Relevance score (_score) in Elasticsearch - Step-by-Step Execution

Concept Flow - Relevance score (_score)

Query sent to Elasticsearch

↓

Documents matched

↓

Calculate _score for each document

↓

Sort documents by _score descending

↓

Return sorted documents with _score

When you search, Elasticsearch finds matching documents, calculates a relevance score (_score) for each, sorts them by this score, and returns the results.

Execution Sample

Elasticsearch

{
  "query": {
    "match": { "text": "apple" }
  }
}

This query searches for documents containing the word 'apple' and Elasticsearch calculates a _score for each match.

Execution Table

Step	Action	Documents Matched	_score Calculation	Result
1	Receive query for 'apple'	All documents checked	No score yet	Prepare to score matches
2	Match documents containing 'apple'	Doc1, Doc2, Doc3	Calculate _score based on term frequency and other factors	Scores assigned: Doc1=1.5, Doc2=2.3, Doc3=0.9
3	Sort documents by _score descending	Doc2, Doc1, Doc3	Sorting done	Doc2 highest relevance, Doc3 lowest
4	Return sorted documents with _score	Doc2, Doc1, Doc3	Scores included	User sees results ranked by relevance

💡 All matching documents scored and sorted; results returned to user.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
Documents Matched	[]	[Doc1, Doc2, Doc3]	[Doc1, Doc2, Doc3]	[Doc2, Doc1, Doc3]
_score	{}	{Doc1:1.5, Doc2:2.3, Doc3:0.9}	{Doc1:1.5, Doc2:2.3, Doc3:0.9}	{Doc2:2.3, Doc1:1.5, Doc3:0.9}

Key Moments - 2 Insights

Why does Doc2 have a higher _score than Doc1 even though both contain 'apple'?

Because _score depends on factors like how often 'apple' appears in the document and how rare the term is across all documents, as shown in step 2 of the execution_table.

Does a document with a higher _score always mean it contains more words from the query?

Not necessarily. _score also considers term rarity and field length, so a document with fewer but more important matches can have a higher _score, as seen in the scoring details in step 2.

Visual Quiz - 3 Questions

Look at the execution_table, what is the _score of Doc3 after step 2?

A 2.3

B 1.5

C 0.9

D 0

💡 Hint

Check the '_score Calculation' column in row for step 2.

At which step are the documents sorted by their _score?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look at the 'Action' column describing sorting in the execution_table.

If Doc1 had a higher term frequency for 'apple', how would the _score change in the variable_tracker?

A Doc1's _score would decrease after Step 2

B Doc1's _score would increase after Step 2

C Doc3's _score would increase after Step 2

D No change in any _score

💡 Hint

Refer to the '_score' values in variable_tracker after Step 2.

Concept Snapshot

Relevance score (_score) in Elasticsearch:
- Calculated for each matching document
- Based on term frequency, rarity, and field length
- Higher _score means more relevant
- Results sorted by _score descending
- Returned with documents for ranking

Full Transcript

When you send a search query to Elasticsearch, it checks all documents to find matches. For each matching document, Elasticsearch calculates a relevance score called _score. This score depends on how often the search terms appear, how rare those terms are, and other factors. After scoring, Elasticsearch sorts the documents by their _score from highest to lowest. Finally, it returns the sorted documents along with their _score values so you see the most relevant results first.