![]() You can add your custom queues and assign them query monitoring rules created manually or using templates. To create a custom queue and QMR you need to open Amazon Redshift service page, on the left side panel choose CONFIG > Workload management, hit ` Create` button to create your custom parameter group, then select your parameter group, click ` Edit workload queues` button and there you are: First of all, I had to create a user group to match my query monitoring rule, then I have started to prepare a custom configuration with Amazon Redshift console that helped me to produce the final JSON representation of the queues and associated rules. During my research, I came across a chain of deprecated parameters and then I’ve found query_execution_time, which is part of Query Monitoring Rules. My team wanted to time out user queries on Redshift and handle received error on the Django backend by providing some custom message and HTTP error code. ![]() How my journey with WLM started? I have come across a parameter group configuration while researching statement_timeout parameter, which can be applied to the whole cluster or as part of a Redshift transaction. The AWS CLI, the Amazon Redshift API, AWS CloudFormation or one of the AWS SDKs. So, how to get things done? You can set up WLM using the following options: Amazon Redshift console, It can be tuned to improve the performance of your system or disabled using custom WLM JSON configuration. SQA employs machine learning algorithms to predict query duration and prioritize short queries over long-running ones to save their time in the queue, stuck behind the complex statements. Workload management is a piece of configuration with big potential and there is another proof of it, the one that is enabled by default – Short Query Acceleration (SQA). For every query monitoring rule you can specify one of the following actions: log, abort or change query priority. Using predicates you can build your custom rules using basic math operations like ‘ ’ and ‘ =’ on them or take advantage of one of the predefined templates. The metrics listed above are called predicates, you can find the table with a detailed description here. With QMR you can match queries by user group or query group (label) to control it with the rules that you’ll specify, these can be related to: These restrictions are called Query Monitoring Rules (QMR) and this part of Redshift configuration, in my opinion, deserves some attention. With custom WLM JSON configuration you can modify the processing rules of the default queue or add more queues with some restrictions applied. Default configuration contains one, default queue that can run up to 5 queries concurrently – no other restrictions applied. The most powerful parameter though is WLM JSON configuration that is used to define query queues and associated processing rules. You can find the full list of the parameters here. To introduce some changes you need to create a new, custom parameter group and assign it to the cluster. Every cluster has a default configuration that cannot be modified. WLM JSON configuration is part of the Redshift cluster parameter group. User groups are important in the context of Query Monitoring Rules (QMR) that are part of Redshift’s WorkLoad Management (WLM). ![]() They can be used to differentiate users’ privileges related to so-called CRUD operations. ![]() Database access is based on users and user groups. When managing the database backend one of the things to take care of is access management. ![]() However, do you know all the possibilities that it brings? In this article, I would like to describe how to improve the way your database is queried in order to take care of its health, performance and user experience. Definitely, it may even be your first choice for Big Data management in the cloud. Amazon Redshift is a well-known data warehouse solution with PostgreSQL background. ![]()
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