Function score query in Elasticsearch

Introduction

A function score query lets you change how search results are scored or ranked by applying custom functions. This helps you show more relevant results based on your own rules.

You want to boost documents that have a higher value in a certain field, like popularity or rating.

You want to reduce the score of documents that are older or less relevant.

You want to combine a normal search with a custom scoring function to influence the order of results.

You want to apply multiple scoring functions with different weights to fine-tune ranking.

You want to add randomness to scores for A/B testing or varied results.

Syntax

Elasticsearch

{
  "function_score": {
    "query": { ... },
    "functions": [
      {
        "field_value_factor": {
          "field": "field_name",
          "factor": 1.2,
          "modifier": "sqrt"
        },
        "weight": 2
      },
      {
        "random_score": {}
      }
    ],
    "score_mode": "sum",
    "boost_mode": "multiply"
  }
}

The query part defines which documents to search.

The functions array contains scoring functions to adjust scores.

Examples

This boosts documents with higher popularity field values.

Elasticsearch

{
  "function_score": {
    "query": { "match": { "title": "elasticsearch" } },
    "functions": [
      {
        "field_value_factor": {
          "field": "popularity",
          "factor": 1.5
        }
      }
    ]
  }
}

This adds a random score to all documents to shuffle results.

Elasticsearch

{
  "function_score": {
    "query": { "match_all": {} },
    "functions": [
      {
        "random_score": {
          "seed": 12345
        }
      }
    ],
    "score_mode": "sum"
  }
}

This replaces the original score with a fixed weight for active documents.

Elasticsearch

{
  "function_score": {
    "query": { "term": { "status": "active" } },
    "functions": [
      {
        "weight": 3
      }
    ],
    "boost_mode": "replace"
  }
}

Sample Program

This query searches for documents with "coffee" in the description. It boosts scores based on the rating field using a logarithmic scale and adds extra weight to documents in the "beverages" category. The scores from functions are summed and then multiplied with the original score.

Elasticsearch

{
  "query": {
    "function_score": {
      "query": {
        "match": {
          "description": "coffee"
        }
      },
      "functions": [
        {
          "field_value_factor": {
            "field": "rating",
            "factor": 2,
            "modifier": "log1p"
          }
        },
        {
          "weight": 5,
          "filter": {
            "term": { "category": "beverages" }
          }
        }
      ],
      "score_mode": "sum",
      "boost_mode": "multiply"
    }
  }
}

Important Notes

You can combine multiple functions to fine-tune scoring.

Use score_mode to decide how function scores combine (sum, multiply, max, etc.).

Use boost_mode to decide how function scores combine with the original query score.

Summary

Function score query lets you customize how search results are ranked.

You can boost or reduce scores using field values, weights, or random scores.

It helps make search results more relevant to your needs.

Practice

1. What is the main purpose of a function_score query in Elasticsearch?

easy

A. To update documents in the index

B. To filter documents based on exact matches only

C. To customize how search results are ranked by modifying their scores

D. To delete documents matching a query

Solution

1. Step 1: Understand the role of function_score query

   The function_score query is designed to change the score of documents returned by a query, allowing customization of ranking.

2. Step 2: Compare with other query types

   Filtering or updating documents are different operations; function_score specifically adjusts scoring.

3. Final Answer:

   To customize how search results are ranked by modifying their scores -> Option C

4. Quick Check:

   Function score query = Custom ranking [OK]

Hint: Function score changes ranking by adjusting scores [OK]

Common Mistakes:

• Confusing function_score with filtering queries
• Thinking it updates or deletes documents
• Assuming it only filters exact matches

2. Which of the following is the correct basic structure of a function_score query in Elasticsearch?

easy

A. { "function_score": { "query": { ... }, "functions": [ ... ] } }

B. { "function_score": [ { "query": { ... } }, { "functions": [ ... ] } ] }

C. { "query": { "function_score": { "functions": [ ... ] } } }

D. { "functions": { "function_score": { "query": { ... } } } }

Solution

1. Step 1: Recall function_score query JSON format

   The function_score query wraps a query and a list of scoring functions inside an object with keys "query" and "functions".

2. Step 2: Validate option structure

   { "function_score": { "query": { ... }, "functions": [ ... ] } } correctly shows the function_score object containing "query" and "functions" keys as expected.

3. Final Answer:

   { "function_score": { "query": { ... }, "functions": [ ... ] } } -> Option A

4. Quick Check:

   Correct JSON structure = { "function_score": { "query": { ... }, "functions": [ ... ] } } [OK]

Hint: Look for "function_score" with nested "query" and "functions" keys [OK]

Common Mistakes:

• Using arrays instead of objects for function_score
• Placing functions outside function_score
• Incorrect nesting of query and functions

3. Given this function_score query snippet, what is the effect of the weight parameter?

{
  "function_score": {
    "query": { "match": { "title": "elasticsearch" } },
    "functions": [
      { "weight": 3 }
    ]
  }
}

medium

A. It adds 3 points to the original score

B. It multiplies the original score of matching documents by 3

C. It filters out documents with score less than 3

D. It sets the score of all documents to 3

Solution

1. Step 1: Understand the weight function in function_score

   The weight function multiplies the score of matching documents by the given weight value.

2. Step 2: Analyze the query snippet

   The query matches documents with "title" containing "elasticsearch" and applies a weight of 3, multiplying their scores by 3.

3. Final Answer:

   It multiplies the original score of matching documents by 3 -> Option B

4. Quick Check:

   Weight multiplies score = It multiplies the original score of matching documents by 3 [OK]

Hint: Weight multiplies scores, not adds or filters [OK]

Common Mistakes:

• Thinking weight adds to score instead of multiplying
• Confusing weight with filtering
• Assuming weight sets fixed score

4. Identify the error in this function_score query:

{
  "function_score": {
    "query": { "match": { "content": "search" } },
    "functions": {
      "weight": 2
    }
  }
}

medium

A. Missing "score_mode" field

B. "weight" cannot be used inside functions

C. "query" must be inside "functions"

D. "functions" should be an array, not an object

Solution

1. Step 1: Check the type of "functions" field

   The "functions" field must be an array of function objects, not a single object.

2. Step 2: Identify the error in the query

   Here, "functions" is incorrectly given as an object with "weight" key instead of an array containing that object.

3. Final Answer:

   "functions" should be an array, not an object -> Option D

4. Quick Check:

   Functions must be array = "functions" should be an array, not an object [OK]

Hint: Functions must be an array of objects [OK]

Common Mistakes:

• Using object instead of array for functions
• Placing query inside functions
• Assuming score_mode is mandatory

5. You want to boost documents with a popularity field greater than 10 by a factor of 5 using a function_score query. Which snippet correctly applies this boost?

hard

A. { "function_score": { "query": { "match_all": {} }, "functions": [ { "filter": { "range": { "popularity": { "gt": 10 } } }, "weight": 5 } ] } }

B. { "function_score": { "query": { "range": { "popularity": { "gt": 10 } } }, "weight": 5 } }

C. { "function_score": { "query": { "match_all": {} }, "weight": 5, "filter": { "range": { "popularity": { "gt": 10 } } } } }

D. { "function_score": { "query": { "match_all": {} }, "functions": { "weight": 5, "filter": { "range": { "popularity": { "gt": 10 } } } } } }

Solution

1. Step 1: Understand boosting with filter and weight

   To boost only documents matching a condition, use a function with a "filter" and a "weight" inside the "functions" array.

2. Step 2: Analyze each option

   { "function_score": { "query": { "match_all": {} }, "functions": [ { "filter": { "range": { "popularity": { "gt": 10 } } }, "weight": 5 } ] } } correctly uses "functions" as an array with a filter on popularity > 10 and weight 5. Other options misuse weight placement or structure.

3. Final Answer:

   { "function_score": { "query": { "match_all": {} }, "functions": [ { "filter": { "range": { "popularity": { "gt": 10 } } }, "weight": 5 } ] } } -> Option A

4. Quick Check:

   Filter + weight inside functions array = { "function_score": { "query": { "match_all": {} }, "functions": [ { "filter": { "range": { "popularity": { "gt": 10 } } }, "weight": 5 } ] } } [OK]

Hint: Use filter and weight inside functions array to boost conditionally [OK]

Common Mistakes:

• Placing weight outside functions array
• Using object instead of array for functions
• Applying weight directly on query instead of functions