Numeric field types in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this mapping for a numeric field?

Given this Elasticsearch mapping snippet, what is the type of the field age and what is its default behavior?

Elasticsearch

{
  "mappings": {
    "properties": {
      "age": {
        "type": "integer"
      }
    }
  }
}

A The field <code>age</code> is a 32-bit signed integer and supports range queries.

B The field <code>age</code> is a floating-point number and supports decimal values.

C The field <code>age</code> is a string and does not support numeric operations.

D The field <code>age</code> is a 64-bit unsigned integer.

💡 Hint

Think about the standard integer type in Elasticsearch and what it supports.

In Elasticsearch, the integer type is a 32-bit signed integer. It supports numeric operations like range queries. It does not support decimals or unsigned values.

❓ Predict Output

intermediate

What happens when you index a decimal number into a field of type long?

Consider this mapping and document indexing: Mapping: { "mappings": { "properties": { "price": { "type": "long" } } } } Document: { "price": 19.99 } What will Elasticsearch do?

A It will round the decimal 19.99 to 20 and store it as a long integer.

B It will store the decimal 19.99 exactly as a long integer without rounding.

C It will convert the decimal to a string and store it as text.

D It will reject the document and throw a mapping exception error.

💡 Hint

Think about how Elasticsearch handles decimal values for integer types.

Elasticsearch expects integer types like long to receive whole numbers. Indexing a decimal into a long field causes a mapping exception error.

🔧 Debug

advanced

Why does this mapping cause an error?

This mapping snippet causes an error when applied. Identify the cause: { "mappings": { "properties": { "score": { "type": "unsigned_long" } } } }

A The field name <code>score</code> is a reserved keyword and cannot be used.

B Elasticsearch does not support the <code>unsigned_long</code> type; only <code>long</code> is valid.

C The mapping is missing the <code>index</code> property which is required for numeric fields.

D The <code>unsigned_long</code> type requires a <code>coerce</code> property to be set.

💡 Hint

Check the official Elasticsearch numeric types supported.

Elasticsearch does not have an unsigned_long type. The valid integer types are byte, short, integer, and long. Using an unsupported type causes an error.

📝 Syntax

advanced

Which mapping snippet correctly defines a scaled_float field with a scaling factor of 100?

Choose the correct Elasticsearch mapping for a scaled_float field named rating with a scaling factor of 100.

A { "mappings": { "properties": { "rating": { "type": "float", "scaling_factor": 100 } } } }

B { "mappings": { "properties": { "rating": { "type": "scaled_float", "scale_factor": 100 } } } }

C { "mappings": { "properties": { "rating": { "type": "scaled_float", "scaling_factor": 100 } } } }

D { "mappings": { "properties": { "rating": { "type": "scaled_float", "scaling": 100 } } } }

💡 Hint

Check the exact property name for scaling factor in the official docs.

The correct property name is scaling_factor. Option C uses the correct type and property name. Other options have wrong property names or types.

🚀 Application

expert

How many numeric fields are created by this mapping?

Given this Elasticsearch mapping, how many numeric fields are defined? { "mappings": { "properties": { "id": { "type": "keyword" }, "count": { "type": "integer" }, "price": { "type": "scaled_float", "scaling_factor": 100 }, "rating": { "type": "float" }, "timestamp": { "type": "date" } } } }

A 3

B 2

C 5

D 4

💡 Hint

Count only fields with numeric types like integer, float, scaled_float.

The numeric fields are count (integer), price (scaled_float), and rating (float). The id is keyword and timestamp is date, so they are not numeric.