Numeric field types in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define an integer field in Elasticsearch mapping.

Elasticsearch

{
  "mappings": {
    "properties": {
      "age": { "type": "[1]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Adate

Btext

Ckeyword

Dinteger

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'text' instead of a numeric type.

Using 'keyword' which is for exact string matches.

The integer type is used to store whole numbers in Elasticsearch.

2fill in blank

medium

Complete the code to define a floating point number field in Elasticsearch mapping.

Elasticsearch

{
  "mappings": {
    "properties": {
      "price": { "type": "[1]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Afloat

Binteger

Ckeyword

Dboolean

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'integer' which only stores whole numbers.

Using 'boolean' which stores true/false values.

The float type stores decimal numbers in Elasticsearch.

3fill in blank

hard

Fix the error in the mapping by choosing the correct numeric type for a large integer value.

Elasticsearch

{
  "mappings": {
    "properties": {
      "population": { "type": "[1]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Ainteger

Blong

Cshort

Dbyte

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'integer' which has a smaller range.

Using 'byte' or 'short' which are for smaller numbers.

The long type is used for large integer values beyond the range of integer.

4fill in blank

hard

Fill both blanks to define a mapping with a double and a scaled_float field.

Elasticsearch

{
  "mappings": {
    "properties": {
      "rating": { "type": "[1]" },
      "price_scaled": { "type": "[2]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Adouble

Bscaled_float

Cfloat

Dinteger

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'integer' for decimal numbers.

Mixing up 'float' and 'scaled_float' types.

double stores double precision floating point numbers.
scaled_float stores floating numbers with a scaling factor for precision.

5fill in blank

hard

Fill all three blanks to define a mapping with byte, short, and integer numeric types.

Elasticsearch

{
  "mappings": {
    "properties": {
      "small_number": { "type": "[1]" },
      "medium_number": { "type": "[2]" },
      "large_number": { "type": "[3]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Abyte

Bshort

Cinteger

Dlong

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'long' for small numbers.

Mixing up the order of numeric types.

byte stores very small integers.
short stores small integers.
integer stores medium range integers.

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