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 OpenSearch operation.
Briefly, this error occurs when OpenSearch fails to construct an aggregation due to incorrect or missing parameters in the aggregation query. This could be due to a wrong field name, incorrect data type, or a missing required parameter. To resolve this issue, you should first verify the field names and data types in your query. Ensure that the field you’re trying to aggregate exists in your index and is of the correct type. Also, check your aggregation syntax to ensure all required parameters are included and correctly formatted.
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 ” Failed to build aggregation [” + aggregator.name() + “] ” to appear. To understand the issues related to this log, read the explanation below about the following OpenSearch concepts: aggregations, search.
Quick links
Overview
Search refers to the searching of documents in an index or multiple indices. The simple search is just a GET API request to the _search endpoint. The search query can either be provided in query string or through a request body.
Examples
When looking for any documents in this index, if search parameters are not provided, every document is a hit and by default 10 hits will be returned.
GET my_documents/_search
A JSON object is returned in response to a search query. A 200 response code means the request was completed successfully.
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
...
]
}
}Notes and good things to know
- Distributed search is challenging and every shard of the index needs to be searched for hits, and then those hits are combined into a single sorted list as a final result.
- There are two phases of search: the query phase and the fetch phase.
- In the query phase, the query is executed on each shard locally and top hits are returned to the coordinating node. The coordinating node merges the results and creates a global sorted list.
- In the fetch phase, the coordinating node brings the actual documents for those hit IDs and returns them to the requesting client.
- A coordinating node needs enough memory and CPU in order to handle the fetch phase.
Log Context
Log “Failed to build aggregation [” + aggregator.name() + “]” class name is AggregationCollectorManager.java. We extracted the following from OpenSearch source code for those seeking an in-depth context :
for (Aggregator aggregator : aggregators) {
try {
aggregator.postCollection();
internals.add(aggregator.buildTopLevel());
} catch (IOException e) {
throw new AggregationExecutionException("Failed to build aggregation [" + aggregator.name() + "]"; e);
}
} // PipelineTreeSource is serialized to the coordinators on older OpenSearch versions for bwc but is deprecated in latest release
// To handle that we need to add it in the InternalAggregations object sent in QuerySearchResult.
