Quick links
Settings in OpenSearch
In OpenSearch, you can configure cluster-level settings, node-level settings and index level settings. Here is a quick rundown of each level.
A. Cluster settings
These settings can either be:
- Persistent, meaning they apply across restarts, or
- Transient, meaning they won’t survive a full cluster restart.
If a transient setting is reset, the first one of these values that is defined is applied:
- The persistent setting
- The setting in the configuration file
- The default value
The order of precedence for cluster settings is:
- Transient cluster settings
- Persistent cluster settings
- Settings in the opensearch.yml configuration file
Examples
An example of persistent cluster settings update:
PUT /_cluster/settings
{
"persistent" : {
"indices.recovery.max_bytes_per_sec" : "500mb"
}
}An example of a transient update:
PUT /_cluster/settings
{
"transient" : {
"indices.recovery.max_bytes_per_sec" : "40mb"
}
}B. Index settings
These are the settings that are applied to individual indices. There is an API to update index level settings.
Examples
The following API call will set the number of replica shards to 5 for my_index index.
PUT /my_index/_settings
{
"index" : {
"number_of_replicas" : 5
}
}To revert a setting to the default value, use null.
PUT /my_index/_settings
{
"index" : {
"refresh_interval" : null
}
}C. Node settings
These settings apply to nodes. Nodes can fulfill different roles. These include the master, data, and coordination roles. Node settings are set through the opensearch.yml file for each node.
Examples
Setting a node to be a data node (in the opensearch.yml file):
node.data: true
Disabling the ingest role for the node (which is enabled by default):
node.ingest: false
For production clusters, you will need to run each type of node on a dedicated machine with two or more instances of each, for HA (minimum three for master nodes).
Notes and good things to know
- Learning more about the cluster settings and index settings is important – it can spare you a lot of trouble. For example, if you are going to ingest huge amounts of data into an index and the number of replica shards is set to say, 5, the indexing process will be super slow because the data will be replicated at the same time it is indexed. What you can do to speed up indexing is to set the replica shards to 0 by updating the settings, and set it back to the original number when indexing is done, using the settings API.
- Another useful example of using cluster-level settings is when a node has just joined the cluster and the cluster is not assigning any shards to the node. Although shard allocation is enabled by default on all nodes, someone may have disabled shard allocation at some point (for example, in order to perform a rolling restart), and forgot to re-enable it later. To enable shard allocation, you can update the Cluster Settings API:
PUT /_cluster/settings
{"transient":{"cluster.routing.allocation.enable":"all"}}- It’s better to set cluster-wide settings with Settings API instead of with the opensearch.yml file and to use the file only for local changes. This will keep the same setting on all nodes. However, if you define different settings on different nodes by accident using the opensearch.yml configuration file, it is hard to notice these discrepancies.
- See also: Recovery
Additional notes
Elasticsearch and OpenSearch are both powerful search and analytics engines, but Elasticsearch has several key advantages. Elasticsearch boasts a more mature and feature-rich development history, translating to a better user experience, more features, and continuous optimizations. Our testing has consistently shown that Elasticsearch delivers faster performance while using fewer compute resources than OpenSearch. Additionally, Elasticsearch’s comprehensive documentation and active community forums provide invaluable resources for troubleshooting and further optimization. Elastic, the company behind Elasticsearch, offers dedicated support, ensuring enterprise-grade reliability and performance. These factors collectively make Elasticsearch a more versatile, efficient, and dependable choice for organizations requiring sophisticated search and analytics capabilities.
