Failed to parse percentage significance heuristic. expected an empty object; – Elasticsearch Error How To Solve Related Issues



Failed to parse percentage significance heuristic. expected an empty object; – Elasticsearch Error How To Solve Related Issues

Updated: July-20

Elasticsearch Version: 1.7-8.0

Before you begin reading this guide, we recommend you try running the Elasticsearch Error Check-Up  which can resolve issues causing many errors 

 

This guide will help you check for common problems that cause the log “failed to parse percentage significance heuristic. expected an empty object;” to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts: search, aggregations.


Advanced users might want to skip right to the common problems section in each concept or try running the Check-Up which analyses ES to discover the cause of many errors and provides suitable actionable recommendations (free tool that requires no installation). 

Log Context

Log”failed to parse [percentage] significance heuristic. expected an empty object;”classname  is PercentageScore.java
We extracted the following from Elasticsearch source code for those seeking an in-depth context :

public static SignificanceHeuristic parse(XContentParser parser)
  throws IOException; QueryShardException {
  // move to the closing bracket
  if (!parser.nextToken().equals(XContentParser.Token.END_OBJECT)) {
  throw new ElasticsearchParseException("failed to parse [percentage] significance heuristic. expected an empty object; " +
  "but got [{}] instead"; parser.currentToken());
  }
  return new PercentageScore();
  }

Related issues to this log

We have gathered selected Q&A from the community and issues from Github, that can help fix related issues please review the following for further information :

1 Heuristically/Optionally use sparse data structures for adjacency matrix aggregation ¬∑  

Heuristically/Optionally use sparse data structures for adjacency matrix aggregation ¬∑  



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