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db.collection.update()

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db.collection.update(query, 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.

Modifies an existing document or documents in a collection. The method can modify specific fields of an existing document or documents or replace an existing document entirely, depending on the update parameter.

By default, the db.collection.update() method updates a single document. Include the option multi: true to update all documents that match the query criteria.

Changed in version 5.0.

The db.collection.update() method has the following form:

db.collection.update(
<query>,
<update>,
{
upsert: <boolean>,
multi: <boolean>,
writeConcern: <document>,
collation: <document>,
arrayFilters: [ <filterdocument1>, ... ],
hint: <document|string>, // Added in MongoDB 4.2
let: <document> // Added in MongoDB 5.0
}
)

The db.collection.update() 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.

When you execute an update() with upsert: true and the query matches no existing document, MongoDB will refuse to insert a new document if the query specifies conditions on the _id field using dot notation.

document or pipeline

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

Contains only <field1>: <value1> pairs.

Aggregation pipeline (Starting in MongoDB 4.2)

Contains only the following aggregation stages:

For details and examples, see Examples.

boolean

Optional. When true, update() either:

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

If both upsert and multi are true and no documents match the query, the update operation inserts only a single document.

To avoid multiple upserts, ensure that the query field(s) are uniquely indexed. See Upsert with Unique Index for an example.

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

boolean

Optional. If set to true, updates multiple documents that meet the query criteria. If set to false, updates one document. The default value is false. For additional information, see Update Multiple Documents Examples.

document

Optional. A document expressing the write concern. Omit to use the default write concern w: 1.

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.

For an example using writeConcern, see Override Default Write Concern.

document

Optional.

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

For an example using collation, see Specify Collation.

New in version 3.4.

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>] to define an identifier to update only those array elements that match the corresponding filter document in the arrayFilters.

Note

You cannot have an array filter document for an identifier if the identifier is not included in the update document.

For examples, see Specify arrayFilters for 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.

document

Optional.

Specifies a document with a list of variables. This allows you to improve command readability by separating the variables from the query text.

The document syntax is:

{ <variable_name_1>: <expression_1>,
...,
<variable_name_n>: <expression_n> }

The variable is set to the value returned by the expression, and cannot be changed afterwards.

To access the value of a variable in the command, use the double dollar sign prefix ($$) together with your variable name in the form $$<variable_name>. For example: $$targetTotal.

Note

To use a variable to filter results, you must access the variable within the $expr operator.

For a complete MQL example using let and variables, see Use Variables in let.

New in version 5.0.

The method returns a WriteResult document that contains the status of the operation.

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.

Attempting to use the $expr operator with the upsert flag set to true will generate an error.

To use db.collection.update() with multi: false on a sharded collection, you must include an exact match on the _id field or target a single shard (such as by including the shard key).

When the db.collection.update() performs update operations (and not document replacement operations), db.collection.update() can target multiple shards.

Tip
See also:

Starting in MongoDB 4.2, replace document operations attempt to target a single shard, first by using the query filter. If the operation cannot target a single shard by the query filter, it then attempts to target by the replacement document.

In earlier versions, the operation attempts to target using the replacement document.

For a db.collection.update() operation that includes upsert: true and is on a sharded collection, you must include the full shard key in the filter:

  • For an update operation.
  • For a replace document operation (starting in MongoDB 4.2).

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.

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

  • 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 specify multi: false.
  • You must include an equality query filter on the full shard key.
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 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.update() 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
  • Can specify multi: true.
  • 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.
  • Must specify multi: false.
  • Requires equality filter on the full shard key if either:

    • upsert: true, or
    • if using a replacement document and the new shard key value belongs to a different shard.
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:

db.collection.update() 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.update() 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 following tabs showcase a variety of common update() operations.

In mongosh, create a books collection which contains the following documents. This command first removes all previously existing documents from the books collection:

db.books.remove({});
db.books.insertMany([
{
"_id" : 1,
"item" : "TBD",
"stock" : 0,
"info" : { "publisher" : "1111", "pages" : 430 },
"tags" : [ "technology", "computer" ],
"ratings" : [ { "by" : "ijk", "rating" : 4 }, { "by" : "lmn", "rating" : 5 } ],
"reorder" : false
},
{
"_id" : 2,
"item" : "XYZ123",
"stock" : 15,
"info" : { "publisher" : "5555", "pages" : 150 },
"tags" : [ ],
"ratings" : [ { "by" : "xyz", "rating" : 5 } ],
"reorder" : false
}
]);

When you specify the option upsert: true:

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

The following tabs showcase a variety of uses of the upsert modifier with update().

When using the upsert: true option with the update() method, and not using a unique index on the query field(s), multiple instances of a update() operation with similar query field(s) could result in duplicate documents being inserted in certain circumstances.

Consider an example where no document with the name Andy exists and multiple clients issue the following command at roughly the same time:

db.people.update(
{ name: "Andy" },
{ $inc: { score: 1 } },
{
upsert: true,
multi: true
}
)

If all update() operations finish the query phase before any client successfully inserts data, and there is no unique index on the name field, each update() operation may result in an insert, creating multiple documents with name: Andy.

To ensure that only one such document is created, and the other update() operations update this new document instead, create a unique index on the name field. This guarantees that only one document with name: Andy is permitted in the collection.

With this unique index in place, the multiple update() operations now exhibit the following behavior:

  • Exactly one update() operation will successfully insert a new document.
  • All other update() operations will update the newly-inserted document, incrementing the score value.
Tip
See also:

Starting in MongoDB 4.2, the db.collection.update() 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).

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, "misc1" : "reminder: ping me at 100pts", "misc2" : "Some random comment", "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
])

Assume that instead of separate misc1 and misc2 fields, you want to gather these into a new comments field. 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.update(
{ },
[
{ $set: { status: "Modified", comments: [ "$misc1", "$misc2" ], lastUpdate: "$$NOW" } },
{ $unset: [ "misc1", "misc2" ] }
],
{ multi: true }
)
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("2020-01-23T05:11:45.784Z"), "comments" : [ "note to self: confirm status", "Need to activate" ] }
{ "_id" : 2, "member" : "xyz123", "status" : "Modified", "points" : 60, "lastUpdate" : ISODate("2020-01-23T05:11:45.784Z"), "comments" : [ "reminder: ping me at 100pts", "Some random comment" ] }

Create a students3 collection with the following documents:

db.students3.insert([
{ "_id" : 1, "tests" : [ 95, 92, 90 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
]);

Using an aggregation pipeline, you can update the documents with the calculated grade average and letter grade.

db.students3.update(
{ },
[
{ $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"
} } } }
],
{ multi: true }
)
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 ], "lastUpdate" : ISODate("2020-01-24T17:29:35.340Z"), "average" : 92, "grade" : "A" }
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "lastUpdate" : ISODate("2020-01-24T17:29:35.340Z"), "average" : 90, "grade" : "A" }
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate("2020-01-24T17:29:35.340Z"), "average" : 75, "grade" : "C" }

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 } }
]

arrayFilters is not available for updates that use an aggregation pipeline.

To update all array elements which match a specified criteria, use the arrayFilters parameter.

In mongosh, create a students collection with the following documents:

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

To update all elements that are greater than or equal to 100 in the grades array, use the filtered positional operator $[<identifier>] with the arrayFilters option:

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

After the operation, the collection contains the following documents:

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

You can also use the arrayFilters parameter to update specific document fields within an array of documents.

In mongosh, create a students2 collection with the following documents:

db.students2.insertMany([
{
"_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:

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

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" : 100, "std" : 6 },
{ "grade" : 87, "mean" : 100, "std" : 3 },
{ "grade" : 85, "mean" : 100, "std" : 4 }
]
}

New in version 4.2.

In mongosh, create a 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.update(
{ points: { $lte: 20 }, status: "P" }, // Query parameter
{ $set: { misc1: "Need to activate" } }, // Update document
{ multi: true, hint: { status: 1 } } // Options
)

The update command returns the following:

WriteResult({ "nMatched" : 3, "nUpserted" : 0, "nModified" : 3 })

To see the index used, run explain on the operation:

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

The db.collection.explain().update() does not modify the documents.

New in version 5.0.

To define variables that you can access elsewhere in the command, use the let option.

Note

To filter results using a variable, you must access the variable within the $expr operator.

Create a collection cakeFlavors:

db.cakeFlavors.insert( [
{ _id: 1, flavor: "chocolate" },
{ _id: 2, flavor: "strawberry" },
{ _id: 3, flavor: "cherry" }
] )

The following example defines targetFlavor and newFlavor variables in let and uses the variables to change the cake flavor from cherry to orange:

db.cakeFlavors.update(
{ $expr: { $eq: [ "$flavor", "$$targetFlavor" ] } },
[ { $set: { flavor: "$$newFlavor" } } ],
{ let : { targetFlavor: "cherry", newFlavor: "orange" } }
)

The following operation to a replica set specifies a write concern of w: 2 with a wtimeout of 5000 milliseconds. This operation either returns after the write propagates to both the primary and one secondary, or times out after 5 seconds.

db.books.update(
{ stock: { $lte: 10 } },
{ $set: { reorder: true } },
{
multi: true,
writeConcern: { w: 2, wtimeout: 5000 }
}
)

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.

In mongosh, create a collection named myColl with the following documents:

db.myColl.insertMany(
[
{ _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 and sets multi to true to update all matching documents:

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

The write result of the operation returns the following document, indicating that all three documents in the collection were updated:

WriteResult({ "nMatched" : 3, "nUpserted" : 0, "nModified" : 3 })

After the operation, the collection contains the following documents:

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

The db.collection.update() method returns a WriteResult() object that contains the status of the operation. Upon success, the WriteResult() object contains the number of documents that matched the query condition, the number of documents inserted by the update, and the number of documents modified:

WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })

If the db.collection.update() method encounters write concern errors, the results include the WriteResult.writeConcernError field:

Changed in version 4.4.

WriteResult({
"nMatched" : 1,
"nUpserted" : 0,
"nModified" : 1,
"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 WriteResult.writeConcernError.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.

If the db.collection.update() method encounters a non-write concern error, the results include the WriteResult.writeError field:

WriteResult({
"nMatched" : 0,
"nUpserted" : 0,
"nModified" : 0,
"writeError" : {
"code" : 7,
"errmsg" : "could not contact primary for replica set shard-a"
}
})
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