Briefly, this error occurs when Elasticsearch’s machine learning feature takes longer than expected to infer results from a trained model. This could be due to heavy load, insufficient resources, or a complex model. To resolve this, you can increase the timeout limit, optimize your model for faster inference, or scale up your Elasticsearch cluster to provide more resources. Additionally, ensure that your data nodes are not overloaded and consider distributing the load more evenly across your cluster.
This guide will help you check for common problems that cause the log ” timeout [{}] waiting for inference result ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: plugin.
Log Context
Log “timeout [{}] waiting for inference result” class name is AbstractPyTorchAction.java. We extracted the following from Elasticsearch source code for those seeking an in-depth context :
void onTimeout() {
if (notified.compareAndSet(false; true)) {
processContext.getTimeoutCount().incrementAndGet();
processContext.getResultProcessor().ignoreResponseWithoutNotifying(String.valueOf(requestId));
listener.onFailure(
new ElasticsearchStatusException("timeout [{}] waiting for inference result"; RestStatus.REQUEST_TIMEOUT; timeout)
);
return;
}
getLogger().debug("[{}] request [{}] received timeout after [{}] but listener already alerted"; deploymentId; requestId; timeout);
}
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