Understanding Elasticsearch Field Length Normalization: When and Why to Disable Norms

• "Why Are My Elasticsearch Scores Wrong?" -"Why are my documents with identical content scoring differently?"
• "How come shorter fields always seem to rank higher?"
• "Why doesn't my exact-match search return equal scores?"

If you've ever asked these questions while working with Elasticsearch, you're not alone. You've probably noticed that documents with the same matching terms sometimes receive unexpectedly different scores. What might appear as random scoring variations actually stems from one of Elasticsearch's most impactful – and often misunderstood – relevance features: field length normalization, also known as norms.

This built-in feature, while powerful for natural language search, can sometimes be your search results' worst enemy. It's like having an overeager assistant who assumes shorter is always better – helpful when summarizing novels, but problematic when dealing with product codes or categories. Let's unravel this mystery and discover when this "helpful" feature might be secretly sabotaging your search results, and more importantly, how to fix it.

When working with Elasticsearch, one of the most subtle yet impactful aspects of relevance scoring is field length normalization. While this feature is beneficial for natural language search, it can sometimes work against you depending on your use case. Let's dive deep into what norms are, when they help, when they hurt, and how to control them.

What are Norms?

Norms are scoring factors in Elasticsearch that contribute to how relevance is calculated for a document. One of their primary functions is field length normalization - making shorter fields score higher than longer ones when they contain the same search term.

A Real-World Example

Imagine you're building a recipe search engine. You have two fields:

• title: The recipe name
• instructions: The step-by-step cooking instructions

Let's look at two recipes:

{
  "title": "Simple Tomato Pasta",
  "instructions": "Boil pasta. Add tomato sauce. Serve."
}

{
  "title": "Tomato and Basil Pasta with Fresh Garden Herbs and Parmesan",
  "instructions": "1. Boil water and cook pasta until al dente. 2. In a pan, sauté garlic... [50 more detailed steps]"
}

When someone searches for "tomato pasta", with default norms enabled, the first recipe might score higher because:

• The term "tomato" in a shorter title field gets more weight
• The shorter instructions field doesn't dilute the overall score

When Norms Help

1. Article Search: When searching article content, a keyword appearing in a 100-word article might be more relevant than the same keyword in a 10,000-word article.

2. Product Descriptions: A product specifically about "bluetooth headphones" might have a shorter, more focused description than one that merely mentions bluetooth headphones as a compatible accessory.

When Norms Hurt

1. SKU/Product Code Search: Consider a product catalog with a product_codes field:

{
  "product_codes": ["ABC123", "XYZ789", "DEF456"]
}

vs

{
  "product_codes": ["ABC123"]
}

When searching for "ABC123", should the first product score lower just because it has more product codes? Probably not!

1. Category Lists: For an e-commerce site with product categories:

{
  "categories": ["Electronics", "Computers", "Laptops"]
}

vs

{
  "categories": ["Electronics"]
}

A search for "Electronics" should treat both products equally, regardless of how many categories they belong to.

How to Disable Norms

When you decide norms aren't appropriate for your use case, you can disable them in your mapping:

{
  "mappings": {
    "properties": {
      "product_codes": {
        "type": "text",
        "norms": false
      }
    }
  }
}

Impact of Disabling Norms

Positive Effects:

• More predictable scoring for structured data
• Reduced index size (saves 1 byte per field per document)
• Slightly improved indexing performance
• Reduced memory usage

What You Lose:

• Field-length normalization
• Index-time field boost capabilities

Best Practices

1. Enable norms for:

   • Full-text fields like article content, descriptions
   • Fields where length indicates relevance
   • Search scenarios requiring fine-tuned relevance scoring

2. Disable norms for:

   • Identifier fields
   • Category or tag lists
   • Structured data where field length doesn't indicate relevance
   • Fields used primarily for filtering rather than scoring

Implementation Strategy

When implementing norm changes in production:

1. Create a new index with updated mappings
2. Use aliases to point to the current index
3. Reindex data to the new index
4. Switch the alias to point to the new index
5. Remove the old index

Conclusion

Understanding and properly configuring norms is crucial for building effective search experiences. While Elasticsearch's defaults work well for natural language text, structured data often benefits from disabling norms. By carefully considering your use case and data characteristics, you can make informed decisions about norm configuration and improve both search relevance and performance.

Remember: The best configuration is one that matches your users' expectations of what makes a document relevant. Don't be afraid to experiment and test different approaches with real user queries and feedback.