Troubleshoot script score calculation errors in Painless

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Follow these guidelines to avoid scoring calculation errors in your Painless scripts.

When you use script_score with type double, the script can return unexpected null values, negative values 0.0, or Infinity, causing documents to receive a score of 0 or be excluded from results entirely. This commonly occurs when field access patterns don't account for missing values or when mathematical operations result in null propagation.

Sample error

		{
  "error": {
    "root_cause": [
      {
        "type": "illegal_argument_exception",
        "reason": "script_score script returned an invalid score [-Infinity] for doc [0]. Must be a non-negative score!"
      }
    ],
    "type": "search_phase_execution_exception",
    "reason": "all shards failed",
    "phase": "query",
    "grouped": true,
    "failed_shards": [
      {
        "shard": 0,
        "index": "products",
        "node": "CxMTEjvKSEC0k0aTr4OM3A",
        "reason": {
          "type": "illegal_argument_exception",
          "reason": "script_score script returned an invalid score [-Infinity] for doc [0]. Must be a non-negative score!"
        }
      }
    ],
    "caused_by": {
      "type": "illegal_argument_exception",
      "reason": "script_score script returned an invalid score [-Infinity] for doc [0]. Must be a non-negative score!",
      "caused_by": {
        "type": "illegal_argument_exception",
        "reason": "script_score script returned an invalid score [-Infinity] for doc [0]. Must be a non-negative score!"
      }
    }
  },
  "status": 400
}
		
	

Problematic code

		{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "lang": "painless",
        "source": """
          double price = 0.0;
          double rating = 5.0;

          return Math.log(price) * rating;
        """
      }
    }
  }
}
		
	

Simulating problematic calculation

Root cause

The error occurs because of mathematical edge cases in calculations:

Math.log() with zero or negative values: Math.log(0) returns negative infinity, Math.log(-x) returns NaN.
Division by zero: Operations such as x/0 throw an arithmetic_exception.
NaN propagation: Any mathematical operation involving NaN results in NaN.
Infinity calculations: Operations with infinity often result in NaN or unexpected values.

When a script returns NaN, negative infinity, or other invalid numbers, Painless converts the score to 0.0, causing unexpected ranking behavior.

Solution: Add mathematical safety checks

Always validate mathematical inputs and handle edge cases:

		GET products/_search
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "lang": "painless",
        "source": """
          double price = 0.0;
          double rating = 5.0;

          double safePrice = Math.max(price, 1.0);

          // Calculate score with safety checks
          double logPrice = Math.log(safePrice);
          double score = logPrice * rating;

          // Handle NaN or infinity results
          if (Double.isNaN(score) || Double.isInfinite(score)) {
            return 1.0;
          }

          return Math.max(score, 0.1);
        """
      }
    }
  }
}
		
	

Ensure > 0 for log
Ensure a minimum positive score

Sample documents

		POST products/_doc
{
  "name": "Premium Laptop",
  "price": 999.99,
  "rating": 4.7,
  "category": "electronics"
}

POST products/_doc
{
  "name": "Free Software",
  "price": 0,
  "rating": 5.0,
  "category": "software"
}
		
	

Results

		{
  ...,
  "hits": {
    ...,
    "hits": [
      {
        "_index": "products",
        "_id": "j6gZNZkB0eMypkDYmmSC",
        "_score": 0.1,
        "_source": {
          "name": "Premium Laptop",
          "price": 999.99,
          "rating": 4.7,
          "category": "electronics"
        }
      },
      {
        "_index": "products",
        "_id": "kKgZNZkB0eMypkDYn2SP",
        "_score": 0.1,
        "_source": {
          "name": "Free Software",
          "price": 0,
          "rating": 5,
          "category": "software"
        }
      }
    ]
  }
}
		
	

Notes

Mathematical safety: Validate inputs for functions like Math.log().
Default values: Provide meaningful defaults for missing fields to maintain consistent scoring.
Minimum scores: Ensure scripts return positive values to avoid zero scores.
Null handling: Mathematical operations with null values propagate null throughout the calculation.