Sampler aggregation must be used with child aggregations – How to solve this Elasticsearch exception

Opster Team

August-23, Version: 6.8-8.9

Before you dig into reading this guide, have you tried asking OpsGPT what this log means? You’ll receive a customized analysis of your log.

Try OpsGPT now for step-by-step guidance and tailored insights into your Elasticsearch operation.

Briefly, this error occurs when a sampler aggregation is used in Elasticsearch without any child aggregations. Sampler aggregation is a special type of aggregation that is used to limit the number of documents that are considered for further aggregations. To resolve this issue, you need to add a sub-aggregation to the sampler aggregation. This could be any type of aggregation such as a terms, histogram, or another sampler aggregation. Make sure the sub-aggregation is correctly nested within the sampler aggregation.

For a complete solution to your to your search operation, try for free AutoOps for Elasticsearch & OpenSearch . With AutoOps and Opster’s proactive support, you don’t have to worry about your search operation – we take charge of it. Get improved performance & stability with less hardware.

This guide will help you check for common problems that cause the log ” Sampler aggregation must be used with child aggregations. ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: search, aggregations.

Log Context

Log “Sampler aggregation must be used with child aggregations.” class name is SamplerAggregator.java. We extracted the following from Elasticsearch source code for those seeking an in-depth context :

 }  @Override
 protected LeafBucketCollector getLeafCollector(AggregationExecutionContext aggCtx; LeafBucketCollector sub) throws IOException {
 if (bdd == null) {
 throw new AggregationExecutionException("Sampler aggregation must be used with child aggregations.");
 }
 return bdd.getLeafCollector(aggCtx);
 }  @Override

 

How helpful was this guide?

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?