![]() The size of your concurrency scaling cluster is directly proportional to your cluster size, so it also scales as your cluster does. Those free credits have met the needs of 97% of our Amazon Redshift customers’ concurrency scaling requirements, meaning that most customers get the benefits of concurrency scaling without increasing their costs. You only pay for what you use on a per-second basis, and you accumulate 1 hour’s worth of concurrency scaling credits every 24 hours. This is a cost-effective, low-touch option for burst workloads. When you choose concurrency scaling, Amazon Redshift automatically and transparently adds more processing power for just those times when you need it. Rather than a consistent volume of workload throughout the day, perhaps there are short periods of time when you need more resources. Concurrency scaling allows you to add more query processing power to your cluster, but only when you need it. In March 2019, AWS announced the availability of Amazon Redshift concurrency scaling. However, after creation, you can resize your cluster to contain up to 32 ra3.xlplus nodes, up to 64 ra3.4xlarge nodes, or up to 128 ra3.16xlarge nodes. You can create an Amazon Redshift cluster with up to 16 nodes. The transition to a larger node type allows you to horizontally scale with those larger nodes. Rather than running a cluster with 64 ra3.4xlarge nodes, you could elastically resize your cluster to use 16 ra3.16xlarge nodes and have the equivalent resources to host your cluster. Based on the additional resources provided by the larger nodes, you will likely decrease the quantity of nodes at the same time. Vertically scaling increases the resources given to each node. If your workload continues to grow and you’re approaching this limit, you may decide to vertically scale your cluster. To continue with the previous example, ra3.4xlarge nodes have a maximum of 64 nodes per cluster. Each node type has a limit to the number of nodes that can be managed in a single cluster. Horizontal scaling has its limits, however. An elastic resize is the fastest way to horizontally scale your cluster to add nodes as a consistent load increases. As adoption increases and more users need access to the data, you can add nodes of the same node type to your cluster to increase the amount of compute power available to handle those queries. Your workload is consistent throughout the day, with few peaks and valleys. This configuration provides 48 vCPUs and 384 GiB RAM. Let’s assume for this example, you build your cluster with four ra3.4xlarge nodes. The RA3 nodes provide three instance types to build your cluster with: ra3.xlplus, ra3.4xlarge, and ra3.16xlarge. For new workloads that are planning to scale, we recommend starting with our RA3 nodes, which allow you to independently tailor your storage and compute requirements. This process begins with choosing the appropriate instance family for your Amazon Redshift nodes. Common Amazon Redshift scaling patternsīecause Amazon Redshift is a managed cloud data warehouse, you only pay for what you use, so sizing your cluster appropriately is critical for getting the best performance at the lowest cost. This post provides an overview of the available scaling options for Amazon Redshift and also shares a new design pattern that enables query processing in scenarios where having multiple leader nodes are required to extract large datasets for clients or BI tools without introducing additional overhead. Users such as data analysts, database developers, and data scientists use Amazon Redshift to analyze their data to make better business decisions. These customers range from small startups to some of the world’s largest enterprises. Tens of thousands of customers use Amazon Redshift as their analytics platform. ![]() ![]() These scenarios are also often paired with legacy business intelligence (BI) tools where data is further analyzed.Īmazon Redshift is a fast, fully managed cloud data warehouse. These enterprise customers require large datasets to be returned from queries at a high frequency. However, enterprises with consistently high demand that can’t be serviced by a single cluster need another option. Others may have a short duration where they need extra capacity to handle peaks that can be addressed through Amazon Redshift concurrency scaling. ![]() ![]() Some may be able to address these requirements through horizontally or vertically scaling a single cluster. Many enterprise customers have demanding query throughput requirements for their data warehouses. ![]()
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