Elasticsearch Elasticsearch Too Much Memory Allocated to Warm Nodes

By Opster Team

Updated: Mar 10, 2024

| 2 min read

What does this mean? 

The memory allocated to the warm nodes in your Elasticsearch cluster can be reduced. Warm nodes are used to store older, less-frequently accessed data. By reducing the memory allocated to these nodes, you can improve the performance efficiency, lower memory requirements, enable scaling efficiency, reduce I/O operations, and facilitate resource consolidation. These benefits result in reduced hardware needs and streamlined resource utilization, leading to overall cost savings for your search deployment.

Why does this occur?

This occurs when the memory-to-disk ratio in your Elasticsearch cluster is not optimized. This can happen due to various reasons, such as:

  1. Over-allocation of memory to warm nodes, which can lead to inefficient resource utilization.
  2. Inefficient data management, where older data is not moved to warm nodes as expected.
  3. Inadequate monitoring and management of the Elasticsearch cluster, leading to suboptimal resource allocation.

Possible impact and consequences of high memory allocation

If the memory allocated to warm nodes is not reduced, it can lead to the following consequences:

  1. Increased costs due to inefficient resource utilization.
  2. Reduced performance efficiency, as more memory is allocated to warm nodes than necessary.
  3. Hindered scaling efficiency, as the cluster may require more resources to handle the same workload.
  4. Increased I/O operations, leading to slower query response times and reduced overall performance.

How to resolve

To resolve the issue and optimize the memory allocation to warm nodes, follow these steps:

1. Analyze your Elasticsearch cluster’s memory usage and identify the warm nodes with excessive memory allocation.

2. Adjust the memory allocation for the warm nodes by updating the Elasticsearch configuration. This can be done by creating a custom JVM options file (to be located in the config/jvm.options.d folder) and setting the appropriate heap size for your data nodes. For example:

# Set the heap size to 50% of available memory, up to a maximum of 32GB

3. Monitor the performance of your Elasticsearch cluster after making the changes. Ensure that the memory-to-disk ratio is optimized, and the cluster is utilizing resources efficiently.

4. Implement a data management strategy to move older, less-frequently accessed data to warm nodes. This can be achieved using Index Lifecycle Management (ILM) policies in Elasticsearch:

PUT _ilm/policy/my_policy
  "policy": {
    "phases": {
      "warm": {
        "min_age": "30d",
        "actions": {
          "allocate": {
            "require": {
              "data": "warm"
          "shrink": {
            "number_of_shards": 1
          "forcemerge": {
            "max_num_segments": 1

5. Regularly monitor and manage your Elasticsearch cluster to ensure optimal resource allocation and performance efficiency.


By following this guide, you can reduce the memory allocated to warm nodes in your Elasticsearch cluster, leading to improved performance efficiency, lower memory requirements, and overall cost savings. Regular monitoring and management of your cluster will help maintain an optimized memory-to-disk ratio and ensure efficient resource utilization.

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