How To Solve Issues Related to Log – Couldnt schedule ML memory update node might be shutting down

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Updated: Jan-20

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Troubleshooting background

To troubleshoot Elasticsearch log “Couldnt schedule ML memory update node might be shutting down” it’s important to understand common problems related to Elasticsearch concepts: memory, plugin. See detailed explanations below complete with common problems, examples and useful tips.

Memory in Elasticsearch

What is it

Memory is one of the most critical resources to monitor in Elasticsearch. Elasticsearch runs on JVM and uses heap memory areas for query cache, request cache, accessing lucene segments and storing fielddata for aggregations and sorting.

Commos problems and important points
  • The most common error that arises in Elasticsearch is OutOfMemory error. This error comes when the node is not able to cope up with the required heap size space. To avoid this, you need to closely monitor the heap utilization and garbage collector performance.
  • As per the most up-to-date best practices you should not allocate more than 50 percent of total RAM to JVM heap size. Starting from Elasticsearch version 5.x onward this can be set using -Xms and -Xmx parameters inside jvm.options configuration file. The defaults are set to 1 GB for both minimum and maximum heap size.
  • The heap size should not set more than 31 GB in any case to avoid the poor garbage collection.

Plugin in Elasticsearch

What it is

A plugin is used to enhance the core functionalities of Elasticsearch. Elasticsearch provides some core plugins as a part of their release installation. In addition to those core plugins, it is possible to write your own custom plugins as well. There are several community plugins available on GitHub for various use cases.

Examples:
  • Get all the instructions for the plugin usage
sudo bin/elasticsearch-plugin -h
  • Installing S3 plugin using URL for storing Elasticsearch snapshots on S3
sudo bin/elasticsearch-plugin install repository-s3
  • Removing a plugin
sudo bin/elasticsearch-plugin remove repository-s3
  • Installing a plugin using the file path
sudo bin/elasticsearch-plugin install file:///path/to/plugin.zip

Notes:
  • Plugins are installed and removed using the elasticsearch-plugin script, which ships as a part of Elasticsearch installation and can be found inside the bin/ directory of the Elasticsearch installation path.
  • A plugin has to be installed on every node of the cluster and each of the nodes has to be restarted to make the plugin visible.
  • You can also download the plugin manually and then install it using the elasticsearch-plugin install command, providing the file name/path of the plugin’s source file.
  • When a plugin is removed, you will need to restart every elasticsearch node in order to complete the removal process.

Common Problems:
  • Managing permission issues during and after plugin installation is the most common problem. If Elasticsearch was installed using the deb or rpm package then the plugin has to be installed using the root user, or else you can install the plugin as the user that owns all of the Elasticsearch files.
  • In case of deb or rpm package installation, it is important to check the permission of the plugins directory after plugin installation and update the permission if it has been modified using the following command:
chown -R elasticsearch:elasticsearch path_to_plugin_directory 
  • If your Elasticsearch nodes are running in a private subnet without internet access, you cannot install a plugin directly. In this case, you can simply download the plugins at once and copy the files inside the plugins directory of the Elasticsearch installation path on every node. The node has to be restarted in this case as well.


To help troubleshoot related issues we have gathered selected Q&A from the community and issues from Github , please review the following for further information :

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2 Kibana Docker “This may take a few minutes” but never boots 0.70 K  1

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Log Context

Log ”Couldn’t schedule ML memory update – node might be shutting down” classname is MlMemoryTracker.java
We have extracted the following from Elasticsearch source code to get an in-depth context :

                 logger.debug("scheduling async refresh");
                threadPool.executor(executorName()).execute(
                    () -> refresh(clusterService.state().getMetaData().custom(PersistentTasksCustomMetaData.TYPE); listener));
                return true;
            } catch (EsRejectedExecutionException e) {
                logger.warn("Couldn't schedule ML memory update - node might be shutting down"; e);
            }
        }

        return false;
    }






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