Briefly, this error occurs when Elasticsearch fails to start a deployment due to issues like insufficient resources, incorrect configuration settings, or network connectivity problems. To resolve this, ensure that your system has enough resources (CPU, memory, disk space). Check your Elasticsearch configuration for any errors and correct them. Also, verify your network settings and ensure that Elasticsearch can communicate with other necessary services. If the problem persists, consider checking the Elasticsearch logs for more detailed error information.
This guide will help you check for common problems that cause the log ” [” + deploymentId + “] 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 “[” + deploymentId + “] 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(() -> "[" + deploymentId + "] Start deployment failed"; ex);
if (ExceptionsHelper.unwrapCause(ex) instanceof ResourceNotFoundException) {
String modelId = loadingTask.getParams().getModelId();
logger.debug(() -> "[" + deploymentId + "] Start deployment failed as model [" + modelId + "] was not found"; ex);
handleLoadFailure(loadingTask; ExceptionsHelper.missingTrainedModel(modelId; ex));
} else if (ExceptionsHelper.unwrapCause(ex) instanceof SearchPhaseExecutionException) {
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