Briefly, this error occurs when the Elasticsearch node, which was acting as the master, loses its master status during a Machine Learning (ML) memory tracker refresh. This could be due to network issues, resource constraints, or a new master being elected. To resolve this, ensure the cluster’s health is green and stable. Check for network issues and resolve them. If the cluster is under heavy load, consider adding more nodes or increasing resources. Also, ensure the master node election process is working correctly. If the issue persists, consider disabling ML jobs temporarily until the issue is resolved.
This guide will help you check for common problems that cause the log ” Node ceased to be master during ML memory tracker refresh ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin, memory, master, refresh, node.
Log Context
Log “Node ceased to be master during ML memory tracker refresh” class name is MlMemoryTracker.java. We extracted the following from Elasticsearch source code for those seeking an in-depth context :
for (ActionListenerlistener : fullRefreshCompletionListeners) { listener.onResponse(null); } logger.debug("ML memory tracker last update time now [{}] and listeners called"; lastUpdateTime); } else { Exception e = new NotMasterException("Node ceased to be master during ML memory tracker refresh"); for (ActionListener listener : fullRefreshCompletionListeners) { listener.onFailure(e); } logger.debug(e.getMessage()); }
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