Function score query in Elasticsearch - Mini Project: Build & Apply

Function score query

📖 Scenario: You are working with a search engine that ranks products based on their relevance and popularity. You want to boost the scores of products that have a high number of reviews to show them higher in search results.

🎯 Goal: Create an Elasticsearch function_score query that boosts product scores based on the review_count field using a field value factor.

📋 What You'll Learn

Create a basic match_all query inside the function_score query.

Add a field_value_factor function that uses the review_count field.

Set the factor to 1.2 to boost scores.

Print the complete JSON query.

💡 Why This Matters

🌍 Real World

Boosting product scores based on popularity helps users find the most reviewed and trusted products quickly.

💼 Career

Understanding function score queries is important for roles involving search engine optimization and Elasticsearch query tuning.

1

Create the base query

Create a variable called query and assign it a JSON object with a match_all query inside a function_score query. The structure should be: {"query": {"function_score": {"query": {"match_all": {}}}}}.

Elasticsearch

# Create the base function_score query with match_all
# Your code here

Hint

Start by creating a dictionary named query with the keys exactly as shown.

2

Add the field_value_factor function

Add a functions list inside the function_score query in query. Inside this list, add a dictionary with a field_value_factor key that uses the field review_count.

Elasticsearch

query = {
    "query": {
        "function_score": {
            "query": {
                "match_all": {}
            },
            # Add functions list with field_value_factor here
        }
    }
}
# Your code here

Hint

Inside function_score, add a functions key with a list containing a dictionary for field_value_factor.

3

Set the boost factor

Inside the field_value_factor dictionary, add a factor key with the value 1.2 to boost the score based on review_count.

Elasticsearch

query = {
    "query": {
        "function_score": {
            "query": {
                "match_all": {}
            },
            "functions": [
                {
                    "field_value_factor": {
                        "field": "review_count",
                        # Add factor 1.2 here
                    }
                }
            ]
        }
    }
}
# Your code here

Hint

Add the factor key with value 1.2 inside field_value_factor.

4

Print the final query

Print the variable query to display the complete function score query JSON.

Elasticsearch

query = {
    "query": {
        "function_score": {
            "query": {
                "match_all": {}
            },
            "functions": [
                {
                    "field_value_factor": {
                        "field": "review_count",
                        "factor": 1.2
                    }
                }
            ]
        }
    }
}

# Print the query
# Your code here

Hint

Use print(query) to show the final JSON object.

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