Total hourly charge: 4 nodes * $3.695/hr = $14.78ĭata Written = 840 GB * 1% (throughput update every hour) = 8.4 GB/hourĭay 1: Free of charge for snapshot storageĭay 2: Snapshot storage space for 840 GB = 840 * $0.021 per GB-month= $17.64/month (1 Primary + 1 Replicas) * 2 shards = 4 total nodesĭb.r6gd.4xlarge hourly pricing = $3.695/hour Shards required for dataset: 840GB ÷ 519.65 GB/node = 2Įach shard: (1 Primary + 1 Replicas) Nodes Your total charges are calculated as follows:ĭb.r6gd.4xlarge usable memory capacity: 105.81 GiB/node = 113.64 GB/node, less 19% memory for non-data use:ĭb.r6gd.4xlarge solid-state drive (SSD) capacity: 398.14 GiB = 427.6 GB Additionally, you set your snapshot retention to 2 days enabling you to store the first snapshot free of charge and charging you for snapshot storage for the second snapshot. You also choose to deploy your workload across three availability zones (AZs) in U.S. Because your application uses most recently updated data, you select the db.r6gd.4xlarge node type with data tiering. You use a MemoryDB cluster with two shard that includes one primary and one replica node per shard to meet the application requirements. On average, 1% of the data is updated every hour. The application has a total dataset size of 840 GB. The application is temporal in nature, mostly accessing data generated over the last month, but is required to keep 12 months of data for compliance purposes. You work in a financial company and your team has built an application with MemoryDB as the primary database to meet the performance requirements. Your total charges are calculated as follows: Additionally, you set your snapshot retention to 2 days allowing you to store the snapshot free of charge for the first day and charging you for snapshot storage for the additional day. You also choose to deploy your workload across two availability zones (AZs) in U.S. You choose two shards of db.r6g.xlarge node type to have enough memory to fit the entire dataset in the cluster (50GB) and select one replica per shard to support the reads of the application and high availability. The application is 80% reads and 20% writes, and approximately 50,000 transactions per second. The application is read-heavy and has a total dataset size of 50 GB consisting of 100 byte objects (includes Redis key, value and command size). To meet these performance requirements, you use Amazon MemoryDB for Redis as your primary database. You work at a media and entertainment company and your team built an application that requires very low latency and high throughput. This takes the total cost of the reserved node over the entire term, including any upfront payment, and spreads it out over each hour of the reserved node term. The effective hourly price shows the amortized hourly node cost. When you purchase a reserved node, you are billed for every hour during the entire reserved node term you select, regardless of whether the node is running. ** Effective hourly pricing helps you calculate the amount of money a reserved node will save you over on-demand pricing. The hourly usage rate is equivalent to the total average monthly payments over the term of the reserved node divided by the total number of hours (based on a 365 day year) over the term of the reserved node. For each month, the actual monthly payment will equal the actual number of hours in that month multiplied by the hourly usage rate or number of seconds in that month multiplied by the hourly usage rate divided by 3600, depending on the MemoryDB node type you run. * This is the average monthly payment over the course of the reserved node term.
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