Numeric field types in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are numeric field types in Elasticsearch?

Numeric field types in Elasticsearch are data types used to store numbers, such as integers and floating-point values, enabling efficient searching and sorting.

beginner

Name three integer numeric field types in Elasticsearch.

Three integer numeric field types are integer (32-bit), long (64-bit), and short (16-bit).

intermediate

What is the difference between float and double numeric types?

float stores 32-bit floating-point numbers with less precision, while double stores 64-bit floating-point numbers with higher precision.

intermediate

Why choose scaled_float over float in Elasticsearch?

scaled_float stores floating-point numbers as scaled integers, improving accuracy and performance for decimal values by avoiding floating-point rounding errors.

advanced

How does Elasticsearch store numeric fields internally?

Elasticsearch stores numeric fields as binary data optimized for fast range queries, sorting, and aggregations, using different sizes depending on the numeric type.

Which numeric field type in Elasticsearch is 64-bit integer?

A long

B integer

C short

D byte

long is the 64-bit integer type in Elasticsearch, suitable for large integer values.

What numeric type would you use for a 32-bit floating-point number?

A scaled_float

B double

C float

D integer

float stores 32-bit floating-point numbers in Elasticsearch.

Which numeric type stores decimal values as scaled integers?

A double

B long

C byte

D scaled_float

scaled_float stores decimal numbers as scaled integers to improve precision.

Which numeric type uses the least storage size?

A byte

B integer

C long

D double

byte uses 8 bits, the smallest storage size among numeric types.

Why is choosing the correct numeric type important in Elasticsearch?

A To enable full-text search

B To improve query speed and storage efficiency

C To avoid using text fields

D To make the data human-readable

Choosing the right numeric type optimizes storage and speeds up queries like sorting and range searches.

Explain the main numeric field types in Elasticsearch and when to use each.

Think about size and precision differences.

Describe how Elasticsearch stores numeric fields and why this matters for performance.

Consider how data format affects speed.

Practice

1.

Which numeric field type in Elasticsearch should you use to store whole numbers without decimals?

easy

A. double

B. integer

C. float

D. text

Solution

1. Step 1: Understand numeric field types

   Integer type stores whole numbers without decimals, suitable for counts or IDs.

2. Step 2: Compare with other types

   Float and double store decimal numbers; text is for strings, not numbers.

3. Final Answer:

   integer -> Option B

4. Quick Check:

   Whole numbers = integer [OK]

Hint: Whole numbers use integer type, decimals need float/double [OK]

Common Mistakes:

• Choosing float or double for whole numbers
• Using text type for numbers
• Confusing integer with long for small numbers

2.

Which of the following is the correct way to define a long numeric field in an Elasticsearch mapping?

{
  "mappings": {
    "properties": {
      "user_id": {
        "type": "_____"
      }
    }
  }
}

easy

A. "long"

B. "integer"

C. "float"

D. "keyword"

Solution

1. Step 1: Identify the field type for large whole numbers

   Long type stores larger whole numbers than integer, suitable for big IDs.

2. Step 2: Match the correct type string in mapping

   Use "long" as the type value in the mapping JSON for such fields.

3. Final Answer:

   "long" -> Option A

4. Quick Check:

   Big whole numbers = "long" [OK]

Hint: Use "long" for large whole numbers in mapping [OK]

Common Mistakes:

• Using "integer" for very large numbers
• Using "keyword" which is for text
• Using "float" for whole numbers

3.

Given this Elasticsearch mapping snippet, what type of number can price store?

{
  "mappings": {
    "properties": {
      "price": {
        "type": "float"
      }
    }
  }
}

medium

A. Boolean true/false values

B. Only whole numbers

C. Text values representing numbers

D. Decimal numbers with floating point

Solution

1. Step 1: Check the type assigned to price

   The type is "float", which stores decimal numbers with floating point precision.

2. Step 2: Understand what float means

   Float allows decimals, unlike integer or long which store whole numbers only.

3. Final Answer:

   Decimal numbers with floating point -> Option D

4. Quick Check:

   Float type = decimals allowed [OK]

Hint: Float type stores decimal numbers, not just whole numbers [OK]

Common Mistakes:

• Thinking float stores only whole numbers
• Confusing float with text or boolean
• Assuming float stores exact decimal precision always

4.

What is wrong with this Elasticsearch mapping for a numeric field?

{
  "mappings": {
    "properties": {
      "score": {
        "type": "integer",
        "index": "not_analyzed"
      }
    }
  }
}

medium

A. 'type' should be 'float' instead of 'integer'

B. 'score' field name is not allowed

C. 'index' parameter is invalid for numeric fields

D. Mapping syntax is missing a closing brace

Solution

1. Step 1: Review the 'index' parameter usage

   Numeric fields do not support 'index' with 'not_analyzed'; this is for text fields.

2. Step 2: Confirm correct mapping for numeric fields

   Numeric fields are indexed by default and do not use 'not_analyzed'.

3. Final Answer:

   'index' parameter is invalid for numeric fields -> Option C

4. Quick Check:

   Numeric fields don't use 'index': 'not_analyzed' [OK]

Hint: Don't use 'index':'not_analyzed' on numeric fields [OK]

Common Mistakes:

• Using text field options on numeric fields
• Changing type unnecessarily
• Ignoring Elasticsearch mapping syntax rules

5.

You want to store product weights that can be very large and may have decimals. Which Elasticsearch numeric field type is best?

hard

A. double

B. long

C. float

D. integer

Solution

1. Step 1: Analyze the requirements for product weights

   Weights can be very large and have decimals, so the type must support large range and decimals.

2. Step 2: Compare float and double types

   Float supports decimals but has less precision and smaller range than double.

3. Step 3: Choose the best type for large decimal numbers

   Double supports large decimal numbers with higher precision, suitable for weights.

4. Final Answer:

   double -> Option A

5. Quick Check:

   Large decimals = double type [OK]

Hint: Use double for large decimal numbers needing precision [OK]

Common Mistakes:

• Choosing integer or long which don't support decimals
• Using float which may lose precision
• Ignoring size and precision needs