Scaling and Capacity Planning for Automate Node Visibility

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This documentation applies to a deprecated version of Chef Automate and will reach its End-Of-Life on December 31, 2019. See the Chef Automate site for current documentation. The new Chef Automate includes newer out-of-the-box compliance profiles, an improved compliance scanner with total cloud scanning functionality, better visualizations, role-based access control and many other features. The new Chef Automate is included as part of the Chef Automate license agreement and is available via subscription.

The following disk and CPU utilization metrics for the node visibility feature in Chef Automate can be used when performing scaling and capacity planning on your Chef Automate server.

Determining Storage Needs

Each time a node converges, Chef sends the information about the recipes and resources used during the run, as well as the node object itself (including its attributes), to the Chef Automate server. Therefore, the storage requirements for Chef Automate depend on your node object size, amount of resources evaluated during a converge, fleet size, and convergence interval.

For example, if your average node object is 15KB (with a five percent size buffer for resource/recipe information) and you converge every 15 minutes, the following formula can be used to calculate your total disk utilization for a day.

(15KB Node Object * 1.05) * 4 converges/hour * 24 hours = 1,512 KB (1.512 MB)

In this example, a single node would require approximately 1.512 MB of disk space per day. With this knowledge, you can easily compute the disk space requirements for your entire fleet depending on your node size and convergence interval.

To determine your node size, you can run the following command:

For a chef-server connected node

$ chef-shell -z
chef (12.13.37)> "#{node.to_json.size/1024} KB"

For a chef-solo node

$ chef-shell -a
chef (12.13.37)> "#{node.to_json.size/1024} KB"

Message Throughput

On a machine provisioned with 4 CPUs and 16GB RAM (the recommended compute resources for a Chef Automate Server), Chef Automate is able to reliably process 9000 concurrent converge messages.

When a converge message is received, Chef Automate places the converge message on a queue managed by RabbitMQ. Depending on CPU utilization, it can take additional time for Chef Automate to process a significant burst of messages.