Take a solution or template that pre-configures 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 or APM (application performance monitoring), by resizing their instance configurations.
- For hot-warm architecture deployments, to configure how indices get managed over time.
- 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.
Some features, such as larger deployment sizes, become available only when you add a credit card and are not free. If a feature is greyed out, you need to add a credit card to unlock the feature.
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.
Note that to increase the number of nodes assigned to an instance configuration you must first scale up to the maximum RAM for that instance type. For example, if the maximum value on the RAM per Node slider for your Elasticsearch data node is 64GB, you need to scale up to that value before you can add additional nodes.
- 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 might 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.
- For hot-warm architecture deployments: Configure index management.
Write down the password for the
elasticuser 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! Now that you are up and running, start exploring with Kibana, our open-source visualization tool. If you’re not familiar with adding data, yet, Kibana can show you how to index your data into Elasticsearch, or try our basic steps for working with Elasticsearch.