dada
Jun 4, 2025
visibility 2020
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(4 votes)

Optimize your queries for performance and search relevance

When working with Search & Navigation, query optimization can have a massive impact on both performance and search quality. Here are some practical tips to help you tune your queries for better speed, lower resource usage, and more relevant search results.

Use single terms query instead of multiple term OR queries

If you're filtering on the same field with multiple term OR conditions, switch to using a terms filter. It’s more efficient and easier to maintain.
In this code this translates to the following:

❌ Bad:

.Filter(x => x.ModelSize.Match("S") | x.ModelSize.Match("M") | x.ModelSize.Match("L"))

✅ Good:

.Filter(x => x.ModelSize.In(new[] { "S", "M", "L" }))


The terms filter is cached in Elasticsearch. This significantly reduces CPU and memory load, speeds up queries, and minimizes shard access.

 

Improve Relevance by Boosting Exact Matches

By default, queries may return results based on stemmed words, synonyms, or even compound terms. To improve relevance, you can explicitly boost exact matches using a custom extension method like InStandardField() below.

public static class QueryStringSearchExtensions
{
    public static IQueriedSearch<TSource, QueryStringQuery> InStandardField<TSource, TExistingQuery>(
        this IQueriedSearch<TSource, TExistingQuery> search,
        Expression<Func<TSource, string>> fieldSelector,
        double? relativeImportance = null)
        where TExistingQuery : QueryStringQuery
    {
        fieldSelector.ValidateNotNullArgument("fieldSelector");
        return search.InField(
            search.Client.Conventions.FieldNameConvention.GetFieldName((Expression)fieldSelector),
            relativeImportance);
    }
}

Usage

    .InStandardField(x => x.Name, 5)         // Boost exact match by factor of 5
    .InStandardField(x => x.SearchText, 5)   // Boost exact match by factor of 5
    .InField(x => x.Name)                    // Standard search
    .InField(x => x.SearchText);             // Standard search

Example: Searching for "vei"

Before Workaround (No boosting):
Many of the matches are compound words or unrelated stems.

"Karriereveiledningskonferansen"
"Vegstrategi"
"Vegforvaltning"
"Fylkesveg"
"Elektrisk vei"
"Vegstrategi"
"Delstrategi veg"
"Nyheter fylkesveg"
"Vegamot AS"
"Stillinger veg"

After Workaround (Boosted relevance):
Results now prioritize exact matches:

"Elektrisk vei"
"Delstrategi veg"
"Stillinger veg"
"Kontaktinformasjon veg"
"Veg-prosjekter"
"Høringssvar delstrategi veg"
"Søknad om reklame langs veg"
"Fravik fra krav i vegnormal"
"Trøndelagsmodellen - skreddersydd veg til sikker jobb"
"Stenger veg i Overhalla"

As you can see, the relevance of results is dramatically improved. Exact matches like "veg" are ranked higher, and compound or less-relevant terms are deprioritized.

Summary

  • Replace term OR logic on the same field with terms filters to improve query performance.
  • Use field boosting via extensions like InStandardField() to elevate exact matches over stemmed, synonyms or compound terms.

By following these patterns, you'll get faster queries and better results — a win-win for both backend performance and user satisfaction.

Jun 04, 2025

Comments

Johan Book
Johan Book Jun 4, 2025 06:45 PM

Good stuff!

Stefan Holm Olsen
Stefan Holm Olsen Jun 7, 2025 06:26 PM

Great points, Daniel.

Your example about terms filter is not even that bad. There are much worse, out in the wild.

dada
dada Jun 11, 2025 11:23 AM

Thank you, Stefan! Yes, the original chain of OR term filters was actually much larger—imagine an OR filter for every possible size you can think of. I was just trying to get the point across using as few bytes as possible. :)

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