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$type (element query)

APPLIES TO: MongoDB vCore

The $type operator selects documents if a field is of the specified type. This is useful for data validation and ensuring consistency across documents in a collection. The $type operator accepts both BSON type numbers and string aliases.

Syntax

The syntax for the $type operator is as follows:

{
  <field>: { $type: <BSON type number> | <string alias> }
}

Parameters

Description
field The field to check the type of.
BSON type number A number corresponding to the BSON type (e.g., 1 for double, 2 for string).
string alias A string alias for the BSON type (e.g., "double", "string", "object", "array").

Common BSON Types

Type Number Alias Description
Double 1 "double" 64-bit floating point
String 2 "string" UTF-8 string
Object 3 "object" Embedded document
Array 4 "array" Array
ObjectId 7 "objectId" ObjectId
Boolean 8 "bool" Boolean
Date 9 "date" Date
Null 10 "null" Null value
32-bit integer 16 "int" 32-bit integer
Timestamp 17 "timestamp" Timestamp
64-bit integer 18 "long" 64-bit integer

Example

Let's understand the usage with sample JSON from the stores dataset.

{
  "_id": "905d1939-e03a-413e-a9c4-221f74055aac",
  "name": "Trey Research | Home Office Depot - Lake Freeda",
  "location": { "lat": -48.9752, "lon": -141.6816 },
  "staff": { "employeeCount": { "fullTime": 12, "partTime": 19 } },
  "sales": {
    "salesByCategory": [ { "categoryName": "Desk Lamps", "totalSales": 37978 } ],
    "revenue": 37978
  },
  "promotionEvents": [
    {
      "eventName": "Crazy Deal Days",
      "promotionalDates": {
        "startDate": { "Year": 2023, "Month": 9, "Day": 27 },
        "endDate": { "Year": 2023, "Month": 10, "Day": 4 }
      },
      "discounts": [
        { "categoryName": "Desks", "discountPercentage": 22 },
        { "categoryName": "Filing Cabinets", "discountPercentage": 23 }
      ]
    }
  ],
  "company": "Trey Research",
  "city": "Lake Freeda",
  "storeOpeningDate": "2024-09-26T22:55:25.779Z",
  "lastUpdated": "Timestamp({ t: 1729983325, i: 1 })"
}

Example 1: Find stores with string-type names

The example finds all stores where the name field is of string type.

db.stores.find(
 { "name": { $type: "string" }},
 { "_id": 1, "name": 1 }).limit(1)

The query returns all documents where the name field contains a string value.

{
  "_id": "a715ab0f-4c6e-4e9d-a812-f2fab11ce0b6",
  "name": "Lakeshore Retail | Holiday Supply Hub - Marvinfort"
}

Example 2: Data validation using multiple type checks

This example demonstrates validating that essential fields have the correct data types.

db.stores.find({
  "_id": "905d1939-e03a-413e-a9c4-221f74055aac",
  "name": { $type: "string" },
  "location": { $type: "object" },
  "staff.employeeCount.fullTime": { $type: ["int", "long"] },
  "promotionEvents": { $type: "array" }},
  { "_id": 1, "name": 1,"location":1, "staff": 1 }
)

The query returns stores where all specified fields have the expected data types, helping ensure data consistency.

{
  "_id": "905d1939-e03a-413e-a9c4-221f74055aac",
  "name": "Trey Research | Home Office Depot - Lake Freeda",
  "location": { "lat": -48.9752, "lon": -141.6816 },
  "staff": { "employeeCount": { "fullTime": 12 } }
}

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