Boosting and Relevancy Scoring
Learn techniques to influence the relevancy score of documents using query boosting, field boosting, and custom scoring functions.
Boosting and Relevancy Scoring is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Relevancy Matters
When you search for something, you don't just want any results; you want the best results. This is where relevancy comes in!
Relevancy helps search engines, like Elasticsearch, decide which documents are most important or 'relevant' to your query and present them first.
Meet the _score
In Elasticsearch, every document that matches your search query gets a numeric value called the _score. This score represents how relevant that document is to your query.
- A higher
_scoremeans the document is considered more relevant. - Elasticsearch uses algorithms (like BM25) to calculate this score, considering factors like how often a term appears and its uniqueness.
Introduction to Boosting
While Elasticsearch calculates relevancy automatically, you often want to guide it. This is where boosting comes in!
Boosting allows you to manually increase or decrease the importance of specific query clauses or fields, directly influencing the _score of matching documents.
Boosting Entire Queries
You can apply a boost parameter to an entire query clause. A boost value greater than 1.0 increases its impact, while a value less than 1.0 decreases it.
The default boost value is 1.0, meaning no special emphasis.
Query Boost in Action
Let's say you're searching for 'coding' and want matches in the description field to be twice as important as other parts of your query.
You can add "boost": 2 to that specific match clause:
GET /my_index/_search
{
"query": {
"match": {
"description": {
"query": "coding",
"boost": 2
}
}
}
}Prioritizing Specific Fields
Often, a match in one field is inherently more valuable than a match in another. For example, finding a keyword in a document's title is usually more relevant than finding it in its content.
Field boosting lets you specify this importance directly within your query.
Field Boost Example
Using the ^ (caret) operator after a field name, you can assign a boost factor. Here, a match in title is 3 times more important than a match in description:
GET /my_index/_search
{
"query": {
"multi_match": {
"query": "quick brown fox",
"fields": [ "title^3", "description^1" ]
}
}
}Beyond Simple Boosting
For even more control over relevancy, Elasticsearch offers the function_score query. This powerful query type allows you to apply custom scoring logic to documents.
You can factor in things like a document's popularity, recency, or specific numeric field values to influence its _score.
function_score Basic Example
Here's a simple function_score example. It searches for 'elastic' and then boosts the score based on the views_count field, multiplying the base score by a factor derived from views_count.
GET /my_index/_search
{
"query": {
"function_score": {
"query": { "match": { "text": "elastic" } },
"field_value_factor": {
"field": "views_count",
"factor": 1.2,
"modifier": "log1p",
"missing": 1
},
"boost_mode": "multiply"
}
}
}Boost Your Knowledge!
Test your understanding of boosting and relevancy in Elasticsearch!
Relevancy Tuned!
Great job! You've learned how to take control of relevancy in Elasticsearch.
- The
_scoredictates a document's importance. - Query boosting lets you emphasize entire query clauses.
- Field boosting prioritizes matches in specific fields.
- The
function_scorequery provides advanced, custom relevancy adjustments.
By using these techniques, you can ensure your users always find the most relevant information first!
Frequently asked questions
Is the “Boosting and Relevancy Scoring” lesson free?
Yes — the full text of “Boosting and Relevancy Scoring” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.
What will I learn in “Boosting and Relevancy Scoring”?
Learn techniques to influence the relevancy score of documents using query boosting, field boosting, and custom scoring functions. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Elasticsearch & Full Text Search Systems?
No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Boosting and Relevancy Scoring” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?
Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Analyzers, Tokenizers, Filters
- Customizing Text Analyzers
- Boosting and Relevancy Scoring
- Synonyms and Stemming