Use this information to better understand how Elasticsearch Add-On for Heroku instance configurations (for example
azure.data.highio.l32sv2) relate to the underlying cloud provider hardware that we use when you create your deployment.
Deployments use a range of virtualized hardware resources from a cloud provider, such as Amazon EC2 (AWS), Google Compute Platform (GCP) or Microsoft Azure. Instance configurations enable the products and features of the Elastic Stack to run on suitable resources that support their intended purpose. For example, if you have a logging use case that benefits from large amounts of slower but more cost-efficient storage space, you can use large spindle drives rather than more expensive SSD storage. Each instance configuration provides a combination of CPU resources, memory, and storage, all of which you can scale from small to very large.
All instances, regardless of the region or provider, are set to UTC timezone.
Understanding an instance configuration nameedit
How to read an Elasticsearch Add-On for Heroku instance configuration name, such as
- The cloud provider the underlying hardware belongs to.
The products or features of the Elastic Stack:
- Elasticsearch master, ml, and data nodes
- Kibana instances
- Application performance monitoring (APM) server instances
- And more …
- Optional: The hardware characteristics, such as optimization for I/O, storage, compute, or memory.
- The short name for the instance type, or a number identifying a custom machine type.
Instance configurations are an Elasticsearch Add-On for Heroku abstraction of virtualized resources from the provider, but you might recognize the underlying hardware they build on in the instance configuration name. We use instance types on AWS and Azure, and custom machine types on GCP. Elasticsearch Add-On for Heroku instance configurations are not the same AWS instance types.
GCP instance configurations have an updated naming convention.
For AWS instance configurations, we are introducing a new naming convention that will also be rolled out to Azure in the future.