You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Currently the ML usage info contains details of how many jobs and datafeeds are created/running, but not the most basic statistic of how many ML nodes are in the cluster.
It should be pretty easy to add this to the MachineLearningFeatureSetUsage class. Then the MachineLearningFeatureSet.execute() method can populate it by iterating the discovery nodes in the cluster state and looking for the ml.enabled attribute (which basically propagates each node's node.ml setting to the other nodes).
Something like:
int mlNodeCount = 0;
for (DiscoveryNode node : clusterState.getNodes()) {
Map<String, String> nodeAttributes = node.getAttributes();
String enabled = nodeAttributes.get(MachineLearning.ML_ENABLED_NODE_ATTR);
if (Boolean.parseBoolean(enabled)) {
++mlNodeCount;
}
}
The text was updated successfully, but these errors were encountered:
Currently the ML usage info contains details of how many jobs and datafeeds are created/running, but not the most basic statistic of how many ML nodes are in the cluster.
It should be pretty easy to add this to the
MachineLearningFeatureSetUsage
class. Then theMachineLearningFeatureSet.execute()
method can populate it by iterating the discovery nodes in the cluster state and looking for theml.enabled
attribute (which basically propagates each node'snode.ml
setting to the other nodes).Something like:
The text was updated successfully, but these errors were encountered: