Cardinality aggregation in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this cardinality aggregation query?

Given an Elasticsearch index with documents containing a field user_id, what will be the value of value in the aggregation result?

Elasticsearch

{
  "size": 0,
  "aggs": {
    "unique_users": {
      "cardinality": {
        "field": "user_id"
      }
    }
  }
}

A The count of unique user_id values in the index

B The total number of documents in the index

C The sum of all user_id values

D The average of all user_id values

💡 Hint

Cardinality aggregation counts unique values of a field.

The cardinality aggregation returns the count of distinct values for the specified field, here user_id.

❓ Predict Output

intermediate

What does this cardinality aggregation with precision_threshold do?

Consider this query with precision_threshold set to 1000. What effect does this have on the cardinality result?

Elasticsearch

{
  "size": 0,
  "aggs": {
    "unique_users": {
      "cardinality": {
        "field": "user_id",
        "precision_threshold": 1000
      }
    }
  }
}

A It limits the count to a maximum of 1000 unique values

B It sorts the unique user_id values before counting

C It decreases accuracy for counts above 1000 unique values

D It increases accuracy for counts up to 1000 unique values, possibly using more memory

💡 Hint

Precision threshold controls accuracy and memory tradeoff.

Setting precision_threshold to 1000 increases accuracy for cardinality counts up to 1000 unique values by using more memory.

🔧 Debug

advanced

Why does this cardinality aggregation return a lower count than expected?

This query returns a cardinality count lower than the actual unique user_id count. What is the most likely cause?

Elasticsearch

{
  "size": 0,
  "aggs": {
    "unique_users": {
      "cardinality": {
        "field": "user_id",
        "precision_threshold": 100
      }
    }
  }
}

A The size parameter should be greater than 0 to get correct counts

B The field user_id is not indexed, so aggregation fails

C The precision_threshold is too low, causing approximate undercounting

D The query is missing a filter to include all documents

💡 Hint

Lower precision_threshold can cause approximate results.

A low precision_threshold reduces memory usage but can cause the cardinality aggregation to underestimate the true count.

🧠 Conceptual

advanced

What is the main tradeoff when using cardinality aggregation in Elasticsearch?

Choose the best description of the tradeoff involved in using cardinality aggregation.

A Document relevance versus aggregation precision

B Accuracy of unique counts versus memory and CPU usage

C Number of shards versus index size

D Speed of query execution versus network bandwidth

💡 Hint

Cardinality uses approximate algorithms to save resources.

Cardinality aggregation uses approximate algorithms that trade accuracy for lower memory and CPU usage.

❓ Predict Output

expert

What is the cardinality aggregation result for nested fields?

Given documents with nested field comments.user_id, which aggregation correctly counts unique user_id values across all comments?

Elasticsearch

{
  "size": 0,
  "aggs": {
    "unique_commenters": {
      "cardinality": {
        "field": "comments.user_id"
      }
    }
  }
}

A Counts unique user_id values across all nested comments correctly

B Returns zero because cardinality does not work on nested fields

C Counts unique user_id values only from the first nested comment per document

D Throws an error because nested fields require a nested aggregation wrapper

💡 Hint

Metric aggregations like cardinality work directly on nested fields.

Cardinality aggregation on nested fields correctly counts unique values across all nested objects without requiring a nested wrapper.

Practice

1.

What does the cardinality aggregation in Elasticsearch do?

easy

A. Calculates the sum of values in a field

B. Sorts documents by a field

C. Finds the average value of a field

D. Counts the number of unique values in a field

Solution

1. Step 1: Understand the purpose of cardinality aggregation

   Cardinality aggregation is designed to count unique values in a specified field.

2. Step 2: Compare with other aggregations

   Sum and average aggregations calculate totals and averages, not unique counts. Sorting is unrelated.

3. Final Answer:

   Counts the number of unique values in a field -> Option D

4. Quick Check:

   Cardinality = Unique count [OK]

Hint: Cardinality = count unique values fast [OK]

Common Mistakes:

• Confusing cardinality with sum or average
• Thinking it returns exact counts always
• Mixing it with sorting or filtering

2.

Which of the following is the correct syntax to define a cardinality aggregation on the field user_id in Elasticsearch?

{
  "aggs": {
    "unique_users": {
      "cardinality": {
        "field": "user_id"
      }
    }
  }
}

easy

A. { "aggs": { "unique_users": { "cardinality": { "field": "user_id" } } } }

B. { "aggs": { "unique_users": { "terms": { "field": "user_id" } } } }

C. { "aggs": { "unique_users": { "sum": { "field": "user_id" } } } }

D. { "aggs": { "unique_users": { "avg": { "field": "user_id" } } } }

Solution

1. Step 1: Identify the aggregation type for unique counts

   The cardinality aggregation is used to count unique values, so the key must be "cardinality".

2. Step 2: Check the field name and structure

   The field is correctly specified as "user_id" inside the cardinality object.

3. Final Answer:

   { "aggs": { "unique_users": { "cardinality": { "field": "user_id" } } } } -> Option A

4. Quick Check:

   Correct syntax uses "cardinality" with "field" [OK]

Hint: Cardinality uses "cardinality" key with "field" [OK]

Common Mistakes:

• Using terms aggregation instead of cardinality
• Using sum or avg aggregations for unique counts
• Incorrect JSON structure or missing braces

3.

Given the following Elasticsearch aggregation query:

{
  "aggs": {
    "unique_countries": {
      "cardinality": {
        "field": "country"
      }
    }
  }
}

If the indexed documents have countries: ["US", "US", "FR", "DE", "FR", "JP"], what is the expected approximate output value for unique_countries?

medium

A. 3

B. 4

C. 5

D. 6

Solution

1. Step 1: Identify unique countries in the list

   The countries are US, FR, DE, JP. Counting unique values gives 4.

2. Step 2: Understand cardinality output

   Cardinality aggregation returns approximate unique counts, which should be 4 here.

3. Final Answer:

   4 -> Option B

4. Quick Check:

   Unique countries count = 4 [OK]

Hint: Count distinct values in the field for cardinality [OK]

Common Mistakes:

• Counting duplicates as unique
• Confusing total documents with unique count
• Expecting exact count always (approximate)

4.

Identify the error in this cardinality aggregation query:

{
  "aggs": {
    "unique_users": {
      "cardinality": {
        "field_name": "user_id"
      }
    }
  }
}

medium

A. Aggregation name cannot be "unique_users"

B. Missing closing brace for "aggs"

C. "field_name" should be "field"

D. Cardinality aggregation requires a "size" parameter

Solution

1. Step 1: Check the field parameter name

   The correct key for specifying the field is "field", not "field_name".

2. Step 2: Verify JSON structure and parameters

   The braces are correctly closed, aggregation name is valid, and "size" is not required for cardinality.

3. Final Answer:

   "field_name" should be "field" -> Option C

4. Quick Check:

   Use "field" key for cardinality field [OK]

Hint: Use "field", not "field_name" in cardinality [OK]

Common Mistakes:

• Using wrong key "field_name" instead of "field"
• Adding unnecessary parameters like "size"
• Misnaming aggregation or missing braces

5.

You want to find the approximate number of unique products sold in each category using Elasticsearch. Which aggregation setup correctly combines terms and cardinality aggregations?

{
  "aggs": {
    "by_category": {
      "terms": { "field": "category" },
      "aggs": {
        "unique_products": {
          "cardinality": { "field": "product_id" }
        }
      }
    }
  }
}

hard

A. This setup groups by category and counts unique products per category

B. This setup counts unique categories per product

C. This setup sums product_id values per category

D. This setup finds average product_id per category

Solution

1. Step 1: Understand the terms aggregation

   "terms" groups documents by unique values in "category" field.

2. Step 2: Understand the nested cardinality aggregation

   Inside each category bucket, cardinality counts unique "product_id" values.

3. Final Answer:

   This setup groups by category and counts unique products per category -> Option A

4. Quick Check:

   Terms + cardinality = unique counts per group [OK]

Hint: Nest cardinality inside terms to count unique per group [OK]

Common Mistakes:

• Confusing aggregation order or nesting
• Using cardinality on category instead of product_id
• Expecting sum or average instead of unique count