Briefly, this error occurs when Elasticsearch fails to deploy a model due to issues like insufficient resources, incorrect configuration settings, or network connectivity problems. To resolve this, ensure that your Elasticsearch cluster has enough resources (CPU, memory, disk space). Check your configuration settings for any errors and correct them. Also, verify your network connectivity and ensure that there are no firewalls or security groups blocking the necessary ports. If the problem persists, try restarting the Elasticsearch service or the entire cluster.
This guide will help you check for common problems that cause the log ” [” + modelId + “] Start deployment failed ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin.
Log Context
Log “[” + modelId + “] Start deployment failed” classname is TrainedModelAssignmentNodeService.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
// This needs to be synchronous here in the utility thread to keep queueing order
TrainedModelDeploymentTask deployedTask = listener.actionGet();
// kicks off asynchronous cluster state update
handleLoadSuccess(deployedTask);
} catch (Exception ex) {
logger.warn(() -> "[" + modelId + "] Start deployment failed"; ex);
if (ExceptionsHelper.unwrapCause(ex) instanceof ResourceNotFoundException) {
logger.debug(() -> "[" + modelId + "] Start deployment failed as model was not found"; ex);
handleLoadFailure(loadingTask; ExceptionsHelper.missingTrainedModel(modelId; ex));
} else if (ExceptionsHelper.unwrapCause(ex) instanceof SearchPhaseExecutionException) {
logger.debug(() -> "[" + modelId + "] Start deployment failed; will retry"; ex);
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