{"id":8580,"date":"2024-09-25T07:00:40","date_gmt":"2024-09-25T14:00:40","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/cosmosdb\/?p=8580"},"modified":"2024-10-23T11:31:28","modified_gmt":"2024-10-23T18:31:28","slug":"announcing-the-ga-of-dynamic-scaling-per-region-and-per-partition-autoscale","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/cosmosdb\/announcing-the-ga-of-dynamic-scaling-per-region-and-per-partition-autoscale\/","title":{"rendered":"Announcing the GA of Dynamic Scaling (Per Region and Per Partition Autoscale)"},"content":{"rendered":"

We are excited to announce the GA of Azure Cosmos DB dynamic scaling \u2013 among multiple new features (Binary Encoding<\/a>, Reserved Capacity<\/a>) released recently to make your Azure Cosmos DB workloads even more cost efficient. Dynamic scaling <\/strong>is an enhancement to autoscale which provides cost optimization for nonuniform workloads. In the earlier version of autoscale, all partitions scaled uniformly based on the most active partition, which caused unnecessary scale-ups if only one or a few partitions were active.\u00a0 With this new feature, partitions and regions now scale independently, improving cost efficiency for non-uniform large-scale workloads with multiple partitions and\/or multiple regions. We\u2019ve seen that dynamic scaling has helped customers save between 15% to 70% on autoscale costs, depending on workload patterns.<\/p>\n

Ideal use cases for dynamic scaling<\/h2>\n

We recommend this feature to all customers interested in scaling the throughput (RU\/s) of their workloads automatically and instantly based on usage. However, there are two specific use cases where we see the most benefits:<\/p>\n

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  • Database workloads that have a highly trafficked primary region and a secondary passive region for disaster recovery.\n
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    • With dynamic scaling, you can now save costs as the secondary region will independently and automatically scale down while idle. Then automatically scale-up as it becomes active and while handling write replication from the primary region.<\/li>\n<\/ul>\n<\/li>\n
    • Multi-region database workloads.\n
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      • These workloads often observe uneven distribution of requests across regions due to natural traffic growth and dips throughout the day. (e.g. a database might be active during business hours across globally distributed time zones).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n