![]() ![]() In the WLM configuration, the “memory_percent_to_use” represents the actual amount of working memory, assigned to the service class. Note: In this example, the WLM configuration is in JSON format and uses a query monitoring rule (Queue1). "metric_name": "query_temp_blocks_to_disk", Use the STV_WLM_SERVICE_CLASS_CONFIG table to check the current WLM configuration of your Amazon Redshift cluster: [ Resolution Checking your WLM configuration and memory usage To check the concurrency level and WLM allocation to the queues, perform the following steps:ġ.FSPCheck the current WLM configuration of your Amazon Redshift cluster.Ģ.FSPCreate a test workload management configuration, specifying the query queue's distribution and concurrency level.ģ.FSP(Optional) If you are using manual WLM, then determine how the memory is distributed between the slot counts. Note: To define metrics-based performance boundaries, use a query monitoring rule (QMR) along with your workload management configuration. As a result, queries with more resource consumption can run in queues with more resource allocation. Manual WLM: Allows you to have more control over concurrency level and memory allocation to the queues.The dispatched query allows users to define the query priority of the workload or users to each of the query queues. Automatic WLM: Allows Amazon Redshift to manage the concurrency level of the queues and memory allocation for each dispatched query.You can configure workload management to manage resources effectively in either of these ways: Some of the queries might consume more cluster resources, affecting the performance of other queries. It routes queries to the appropriate queues with memory allocation for queries at runtime. Amazon Redshift workload management (WLM) allows you to manage and define multiple query queues. ![]()
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