When you create or edit an existing deployment, you can fine-tune the capacity, add extensions, and select additional features.
Autoscaling reduces some of the manual effort required to manage a deployment by adjusting the capacity as demands on the deployment change. Currently, autoscaling is supported to scale Elasticsearch data tiers upwards, and to scale machine learning nodes both upwards and downwards. Check Deployment autoscaling to learn more.
Depending upon how much data you have and what queries you plan to run, you need to select a cluster size that fits your needs. There is no silver bullet for deciding how much memory you need other than simply testing it. The cluster performance metrics in the Elasticsearch Service Console can tell you if your cluster is sized appropriately. You can also enable deployment monitoring for more detailed performance metrics. Fortunately, you can change the amount of memory allocated to the cluster later without any downtime for HA deployments.
You cannot select a specific instance and remove it in Cloud. To change the cluster topology, go to Deployment > Edit, select a storage and RAM setting from the Size per zone drop-down list, and save your changes. When downsizing the cluster, make sure to have enough resources to handle the current load, otherwise your cluster will be under stress.
For trials, larger sizes are not available until you add a credit card.
Currently, half the memory is assigned to the JVM heap. For example, on a 32 GB cluster, 16 GB are allotted to heap. The disk-to-RAM ratio currently is 1:24, meaning that you get 24 GB of storage space for each 1 GB of RAM. All clusters are backed by SSD drives.
For production systems, we recommend not using less than 4 GB of RAM for your cluster, which assigns 2 GB to the JVM heap.
The CPU resources assigned to a cluster are relative to the size of your cluster, meaning that a 32 GB cluster gets twice as much CPU resources as a 16 GB cluster. All clusters are guaranteed their share of CPU resources, as we do not overcommit resources. Smaller clusters up to and including 8 GB of RAM benefit from temporary CPU boosting to improve performance when needed most.
If you don’t want to autoscale your deployment, you can manually increase or decrease capacity by adjusting the size of hot, warm, cold, and frozen data tiers nodes. For example, you might want to add warm tier nodes if you have time series data that is accessed less-frequently and rarely needs to be updated. Alternatively, you might need cold tier nodes if you have time series data that is accessed occasionally and not normally updated.
To learn more about how much memory might be needed, check Elasticsearch in Production.
High availability is achieved by running a cluster with replicas in multiple data centers (availability zones), to prevent against downtime when infrastructure problems occur or when resizing or upgrading deployments. We offer the options of running in one, two, or three data centers.
Running in two data centers or availability zones is our default high availability configuration. It provides reasonably high protection against infrastructure failures and intermittent network problems. You might want three data centers if you need even higher fault tolerance. Just one zone might be sufficient, if the cluster is mainly used for testing or development.
Some regions might have only two availability zones.
Like many other changes, you change the level of fault tolerance while the cluster is running. For example, when you prepare a new cluster for production use, you can first run it in a single data center and then add another data center right before deploying to production.
While multiple data centers or availability zones increase a cluster’s fault tolerance, they do not protect against problematic searches that cause nodes to run out of memory, for example. For a cluster to be highly reliable and available, it is also important to have enough memory.
The node capacity you chose is per data center. The reason for this is that there is no point in having two data centers if the failure of one will result in a cascading error because the remaining data center cannot handle the total load. Through the allocation awareness in Elasticsearch, we configure the nodes so that your Elasticsearch cluster will automatically allocate replicas between each availability zone
Our article on Elasticsearch in Production covers availability zones and resilience against infrastructure failures in more detail.
The defaults for the different supported script types are generally safe to accept as is, unless you have a specific requirement. The script_fields in filters and facets are one of the features that make Elasticsearch so flexible, but they can allow arbitrary code execution, like
Runtime.exec("cat /etc/passwd") and other malicious operations.
For each supported script type, you have three levels of scripting control:
- Disable the scripts completely
- Enable scripts to run in a sandbox
- Enable all scripts
You can review your Elasticsearch shard activity from Elasticsearch Service. At the bottom of the Elasticsearch page, you can hover over each part of the shard visualization for specific numbers.
We recommend that you read Sizing Elasticsearch before you change the number of shards.
Elastic plugins, custom plugins, dictionaries, and scriptsedit
Lists the official plugins available for your selected Elasticsearch version.
When selecting a plugin from this list you get a version that has been tested with the chosen Elasticsearch version. The main difference between selecting a plugin from this list and uploading the same plugin as a custom extension is in who decides the version used. To learn more, check Add plugins.
The reason we do not list the version chosen on this page is because we reserve the option to change it when necessary. That said, we will not force a cluster restart for a simple plugin upgrade unless there are severe issues with the current version. In most cases, plugin upgrades are applied lazily, in other words when something else forces a restart like you changing the plan or Elasticsearch runs out of memory.
If you have uploaded any custom plugins, user bundles with dictionaries or scripts, should appear on the list and this where you choose to enable them for the cluster.
Only Gold and Platinum subscriptions have access to uploading custom plugins. All subscription levels, including Standard, can upload scripts and dictionaries.
For new deployments that use Elasticsearch version 5.0 and later, we automatically create a Kibana instance for you.
If you use a version before 5.0 or if your cluster didn’t include a Kibana instance initially, there might not be a Kibana endpoint URL shown, yet. To enable Kibana, select Enable. Enabling Kibana provides you with an endpoint URL, where you can access Kibana. It can take a short while to provision Kibana right after you select Enable, so if you get an error message when you first access the endpoint URL, try again.
*Log in to Kibana with the
elastic superuser to try it out. The password was provided when you created your cluster or
can be reset.
For version 7.9.2 and later, you can go straight into Kibana with single sign-on (SSO).
In production systems, you might need to control what Elasticsearch data users can access through Kibana, so you need to create credentials that can be used to access the necessary Elasticsearch resources. This means granting read access to the necessary indexes, as well as access to update the
Set specific configuration parameters to change how Elasticsearch and other Elastic products run. User settings are appended to the appropriate YAML configuration file, but not all settings are supported in Elasticsearch Service.
For more information, refer to Edit your user settings.
There are a few actions you can perform in the deployment management menu:
- Perform a cluster restart - Needed only rarely, but full cluster restarts can help with a suspected operational issue before reaching out to Elastic for help.
- Delete your cluster - For clusters that you no longer need and don’t want to be charged for any longer. Deleting a cluster removes the cluster and all your data permanently.
Use the actions in the deployment management with care. Clusters are not available while they restart and deleting a cluster does really remove the cluster and all your data permanently.