Docs Menu

db.collection.updateOne()

On this page

  • Definition
  • Syntax
  • Access Control
  • Behavior
  • Examples
db.collection.updateOne(filter, update, options)
Important
mongosh Method

This is a mongosh method. This is not the documentation for Node.js or other programming language specific driver methods.

In most cases, mongosh methods work the same way as the legacy mongo shell methods. However, some legacy methods are unavailable in mongosh.

For the legacy mongo shell documentation, refer to the documentation for the corresponding MongoDB Server release:

For MongoDB API drivers, refer to the language specific MongoDB driver documentation.

New in version 3.2.

Updates a single document within the collection based on the filter.

The updateOne() method has the following syntax:

db.collection.updateOne(
<filter>,
<update>,
{
upsert: <boolean>,
writeConcern: <document>,
collation: <document>,
arrayFilters: [ <filterdocument1>, ... ],
hint: <document|string> // Available starting in MongoDB 4.2.1
}
)

The db.collection.updateOne() method takes the following parameters:

Parameter
Type
Description
document

The selection criteria for the update. The same query selectors as in the find() method are available.

Specify an empty document { } to update the first document returned in the collection.

document or pipeline

The modifications to apply. Can be one of the following:

Aggregation pipeline (Starting in MongoDB 4.2)

Contains only the following aggregation stages:

For more information, see Update with an Aggregation Pipeline.

To update with a replacement document, see db.collection.replaceOne().

upsert
boolean

Optional. When true, updateOne() either:

  • Creates a new document if no documents match the filter. For more details see upsert behavior.
  • Updates a single document that matches the filter.

To avoid multiple upserts, ensure that the filter field(s) are uniquely indexed.

Defaults to false, which does not insert a new document when no match is found.

writeConcern
document

Optional. A document expressing the write concern. Omit to use the default write concern.

Do not explicitly set the write concern for the operation if run in a transaction. To use write concern with transactions, see Transactions and Write Concern.

collation
document

Optional.

Specifies the collation to use for the operation.

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

The collation option has the following syntax:

collation: {
locale: <string>,
caseLevel: <boolean>,
caseFirst: <string>,
strength: <int>,
numericOrdering: <boolean>,
alternate: <string>,
maxVariable: <string>,
backwards: <boolean>
}

When specifying collation, the locale field is mandatory; all other collation fields are optional. For descriptions of the fields, see Collation Document.

If the collation is unspecified but the collection has a default collation (see db.createCollection()), the operation uses the collation specified for the collection.

If no collation is specified for the collection or for the operations, MongoDB uses the simple binary comparison used in prior versions for string comparisons.

You cannot specify multiple collations for an operation. For example, you cannot specify different collations per field, or if performing a find with a sort, you cannot use one collation for the find and another for the sort.

New in version 3.4.

arrayFilters
array

Optional. An array of filter documents that determine which array elements to modify for an update operation on an array field.

In the update document, use the $[<identifier>] filtered positional operator to define an identifier, which you then reference in the array filter documents. You cannot have an array filter document for an identifier if the identifier is not included in the update document.

Note

The <identifier> must begin with a lowercase letter and contain only alphanumeric characters.

You can include the same identifier multiple times in the update document; however, for each distinct identifier ($[identifier]) in the update document, you must specify exactly one corresponding array filter document. That is, you cannot specify multiple array filter documents for the same identifier. For example, if the update statement includes the identifier x (possibly multiple times), you cannot specify the following for arrayFilters that includes 2 separate filter documents for x:

// INVALID
[
{ "x.a": { $gt: 85 } },
{ "x.b": { $gt: 80 } }
]

However, you can specify compound conditions on the same identifier in a single filter document, such as in the following examples:

// Example 1
[
{ $or: [{"x.a": {$gt: 85}}, {"x.b": {$gt: 80}}] }
]
// Example 2
[
{ $and: [{"x.a": {$gt: 85}}, {"x.b": {$gt: 80}}] }
]
// Example 3
[
{ "x.a": { $gt: 85 }, "x.b": { $gt: 80 } }
]

For examples, see Specify arrayFilters for an Array Update Operations.

New in version 3.6.

Document or string

Optional. A document or string that specifies the index to use to support the query predicate.

The option can take an index specification document or the index name string.

If you specify an index that does not exist, the operation errors.

For an example, see Specify hint for Update Operations.

New in version 4.2.1.

The method returns a document that contains:

  • matchedCount containing the number of matched documents
  • modifiedCount containing the number of modified documents
  • upsertedId containing the _id for the upserted document.
  • A boolean acknowledged as true if the operation ran with write concern or false if write concern was disabled

On deployments running with authorization, the user must have access that includes the following privileges:

  • update action on the specified collection(s).
  • find action on the specified collection(s).
  • insert action on the specified collection(s) if the operation results in an upsert.

The built-in role readWrite provides the required privileges.

db.collection.updateOne() finds the first document that matches the filter and applies the specified update modifications.

For the update specifications, the db.collection.updateOne() method can accept a document that only contains update operator expressions.

For example:

db.collection.updateOne(
<query>,
{ $set: { status: "D" }, $inc: { quantity: 2 } },
...
)

Starting in MongoDB 4.2, the db.collection.updateOne() method can accept an aggregation pipeline [ <stage1>, <stage2>, ... ] that specifies the modifications to perform. The pipeline can consist of the following stages:

Using the aggregation pipeline allows for a more expressive update statement, such as expressing conditional updates based on current field values or updating one field using the value of another field(s).

For example:

db.collection.updateOne(
<query>,
[
{ $set: { status: "Modified", comments: [ "$misc1", "$misc2" ] } },
{ $unset: [ "misc1", "misc2" ] }
]
...
)
Note

The $set and $unset used in the pipeline refers to the aggregation stages $set and $unset respectively, and not the update operators $set and $unset.

For examples, see Update with Aggregation Pipeline.

If upsert: true and no documents match the filter, db.collection.updateOne() creates a new document based on the filter criteria and update modifications. See Update with Upsert.

If you specify upsert: true on a sharded collection, you must include the full shard key in the filter. For additional db.collection.updateOne() behavior on a sharded collection, see Sharded Collections.

If an update operation changes the document size, the operation will fail.

To use db.collection.updateOne() on a sharded collection:

  • If you don't specify upsert: true, you must include an exact match on the _id field or target a single shard (such as by including the shard key in the filter).
  • If you specify upsert: true, the filter must include the shard key.

However, starting in version 4.4, documents in a sharded collection can be missing the shard key fields. To target a document that is missing the shard key, you can use the null equality match in conjunction with another filter condition (such as on the _id field). For example:

{ _id: <value>, <shardkeyfield>: null } // _id of the document missing shard key

Starting in MongoDB 4.2, you can update a document's shard key value unless the shard key field is the immutable _id field. In MongoDB 4.2 and earlier, a document's shard key field value is immutable.

Warning

Starting in version 4.4, documents in sharded collections can be missing the shard key fields. Take precaution to avoid accidentally removing the shard key when changing a document's shard key value.

To modify the existing shard key value with db.collection.updateOne():

  • You must run on a mongos. Do not issue the operation directly on the shard.
  • You must run either in a transaction or as a retryable write.
  • You must include an equality filter on the full shard key.

See also upsert on a Sharded Collection.

Starting in version 4.4, documents in a sharded collection can be missing the shard key fields. To use db.collection.updateOne() to set the document's missing shard key, you must run on a mongos. Do not issue the operation directly on the shard.

In addition, the following requirements also apply:

Task
Requirements
To set to null
  • Requires equality filter on the full shard key if upsert: true.
To set to a non-null value
  • Must be performed either inside a transaction or as a retryable write.
  • Requires equality filter on the full shard key if upsert: true.
Tip

Since a missing key value is returned as part of a null equality match, to avoid updating a null-valued key, include additional query conditions (such as on the _id field) as appropriate.

See also:

updateOne() is not compatible with db.collection.explain().

Use update() instead.

db.collection.updateOne() can be used inside multi-document transactions.

Important

In most cases, multi-document transaction incurs a greater performance cost over single document writes, and the availability of multi-document transactions should not be a replacement for effective schema design. For many scenarios, the denormalized data model (embedded documents and arrays) will continue to be optimal for your data and use cases. That is, for many scenarios, modeling your data appropriately will minimize the need for multi-document transactions.

For additional transactions usage considerations (such as runtime limit and oplog size limit), see also Production Considerations.

Starting in MongoDB 4.4, you can create collections and indexes inside a multi-document transaction if the transaction is not a cross-shard write transaction.

Specifically, in MongoDB 4.4 and greater, db.collection.updateOne() with upsert: true can be run on an existing collection or a non-existing collection. If run on a non-existing collection, the operation creates the collection.

In MongoDB 4.2 and earlier, the operation must be run on an existing collection.

Do not explicitly set the write concern for the operation if run in a transaction. To use write concern with transactions, see Transactions and Write Concern.

The restaurant collection contains the following documents:

{ "_id" : 1, "name" : "Central Perk Cafe", "Borough" : "Manhattan" },
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "Borough" : "Queens", "violations" : 2 },
{ "_id" : 3, "name" : "Empire State Pub", "Borough" : "Brooklyn", "violations" : 0 }

The following operation updates a single document where name: "Central Perk Cafe" with the violations field:

try {
db.restaurant.updateOne(
{ "name" : "Central Perk Cafe" },
{ $set: { "violations" : 3 } }
);
} catch (e) {
print(e);
}

The operation returns:

{ "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }

If no matches were found, the operation instead returns:

{ "acknowledged" : true, "matchedCount" : 0, "modifiedCount" : 0 }

Setting upsert: true would insert the document if no match was found. See Update with Upsert

Starting in MongoDB 4.2, the db.collection.updateOne() can use an aggregation pipeline for the update. The pipeline can consist of the following stages:

Using the aggregation pipeline allows for a more expressive update statement, such as expressing conditional updates based on current field values or updating one field using the value of another field(s).

The following examples uses the aggregation pipeline to modify a field using the values of the other fields in the document.

Create a members collection with the following documents:

db.members.insertMany([
{ "_id" : 1, "member" : "abc123", "status" : "A", "points" : 2, "misc1" : "note to self: confirm status", "misc2" : "Need to activate", "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
{ "_id" : 2, "member" : "xyz123", "status" : "A", "points" : 60, comments: [ "reminder: ping me at 100pts", "Some random comment" ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
])

Assume that instead of separate misc1 and misc2 fields in the first document, you want to gather these into a comments field, like the second document. The following update operation uses an aggregation pipeline to:

  • add the new comments field and set the lastUpdate field.
  • remove the misc1 and misc2 fields for all documents in the collection.
db.members.updateOne(
{ _id: 1 },
[
{ $set: { status: "Modified", comments: [ "$misc1", "$misc2" ], lastUpdate: "$$NOW" } },
{ $unset: [ "misc1", "misc2" ] }
]
)
Note

The $set and $unset used in the pipeline refers to the aggregation stages $set and $unset respectively, and not the update operators $set and $unset.

First Stage

The $set stage:

  • creates a new array field comments whose elements are the current content of the misc1 and misc2 fields and
  • sets the field lastUpdate to the value of the aggregation variable NOW. The aggregation variable NOW resolves to the current datetime value and remains the same throughout the pipeline. To access aggregation variables, prefix the variable with double dollar signs $$ and enclose in quotes.
Second Stage
The $unset stage removes the misc1 and misc2 fields.

After the command, the collection contains the following documents:

{ "_id" : 1, "member" : "abc123", "status" : "Modified", "points" : 2, "lastUpdate" : ISODate("2020-01-23T05:21:59.321Z"), "comments" : [ "note to self: confirm status", "Need to activate" ] }
{ "_id" : 2, "member" : "xyz123", "status" : "A", "points" : 60, "comments" : [ "reminder: ping me at 100pts", "Some random comment" ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }

The aggregation pipeline allows the update to perform conditional updates based on the current field values as well as use current field values to calculate a separate field value.

For example, create a students3 collection with the following documents:

db.students3.insert([
{ "_id" : 1, "tests" : [ 95, 92, 90 ], "average" : 92, "grade" : "A", "lastUpdate" : ISODate("2020-01-23T05:18:40.013Z") },
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "average" : 91, "grade" : "A", "lastUpdate" : ISODate("2020-01-23T05:18:40.013Z") },
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
]);

The third document _id: 3 is missing the average and grade fields. Using an aggregation pipeline, you can update the document with the calculated grade average and letter grade.

db.students3.updateOne(
{ _id: 3 },
[
{ $set: { average: { $trunc: [ { $avg: "$tests" }, 0 ] }, lastUpdate: "$$NOW" } },
{ $set: { grade: { $switch: {
branches: [
{ case: { $gte: [ "$average", 90 ] }, then: "A" },
{ case: { $gte: [ "$average", 80 ] }, then: "B" },
{ case: { $gte: [ "$average", 70 ] }, then: "C" },
{ case: { $gte: [ "$average", 60 ] }, then: "D" }
],
default: "F"
} } } }
]
)
Note

The $set used in the pipeline refers to the aggregation stage $set, and not the update operators $set.

First Stage

The $set stage:

  • calculates a new field average based on the average of the tests field. See $avg for more information on the $avg aggregation operator and $trunc for more information on the $trunc truncate aggregation operator.
  • sets the field lastUpdate to the value of the aggregation variable NOW. The aggregation variable NOW resolves to the current datetime value and remains the same throughout the pipeline. To access aggregation variables, prefix the variable with double dollar signs $$ and enclose in quotes.
Second Stage
The $set stage calculates a new field grade based on the average field calculated in the previous stage. See $switch for more information on the $switch aggregation operator.

After the command, the collection contains the following documents:

{ "_id" : 1, "tests" : [ 95, 92, 90 ], "average" : 92, "grade" : "A", "lastUpdate" : ISODate("2020-01-23T05:18:40.013Z") }
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "average" : 91, "grade" : "A", "lastUpdate" : ISODate("2020-01-23T05:18:40.013Z") }
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate("2020-01-24T17:33:30.674Z"), "average" : 75, "grade" : "C" }

The restaurant collection contains the following documents:

{ "_id" : 1, "name" : "Central Perk Cafe", "Borough" : "Manhattan", "violations" : 3 },
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "Borough" : "Queens", "violations" : 2 },
{ "_id" : 3, "name" : "Empire State Pub", "Borough" : "Brooklyn", "violations" : "0" }

The following operation attempts to update the document with name : "Pizza Rat's Pizzaria", while upsert: true :

try {
db.restaurant.updateOne(
{ "name" : "Pizza Rat's Pizzaria" },
{ $set: {"_id" : 4, "violations" : 7, "borough" : "Manhattan" } },
{ upsert: true }
);
} catch (e) {
print(e);
}

Since upsert:true the document is inserted based on the filter and update criteria. The operation returns:

{
"acknowledged" : true,
"matchedCount" : 0,
"modifiedCount" : 0,
"upsertedId" : 4
}

The collection now contains the following documents:

{ "_id" : 1, "name" : "Central Perk Cafe", "Borough" : "Manhattan", "violations" : 3 },
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "Borough" : "Queens", "violations" : 2 },
{ "_id" : 3, "name" : "Empire State Pub", "Borough" : "Brooklyn", "violations" : 4 },
{ "_id" : 4, "name" : "Pizza Rat's Pizzaria", "Borough" : "Manhattan", "violations" : 7 }

The name field was filled in using the filter criteria, while the update operators were used to create the rest of the document.

The following operation updates the first document with violations that are greater than 10:

try {
db.restaurant.updateOne(
{ "violations" : { $gt: 10} },
{ $set: { "Closed" : true } },
{ upsert: true }
);
} catch (e) {
print(e);
}

The operation returns:

{
"acknowledged" : true,
"matchedCount" : 0,
"modifiedCount" : 0,
"upsertedId" : ObjectId("56310c3c0c5cbb6031cafaea")
}

The collection now contains the following documents:

{ "_id" : 1, "name" : "Central Perk Cafe", "Borough" : "Manhattan", "violations" : 3 },
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "Borough" : "Queens", "violations" : 2 },
{ "_id" : 3, "name" : "Empire State Pub", "Borough" : "Brooklyn", "violations" : 4 },
{ "_id" : 4, "name" : "Pizza Rat's Pizzaria", "Borough" : "Manhattan", "grade" : 7 }
{ "_id" : ObjectId("56310c3c0c5cbb6031cafaea"), "Closed" : true }

Since no documents matched the filter, and upsert was true, updateOne() inserted the document with a generated _id and the update criteria only.

Given a three member replica set, the following operation specifies a w of majority, wtimeout of 100:

try {
db.restaurant.updateOne(
{ "name" : "Pizza Rat's Pizzaria" },
{ $inc: { "violations" : 3}, $set: { "Closed" : true } },
{ w: "majority", wtimeout: 100 }
);
} catch (e) {
print(e);
}

If the primary and at least one secondary acknowledge each write operation within 100 milliseconds, it returns:

{ "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }

If the acknowledgement takes longer than the wtimeout limit, the following exception is thrown:

Changed in version 4.4.

WriteConcernError({
"code" : 64,
"errmsg" : "waiting for replication timed out",
"errInfo" : {
"wtimeout" : true,
"writeConcern" : {
"w" : "majority",
"wtimeout" : 100,
"provenance" : "getLastErrorDefaults"
}
}
})

The following table explains the possible values of errInfo.writeConcern.provenance:

Provenance
Description
clientSupplied
The write concern was specified in the application.
customDefault
The write concern originated from a custom defined default value. See setDefaultRWConcern.
getLastErrorDefaults
The write concern originated from the replica set's settings.getLastErrorDefaults field.
implicitDefault
The write concern originated from the server in absence of all other write concern specifications.

New in version 3.4.

Collation allows users to specify language-specific rules for string comparison, such as rules for lettercase and accent marks.

A collection myColl has the following documents:

{ _id: 1, category: "café", status: "A" }
{ _id: 2, category: "cafe", status: "a" }
{ _id: 3, category: "cafE", status: "a" }

The following operation includes the collation option:

db.myColl.updateOne(
{ category: "cafe" },
{ $set: { status: "Updated" } },
{ collation: { locale: "fr", strength: 1 } }
);

New in version 3.6.

Starting in MongoDB 3.6, when updating an array field, you can specify arrayFilters that determine which array elements to update.

Create a collection students with the following documents:

db.students.insert([
{ "_id" : 1, "grades" : [ 95, 92, 90 ] },
{ "_id" : 2, "grades" : [ 98, 100, 102 ] },
{ "_id" : 3, "grades" : [ 95, 110, 100 ] }
])

To modify all elements that are greater than or equal to 100 in the grades array, use the filtered positional operator $[<identifier>] with the arrayFilters option in the db.collection.updateOne() method:

db.students.updateOne(
{ grades: { $gte: 100 } },
{ $set: { "grades.$[element]" : 100 } },
{ arrayFilters: [ { "element": { $gte: 100 } } ] }
)

The operation updates the grades field of a single document, and after the operation, the collection has the following documents:

{ "_id" : 1, "grades" : [ 95, 92, 90 ] }
{ "_id" : 2, "grades" : [ 98, 100, 100 ] }
{ "_id" : 3, "grades" : [ 95, 110, 100 ] }

Create a collection students2 with the following documents:

db.students2.insert([
{
"_id" : 1,
"grades" : [
{ "grade" : 80, "mean" : 75, "std" : 6 },
{ "grade" : 85, "mean" : 90, "std" : 4 },
{ "grade" : 85, "mean" : 85, "std" : 6 }
]
},
{
"_id" : 2,
"grades" : [
{ "grade" : 90, "mean" : 75, "std" : 6 },
{ "grade" : 87, "mean" : 90, "std" : 3 },
{ "grade" : 85, "mean" : 85, "std" : 4 }
]
}
])

To modify the value of the mean field for all elements in the grades array where the grade is greater than or equal to 85, use the filtered positional operator $[<identifier>] with the arrayFilters in the db.collection.updateOne() method:

db.students2.updateOne(
{ },
{ $set: { "grades.$[elem].mean" : 100 } },
{ arrayFilters: [ { "elem.grade": { $gte: 85 } } ] }
)

The operation updates the array of a single document, and after the operation, the collection has the following documents:

{
"_id" : 1,
"grades" : [
{ "grade" : 80, "mean" : 75, "std" : 6 },
{ "grade" : 85, "mean" : 100, "std" : 4 },
{ "grade" : 85, "mean" : 100, "std" : 6 }
]
}
{
"_id" : 2,
"grades" : [
{ "grade" : 90, "mean" : 75, "std" : 6 },
{ "grade" : 87, "mean" : 90, "std" : 3 },
{ "grade" : 85, "mean" : 85, "std" : 4 }
]
}

New in version 4.2.1.

Create a sample members collection with the following documents:

db.members.insertMany([
{ "_id" : 1, "member" : "abc123", "status" : "P", "points" : 0, "misc1" : null, "misc2" : null },
{ "_id" : 2, "member" : "xyz123", "status" : "A", "points" : 60, "misc1" : "reminder: ping me at 100pts", "misc2" : "Some random comment" },
{ "_id" : 3, "member" : "lmn123", "status" : "P", "points" : 0, "misc1" : null, "misc2" : null },
{ "_id" : 4, "member" : "pqr123", "status" : "D", "points" : 20, "misc1" : "Deactivated", "misc2" : null },
{ "_id" : 5, "member" : "ijk123", "status" : "P", "points" : 0, "misc1" : null, "misc2" : null },
{ "_id" : 6, "member" : "cde123", "status" : "A", "points" : 86, "misc1" : "reminder: ping me at 100pts", "misc2" : "Some random comment" }
])

Create the following indexes on the collection:

db.members.createIndex( { status: 1 } )
db.members.createIndex( { points: 1 } )

The following update operation explicitly hints to use the index { status: 1 }:

Note

If you specify an index that does not exist, the operation errors.

db.members.updateOne(
{ "points": { $lte: 20 }, "status": "P" },
{ $set: { "misc1": "Need to activate" } },
{ hint: { status: 1 } }
)

The update command returns the following:

{ "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }

To view the indexes used, you can use the $indexStats pipeline:

db.members.aggregate( [ { $indexStats: { } }, { $sort: { name: 1 } } ] )
Tip
See also:

To update multiple documents, see db.collection.updateMany().

Give Feedback
MongoDB logo
© 2021 MongoDB, Inc.

About

  • Careers
  • Legal Notices
  • Privacy Notices
  • Security Information
  • Trust Center
© 2021 MongoDB, Inc.