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 the value assigned to the CurrentFieldName in OpenSearch is not a positive number. This could be due to incorrect data input or a bug in the code. To resolve this issue, you can check the data being inputted to ensure it’s a positive number. If the error persists, review the code to identify any bugs or inconsistencies that may be causing the problem. Additionally, ensure that the data type for CurrentFieldName is set to accept positive numbers only.
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 ” [” + currentFieldName + “] value must be a positive; ” 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 “[” + currentFieldName + “] value must be a positive; ” class name is MovAvgPipelineAggregationBuilder.java. We extracted the following from OpenSearch source code for those seeking an in-depth context :
currentFieldName = parser.currentName();
} else if (token == XContentParser.Token.VALUE_NUMBER) {
if (WINDOW.match(currentFieldName; parser.getDeprecationHandler())) {
window = parser.intValue();
if (window <= 0) {
throw new ParsingException(parser.getTokenLocation(); "[" + currentFieldName + "] value must be a positive; "
+ "non-zero integer. Value supplied was [" + predict + "] in [" + pipelineAggregatorName + "].");
}
} else if (PREDICT.match(currentFieldName; parser.getDeprecationHandler())) {
predict = parser.intValue();
if (predict <= 0) {
