Amazon Opensearch MasterJVM Memory Pressure usage gets high after upgrading Data nodes


Can someone help me to understand why my Master JVM Memory Pressure suddenly goes high after I upgraded my Data nodes instance type?Enter image description here

asked 2 years ago1319 views
2 Answers

Hi there,

Is it not likely that you have created virtual traffic by needlessly upgrading the nodes thus allowing the JVM to believe that the cluster is receiving more traffic than it really is, but for safety sake go through the following best practices for JVM pressure regulation.

Troubleshooting high JVM memory pressure: (

Here are the best practices below for your convenience:

You can resolve high JVM memory pressure issues by reducing traffic to the cluster. To reduce traffic to the cluster, follow these best practices:

  • Clear the field data cache with the POST /index_name/_cache/clear?fielddata=true API operation. Note: Clearing the cache can disrupt queries that are in progress.
  • Avoid aggregating on text fields or change the mapping type to keyword.
  • Scale the domain (so that the maximum heap size per node is 32 GB).
  • Enable slow logs to figure out faulty requests. Note: Verify that the JVM memory pressure is below 90%. For more information about slow Elasticsearch queries, see Advanced tuning: finding and fixing slow Elasticsearch queries on the Elasticsearch website.
  • Optimize search or indexing by choosing the correct number of shards. For more information about indexing and shard count, see Get started with Amazon OpenSearch Service: How many shards do I need?
  • Reduce the number of shards by deleting old or unused indices.
  • For advanced users, you can update the parent, fielddata, or request circuit breaker settings according to your use case. For more information about JVM circuit breakers, see JVM OutOfMemoryError.
answered 2 years ago

In addition to what's already provided, take a look at CloudWatch Metrics to see what's going on with the memory using the steps outlined.

answered a year ago

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