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Introduction

Documents

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  • Compatibility
  • Document Structure
  • Dot Notation
  • Document Limitations
  • Other Uses of the Document Structure
  • Further Reading

MongoDB stores data records as BSON documents. BSON is a binary representation of JSON documents, though it contains more data types than JSON. For the BSON spec, see bsonspec.org. See also BSON Types.

A MongoDB document.

Compatibility

MongoDB stores records as documents for deployments hosted in the following environments:

  • MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

Document Structure

MongoDB documents are composed of field-and-value pairs and have the following structure:

{
   field1: value1,
   field2: value2,
   field3: value3,
   ...
   fieldN: valueN
}

The value of a field can be any of the BSON data types, including other documents, arrays, and arrays of documents. For example, the following document contains values of varying types:

var mydoc = {
               _id: ObjectId("5099803df3f4948bd2f98391"),
               name: { first: "Alan", last: "Turing" },
               birth: new Date('Jun 23, 1912'),
               death: new Date('Jun 07, 1954'),
               contribs: [ "Turing machine", "Turing test", "Turingery" ],
               views : NumberLong(1250000)
            }

The above fields have the following data types:

  • _id holds an ObjectId.

  • name holds an embedded document that contains the fields first and last.

  • birth and death hold values of the Date type.

  • contribs holds an array of strings.

  • views holds a value of the NumberLong type.

Field Names

Field names are strings.

Documents have the following restrictions on field names:

  • The field name _id is reserved for use as a primary key; its value must be unique in the collection, is immutable, and may be of any type other than an array or regex. If the _id contains subfields, the subfield names cannot begin with a ($) symbol.

  • Field names cannot contain the null character.

  • The server permits storage of field names that contain dots (.) and dollar signs ($).

  • MongodB 5.0 adds improved support for the use of ($) and (.) in field names. There are some restrictions. See Field Name Considerations for more details.

The MongoDB Query Language doesn't support documents with duplicate field names:

  • Although some BSON builders may support creating a BSON document with duplicate field names, inserting these documents into MongoDB isn't supported even if the insert succeeds, or appears to succeed.

  • For example, inserting a BSON document with duplicate field names through a MongoDB driver may result in the driver silently dropping the duplicate values prior to insertion, or may result in an invalid document being inserted that contains duplicate fields. Querying those documents leads to inconsistent results.

  • Updating documents with duplicate field names isn't supported, even if the update succeeds or appears to succeed.

Starting in MongoDB 6.1, to see if a document has duplicate field names, use the validate command with the full field set to true. In any MongoDB version, use the $objectToArray aggregation operator to see if a document has duplicate field names.

Dot Notation

MongoDB uses the dot notation to access the elements of an array and to access the fields of an embedded document.

Arrays

To specify or access an element of an array by the zero-based index position, concatenate the array name with the dot (.) and zero-based index position, and enclose in quotes:

"<array>.<index>"

For example, given the following field in a document:

{
   ...
   contribs: [ "Turing machine", "Turing test", "Turingery" ],
   ...
}

To specify the third element in the contribs array, use the dot notation "contribs.2".

For examples querying arrays, see:

Tip

See also:

  • $[] all positional operator for update operations,

  • $[<identifier>] filtered positional operator for update operations,

  • $ positional operator for update operations,

  • $ projection operator when array index position is unknown

  • Query an Array for dot notation examples with arrays.

Embedded Documents

To specify or access a field of an embedded document with dot notation, concatenate the embedded document name with the dot (.) and the field name, and enclose in quotes:

"<embedded document>.<field>"

For example, given the following field in a document:

{
   ...
   name: { first: "Alan", last: "Turing" },
   contact: { phone: { type: "cell", number: "111-222-3333" } },
   ...
}

  • To specify the field named last in the name field, use the dot notation "name.last".

  • To specify the number in the phone document in the contact field, use the dot notation "contact.phone.number".

Warning

Partition fields cannot use field names that contain a dot (.).

For examples querying embedded documents, see:

Document Limitations

Documents have the following attributes:

Document Size Limit

The maximum BSON document size is 16 megabytes.

The maximum document size helps ensure that a single document cannot use excessive amount of RAM or, during transmission, excessive amount of bandwidth. To store documents larger than the maximum size, MongoDB provides the GridFS API. See mongofiles and the documentation for your driver for more information about GridFS.

Document Field Order

Unlike JavaScript objects, the fields in a BSON document are ordered.

Field Order in Queries

For queries, the field order behavior is as follows:

  • When comparing documents, field ordering is significant. For example, when comparing documents with fields a and b in a query:

    • {a: 1, b: 1} is equal to {a: 1, b: 1}

    • {a: 1, b: 1} is not equal to {b: 1, a: 1}

  • For efficient query execution, the query engine may reorder fields during query processing. Among other cases, reordering fields may occur when processing these projection operators: $project, $addFields, $set, and $unset.

    • Field reordering may occur in intermediate results as well as the final results returned by a query.

    • Because some operations may reorder fields, you should not rely on specific field ordering in the results returned by a query that uses the projection operators listed earlier.

Field Order in Write Operations

For write operations, MongoDB preserves the order of the document fields except for the following cases:

  • The _id field is always the first field in the document.

  • Updates that include renaming of field names may result in the reordering of fields in the document.

The _id Field

In MongoDB, each document stored in a standard collection requires a unique _id field that acts as a primary key. If an inserted document omits the _id field, the MongoDB driver automatically generates an ObjectId for the _id field.

This also applies to documents inserted through update operations with upsert: true.

Note

In time series collections, documents do not require a unique _id field because MongoDB does not create an index on the _id field.

The _id field has the following behavior and constraints:

  • By default, MongoDB creates a unique index on the _id field during the creation of a collection.

  • The _id field is always the first field in the documents. If the server receives a document that does not have the _id field first, then the server will move the field to the beginning.

  • If the _id contains subfields, the subfield names cannot begin with a ($) symbol.

  • The _id field may contain values of any BSON data type, other than an array, regex, or undefined.

    Warning

    To ensure functioning replication, do not store values that are of the BSON regular expression type in the _id field.

The following are common options for storing values for _id:

  • Use an ObjectId.

  • Use a natural unique identifier, if available. This saves space and avoids an additional index.

  • Generate an auto-incrementing number.

  • Generate a UUID in your application code. For a more efficient storage of the UUID values in the collection and in the _id index, store the UUID as a value of the BSON BinData type.

    Index keys that are of the BinData type are more efficiently stored in the index if:

    • the binary subtype value is in the range of 0-7 or 128-135, and

    • the length of the byte array is: 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, or 32.

  • Use your driver's BSON UUID facility to generate UUIDs. Be aware that driver implementations may implement UUID serialization and deserialization logic differently, which may not be fully compatible with other drivers. See your driver documentation for information concerning UUID interoperability.

Note

Most MongoDB driver clients include the _id field and generate an ObjectId before sending the insert operation to MongoDB. However, if the client sends a document without an _id field, the mongod adds the _id field and generates the ObjectId.

Other Uses of the Document Structure

In addition to defining data records, MongoDB uses the document structure throughout, including but not limited to: query filters, update specifications documents, and index specification documents

Query Filter Documents

Query filter documents specify the conditions that determine which records to select for read, update, and delete operations.

You can use <field>:<value> expressions to specify the equality condition and query operator expressions.

{
  <field1>: <value1>,
  <field2>: { <operator>: <value> },
  ...
}

For examples, see:

Update Specification Documents

Update specification documents use update operators to specify the data modifications to perform on specific fields during an update operation.

{
  <operator1>: { <field1>: <value1>, ... },
  <operator2>: { <field2>: <value2>, ... },
  ...
}

For examples, see Update specifications.

Index Specification Documents

Index specification documents define the field to index and the index type:

{ <field1>: <type1>, <field2>: <type2>, ...  }

Further Reading

For more information on the MongoDB document model, download the MongoDB Application Modernization Guide.

The download includes the following resources:

  • Presentation on the methodology of data modeling with MongoDB

  • White paper covering best practices and considerations for migrating to MongoDB from an RDBMS data model

  • Reference MongoDB schema with its RDBMS equivalent

  • Application Modernization scorecard

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