How To Solve Issues Related to Log – Failed to refresh job memory requirements

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Troubleshooting background

To troubleshoot Elasticsearch log “Failed to refresh job memory requirements” it’s important to know common problems related to Elasticsearch concepts: memory, plugin, refresh. See below-detailed explanations 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

Plugins are used to extend the functionality of Elasticsearch. In addition to the core plugins available to you, it is possible to write custom plugins as well. Plugins are generated in a zip format with the mandatory file structure.

Examples:
  • Core Plugins: Xpack for Security and monitoring, Discovery plugins for EC2
  • Adding S3 plugin for storing snapshots on S3
sudo bin/elasticsearch-plugin install repository-s3
  • Adding HDFS plugin for storing snapshots on HDFS
sudo bin/elasticsearch-plugin install repository-hdfs
  • Removing a plugin
sudo bin/elasticsearch-plugin remove repository-hdfs
Notes:
  • Plugins are installed using the Elasticsearch-plugin script, which enables actions such as  listing, removing and installing plugins.
  • Core plugins can be installed simply by providing the name of the plugin to the Elasticsearch-plugin command.
  • 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 the elasticsearch node(s) in order to complete the removal process.
Common Problems:
  • You need to install the required plugins in your Elasticsearch deployment before moving it to production machines. (as it’s likely your production machines are behind a proxy and it’s very hard to get plugins installed behind a proxy).
  • The same is true when you are going to deploy elasticsearch using Docker images, you will most likely be rebuilding the standard image and including your required plugins in the custom docker build. Make sure the docker build is run on a build machine that is not behind a proxy, otherwise the plugin installation will fail during docker build.

Click here to get to our list of the Most frequent issues caused by Elasticsearch Plugins


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 :

Elasticsearch Memory Usage increases over time and is at 100%
stackoverflow.com/questions/50138518/elasticsearch-memory-usage-increases-over-time-and-is-at-100

Number of Views : 0.18 K  Score on Stackoverflow :

Github Issue Number 44156
github.com/elastic/elasticsearch/issues/44156

 


Log Context

Log ”Failed to refresh job memory requirements” classname is MlMemoryTracker.java
We have extracted the following from Elasticsearch source code to get an in-depth context :

<pre class="wp-block-syntaxhighlighter-code"> 
        if (isMaster) {
            try {
                ActionListener<Void> listener = ActionListener.wrap(
                    aVoid -> logger.trace("Job memory requirement refresh request completed successfully");
                    e -> logger.warn("Failed to refresh job memory requirements"; e)
                );
                logger.debug("scheduling async refresh");
                threadPool.executor(executorName()).execute(
                    () -> refresh(clusterService.state().getMetaData().custom(PersistentTasksCustomMetaData.TYPE); listener));
                return true;




</pre>

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