![]() ![]() It may also occur as a result of Redshift scheduled maintenance during the maintenance window. The AWS documentation lists what changes are dynamic and static.Ī cluster reboot can be done through the Redshift console, CLI, or API. Static changes, which include changing between the automatic and manual WLM modes, require a cluster reboot to take effect. As such, manual efforts can be nontrivial and time consuming – in so far, the Datacoral default behavior or the Redshift automatic mode provides adequate performance, a manual tuning effort might not be cost effective.Īnother factor that should be taken into account is that there are two types of changes to the WLM configuration, dynamic and static. Generally, the fine-tuning of workload settings requires a good understanding of Redshift performance characteristics and the factors that affect them as well as an understanding of the specifics of the workloads that are to be tuned. ![]() The manual mode provides rich functionality for controlling workloads. ![]() 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. The Redshift WLM has two fundamental modes, automatic and manual. However, such modifications are not recommended in normal cases. The Datacoral defaults should provide adequate out-of-the-box performance without any need for user intervention, but it is possible to change the WLM configuration if need be. It is possible to modify the WLM settings away from the defaults provided by Datacoral. Concurrency Scaling can be configured as part of the WLM functionality. Rather than restricting activity, Concurrency Scaling is meant to add resources in an elastic way as needed so to avoid scarcity issues. WLM is used to govern the usage of scarce resources and prioritize certain activities over others. ![]() The need for WLM may be diminished if Redshift’s Concurrency Scaling functionality is used. Those queries tend to go against system tables rather than user data, but since the data sources for many Redshift system tables are spread out over all the nodes, these monitoring queries may have some impact on the number of user queries that can be executed concurrently. In order to properly monitor what happens on the system and alert users of problems, the Redshift control plane constantly issues SQL queries. The managed service aspect of Redshift also has an impact on resource management in the area of concurrency. The WLM functionality provides a means for controlling the behavior of the queueing mechanism, including setting priorities for queries from different users or groups of users. Redshift resolves this issue by having a queueing mechanism that makes newly submitted queries wait if the system is fully loaded. As a result of running on all the cluster nodes, queries tend to be very fast, but only a limited number can be run concurrently without risking overloading the system. But distributing the data of a table over all the nodes, e.g., by using a hash function on some column, means that any query accessing the table would require work to be performed on all the nodes – you can’t just run one particular query on one specific node and another (presumably more important query) on four specific other nodes. Ideally, the distribution is somewhat even between nodes. Redshift's fundamental MPP architecture generally has the effect that the data stored in a Redshift cluster is distributed over all the cluster nodes. In the case of Redshift, additional considerations involve the architecture of Redshift as an MPP database and the implications of Redshift being a managed service. Given that resources are often either scarce or costly, it makes sense to have an infrastructure that lets users govern the usage and prioritize the different types of tasks that use them. Redshift, like many other database engines, has infrastructure for managing resources and workloads. ![]()
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