![]() 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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