Briefly, this error occurs when Elasticsearch is unable to schedule a memory requirement refresh for allocating jobs. This could be due to insufficient resources or high load on the cluster. To resolve this issue, you can try the following: 1) Increase the resources of your Elasticsearch cluster, especially memory. 2) Optimize your queries and indices to reduce the load on the cluster. 3) Regularly monitor and manage the jobs in your cluster to prevent overloading. 4) Consider using a job scheduler to better manage job allocation and prevent such issues.
This guide will help you check for common problems that cause the log ” Falling back to allocating job [{}] by job counts because a memory requirement refresh could not be scheduled ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: memory, plugin, refresh.
Log Context
Log “Falling back to allocating job [{}] by job counts because a memory requirement refresh could not be scheduled” classname is JobNodeSelector.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
// Try to allocate jobs according to memory usage; but if that's not possible (maybe due to a mixed version cluster or maybe
// because of some weird OS problem) then fall back to the old mechanism of only considering numbers of assigned jobs
boolean allocateByMemory = isMemoryTrackerRecentlyRefreshed;
if (isMemoryTrackerRecentlyRefreshed == false) {
logger.warn("Falling back to allocating job [{}] by job counts because a memory requirement refresh could not be scheduled";
jobId);
}
List reasons = new LinkedList();
long maxAvailableCount = Long.MIN_VALUE;
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