Function score query in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start a function score query with the correct key.

Elasticsearch

{
  "[1]": {
    "query": {
      "match_all": {}
    }
  }
}

Drag options to blanks, or click blank then click option'

Afunction_score

Bmatch

Cbool

Dterm

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'match' or 'bool' instead of 'function_score' as the root key.

The top-level key for a function score query is function_score.

2fill in blank

medium

Complete the code to add a weight function inside the function score query.

Elasticsearch

{
  "function_score": {
    "query": { "match_all": {} },
    "functions": [
      {
        "[1]": 5
      }
    ]
  }
}

Drag options to blanks, or click blank then click option'

Arandom_score

Bscript_score

Cweight

Dfield_value_factor

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'script_score' or 'random_score' when a fixed weight is needed.

The weight function assigns a fixed weight to matching documents.

3fill in blank

hard

Fix the error in the function score query by completing the missing key for the filter.

Elasticsearch

{
  "function_score": {
    "query": { "match_all": {} },
    "functions": [
      {
        "filter": { "term": { "status": "active" } },
        "weight": 3
      }
    ],
    "[1]": "sum"
  }
}

Drag options to blanks, or click blank then click option'

Amax_boost

Bscore_mode

Cboost_mode

Dmin_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'boost_mode' or 'min_score' instead of 'score_mode' for combining function scores.

The score_mode defines how scores from functions are combined, e.g., 'sum'.

4fill in blank

hard

Fill both blanks to create a function score query that uses a field value factor and multiplies the score.

Elasticsearch

{
  "function_score": {
    "query": { "match": { "title": "[1]" } },
    "functions": [
      {
        "field_value_factor": {
          "field": "popularity"
        }
      }
    ],
    "[2]": "multiply"
  }
}

Drag options to blanks, or click blank then click option'

Aelasticsearch

Bscore_mode

Cboost_mode

Dquery_string

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'query_string' instead of the search term.

Confusing 'boost_mode' with 'score_mode'.

The query matches the term 'elasticsearch' in the title, and score_mode is set to 'multiply' to multiply scores.

5fill in blank

hard

Fill all three blanks to build a function score query with a script score, a filter, and a boost mode.

Elasticsearch

{
  "function_score": {
    "query": { "match_all": {} },
    "functions": [
      {
        "[1]": {
          "script": {
            "source": "doc['views'].value / 10"
          }
        },
        "filter": { "term": { "category": "[2]" } }
      }
    ],
    "[3]": "replace"
  }
}

Drag options to blanks, or click blank then click option'

Ascript_score

Btechnology

Cboost_mode

Dweight

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'weight' instead of 'script_score' for the function.

Confusing 'score_mode' with 'boost_mode'.

The function uses a script_score to calculate score, filters by category 'technology', and sets boost_mode to 'replace' to use function score only.

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