Take a template that pre-configures the Elastic Stack and make it yours by customizing your deployment to make it fit just right. You can adjust capacity and performance, change the level of fault tolerance, add more features, and much more.
There are several reasons why you might want to change the configuration of your deployment:
- To increase or decrease capacity by changing the amount of reserved memory and storage for different parts of your deployment.
- To improve high availability by adjusting the number of availability zones that your deployment runs on.
- To enable features, such as machine learning, by resizing their instance configurations.
- To enable specific Elasticsearch plugins which are not enabled by default.
- To set specific configuration parameters for your Elasticsearch nodes or Kibana instances.
You can either customize your deployment before creating it or customize an existing deployment.
To customize your deployment:
Adjust the resources assigned to your instances:
- Resize the memory or storage assigned to instance configurations using our sliders to improve performance. Increasing memory or storage also increases the CPU resources that get assigned relative to the size of the instance, meaning that a 32 GB instance gets twice as much CPU resources as a 16 GB one.
- Add fault tolerance (high availability) by using more availability zones.
Add features that were not previously enabled or disable features you no longer need.
For example, to enable machine learning, you resize its instance configuration to the recommended minimum of 16 GB of memory or to a size that you already know works well for your anomaly detection.
Write down the password for the
elasticuser (or the
adminuser for version 2.x) and keep it somewhere safe. You need the password to connect to your Elasticsearch cluster and Kibana. (Missed it? Reset the password.)
That’s it! After your deployment is ready, follow the steps for connecting to Elasticsearch and Kibana to start working with some data.