That redshift by default receive 5 queries at same time, but that is a setting we can change. So we have prepared the necessary queries for each view to be run parallel from our EC2 app machine, to our Redshift DWH, and run the threads but views still take long to load, sometimes even longer. Some views were taking more than a minute to finish queries and load the info in screen, so we decided to use threads in Java and make the queries asynchronous. The backend is built in Java Server Faces (JSF), and before, the queries were made lineal. We are building a business intelligence system, and we have a huge PostgreSQL Database (DB) where we make all the info processing, and a Redshift Data Warehouse (DWH) where we store the data and execute queries. ![]()
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