In addition to reading this guide, we recommend you run the Elasticsearch Health Check-Up. It will detect issues and improve your Elasticsearch performance by analyzing your shard sizes, threadpools, memory, snapshots, disk watermarks and more.The Elasticsearch Check-Up is free and requires no installation.
Before you begin reading this guide, we recommend you try running the free OpenSearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.
OpenSearch loaded client nodes is another one of the issues that can be prevented and resolved automatically using AutoOps for OpenSearch. AutoOps will also help you optimize other important settings and processes in OpenSearch to improve performance. Try AutoOps for free.
Sometimes you can observe that the CPU and load on some coordinating nodes (client nodes) is higher than others.
This can be caused by applications that are not load balancing correctly across the coordinating nodes, and are making all their HTTP calls to just one or some of the nodes.
A saturated coordinating node could cause an increase in search or indexing response latency, or an increase in write queue/search queue when the cluster is under load (despite there being processing capacity on data nodes). Eventually this could lead to queries timing out.
How to resolve it
You should fix this by putting a load balancer in front of your OpenSearch nodes, or by including ALL of the nodes in the client application.
os = OpenSearch( ['clientNode1', 'clientNode2','clientNode3'], http_auth=('user', 'secret'), scheme="https", port=443, )
The above example is how you can load balance across 3 nodes (you should include all the nodes) when using the python client without a load balancer. All of the official OpenSearch clients use similar arrays in their construction.
We are sorry that this post was not useful for you!
Let us improve this post!
Tell us how we can improve this post?