Numeric field types in Elasticsearch - Step-by-Step Execution

Concept Flow - Numeric field types

Define Numeric Field Type

↓

Choose Specific Type: integer, long, float, double, etc.

↓

Index Numeric Data

↓

Store and Search Numeric Values Efficiently

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Use in Queries and Aggregations

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Results

This flow shows how numeric field types are defined, indexed, and used in Elasticsearch for efficient storage and querying.

Execution Sample

Elasticsearch

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

Defines a numeric field 'age' of type integer in Elasticsearch mapping.

Execution Table

Step	Action	Field Type	Input Value	Stored Format	Query Behavior
1	Define field 'age'	integer	N/A	N/A	N/A
2	Index document with age=30	integer	30	Stored as 32-bit integer	Can be searched and aggregated numerically
3	Query for age > 25	integer	N/A	N/A	Matches documents where age is greater than 25
4	Index document with age=30.5	integer	30.5	Error or rounded	Rejected or rounded depending on settings
5	Define field 'price'	float	N/A	N/A	N/A
6	Index document with price=19.99	float	19.99	Stored as 32-bit float	Supports range queries and aggregations
7	Query for price < 20.0	float	N/A	N/A	Matches documents where price is less than 20.0
8	Exit	N/A	N/A	N/A	All numeric fields indexed and ready for queries

💡 All numeric fields are indexed and ready for numeric queries and aggregations.

Variable Tracker

Variable	Start	After Step 2	After Step 4	After Step 6
age	undefined	30	error or 30	30
price	undefined	undefined	undefined	19.99

Key Moments - 3 Insights

Why does indexing a decimal value into an integer field cause an error or rounding?

Integer fields only accept whole numbers. As shown in execution_table step 4, input 30.5 is invalid or rounded because integer type cannot store decimals.

How does Elasticsearch store numeric values internally?

As shown in steps 2 and 6, integers are stored as 32-bit integers, floats as 32-bit floats, enabling efficient storage and fast numeric queries.

Can numeric fields be used in range queries and aggregations?

Yes, as shown in steps 3 and 7, numeric fields support range queries and aggregations for filtering and summarizing data.

Visual Quiz - 3 Questions

Look at the execution_table at step 4. What happens when indexing 30.5 into an integer field?

A It stores 30.5 exactly

B It causes an error or rounds the value

C It stores 0

D It converts to string

💡 Hint

Check execution_table row 4 under Stored Format and Query Behavior

At which step does Elasticsearch index a float value?

A Step 2

B Step 4

C Step 6

D Step 3

💡 Hint

Look for 'float' in Field Type column in execution_table

If you want to store large whole numbers, which numeric type should you choose?

A long

B float

C integer

D double

💡 Hint

Consider numeric types that support larger ranges than integer

Concept Snapshot

Numeric field types in Elasticsearch define how numbers are stored and queried.
Common types: integer, long, float, double.
Integer types store whole numbers; float/double store decimals.
Numeric fields enable efficient range queries and aggregations.
Invalid data types cause errors or rounding.
Choose type based on value range and precision needed.

Full Transcript

This visual execution trace shows how numeric field types work in Elasticsearch. First, you define a numeric field like 'age' as integer. When you index a document with age=30, it stores the value as a 32-bit integer. Queries like age > 25 work efficiently. If you try to index a decimal like 30.5 into an integer field, it causes an error or rounds the value. For decimal numbers, you define fields as float or double, which store numbers with decimals and support range queries. Numeric fields enable fast searching and aggregations on numbers. Choosing the right numeric type depends on the range and precision of your data.