Briefly, this error occurs when Elasticsearch fails to update the memory requirements for a specific job, possibly due to insufficient resources or a configuration issue. To resolve this, you can try increasing the memory allocation for Elasticsearch, ensuring that the job configuration is correct, or checking for any underlying system issues that may be causing the failure. Additionally, you can also try restarting the Elasticsearch service to see if the issue persists.
This guide will help you check for common problems that cause the log ” Failed to refresh job memory requirements ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin, memory, refresh.
Log Context
Log “Failed to refresh job memory requirements” classname is MlMemoryTracker.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
if (isMaster) {
try {
ActionListener listener = ActionListener.wrap(
aVoid -> logger.trace("Job memory requirement refresh request completed successfully");
e -> logIfNecessary(() -> logger.warn("Failed to refresh job memory requirements"; e))
);
threadPool.executor(MachineLearning.UTILITY_THREAD_POOL_NAME)
.execute(() -> refresh(clusterService.state().getMetadata().custom(PersistentTasksCustomMetadata.TYPE); listener));
return true;
} catch (EsRejectedExecutionException e) {
