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.