Cloud Bigtable is a fully-managed, scalable NoSQL database service for big operational and analytical workloads on the Google Cloud Platform (GCP). So, recently, the public cloud provider declared the general availability of Bigtable Autoscaling. This automatically adds or removes capacity in response to the changing demand for applications allowing cost optimizations. Hence, Autoscaling for Bigtable automatically scales the number of nodes in a cluster up or down based on changing demands of use.

Before, scaling of Bigtable was done programmatically as Bigtable’s Cloud Monitoring API exposes several metrics. A customer could programmatically monitor those metrics for a cluster and then add or remove nodes based on the current metrics.

InfoQ explains in an article that customers can now configure the autoscaling of their Bigtable clusters via the Cloud Console, gcloud, the Bigtable admin API, or client libraries. Users can set the minimum and the maximum number of nodes for their Bigtable autoscaling configuration instead of doing it programmatically.

In a Google’s deep-dive blog post on Bigtable Autoscaling, Billy Jacobson, a developer advocate Bigtable, and Justin Uang, a software engineer Bigtable, talked about the enabling of the autoscaling feature:

“Autoscaling can be enabled for existing clusters or configured with new clusters. You’ll need two pieces of information: a target CPU utilization and a range to keep your node count within. No complex calculations, programming, or maintenance are required. One constraint to be aware of is the maximum node count in your range cannot be more than 10 times the minimum node count. Storage utilization is a factor in autoscaling, but the targets for storage utilization are set by Bigtable and not configurable.”

In addition to Autoscaling, Google also added features to further optimize cost and reduce management, such as:

• Double the storage amount to let customers store more data for less, particularly valuable for optimized storage workloads.
• Cluster groups provide flexibility for determining how customers can route their application traffic to ensure a better customer experience.
• More granular utilization metrics improve observability, faster troubleshooting, and workload management.

Lastly, the autoscaling feature is available in all Bigtable regions and works on HDD and SSD clusters.