Encountered more than one weight for a – Elasticsearch Error How To Solve Related Issues

Encountered more than one weight for a – 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 “Encountered more than one weight for a” 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”Encountered more than one weight for a”classname  is WeightedAvgAggregator.java
We extracted the following from Elasticsearch source code for those seeking an in-depth context :

valueCompensations = bigArrays.grow(valueCompensations; bucket + 1);
  weightCompensations = bigArrays.grow(weightCompensations; bucket + 1);
  if (docValues.advanceExact(doc) && docWeights.advanceExact(doc)) {
  if (docWeights.docValueCount() > 1) {
  throw new AggregationExecutionException("Encountered more than one weight for a " +
  "single document. Use a script to combine multiple weights-per-doc into a single value.");
  // There should always be one weight if advanceExact lands us here; either
  // a real weight or a `missing` weight
  assert docWeights.docValueCount() == 1;

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 'invalid value encountered in double_scalars' warning, possibly numpy -views 181,634 ,score 70

Nearest neighbors in high-dimensional data? -views   61,736,score 163

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