Adaptive allocations improvements #126307
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elasticsearchmachine
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jan-elastic
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Pinging @elastic/ml-core (Team:ML) |
jan-elastic
commented
Apr 4, 2025
| * @param reason may contain a human-readable explanation for the current state | ||
| * @param startTime the time when the assignment was created | ||
| * @param maxAssignedAllocations used for adaptive allocations | ||
| * @param maxAssignedAllocations keeps track of the maximum number of allocations used for this assignment |
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I don't know what this is exactly used for. It looks like only for recording, not for decision making. Definitely not used by adaptive allocations.
jan-elastic
commented
Apr 4, 2025
| } | ||
| if (assignmentStates.get(deploymentId) != AssignmentState.STARTED) { | ||
| logger.debug( | ||
| "adaptive allocations scaler: skipping scaling [{}] because it is in [{}] state.", |
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Updating a model that's not in the STARTED state leads to errors in the logs.
jan-elastic
commented
Apr 4, 2025
| this.nodes = nodes.stream().sorted(Comparator.comparing(Node::id)).toList(); | ||
| this.deployments = deployments.stream().sorted(Comparator.comparing(AssignmentPlan.Deployment::deploymentId)).toList(); | ||
| this.deployments = deployments.stream() | ||
| .filter(deployment -> deployment.allocations() > 0) |
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If these aren't filtered, computePlan will
Plan with at least one allocation for previously assigned models
jonathan-buttner
approved these changes
Apr 4, 2025
prwhelan
approved these changes
Apr 4, 2025
💔 Backport failed
You can use sqren/backport to manually backport by running |
elasticsearchmachine
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Apr 7, 2025
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various small fixes