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db.collection.updateOne[filter, update, options]
Important
mongosh Method
This page documents a mongosh
method. This is not the documentation for a language-specific driver, such as Node.js.
For MongoDB API drivers, refer to the language-specific MongoDB driver documentation.
Updates a single document within the collection based on the filter.
The updateOne[]
method has the following syntax:
db.collection.updateOne[ , , { upsert: , writeConcern: , collation: , arrayFilters: [ , ... ], hint: // Available starting in MongoDB 4.2.1 } ]
The db.collection.updateOne[]
method takes the following parameters:
filter | document | The selection criteria for the update. The same query selectors as in the Specify an empty document | ||||||||||
update | document or pipeline | The modifications to apply. Can be one of the following:
To update with a replacement document, see | ||||||||||
| boolean | Optional. When
To avoid multiple upserts, ensure that the Defaults to | ||||||||||
| 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. | ||||||||||
| 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:
When
specifying collation, the If the collation is unspecified but the collection has a default collation [see
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. | ||||||||||
| 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 NoteThe |
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.
hint
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 documentsmodifiedCount
containing the number of modified documentsupsertedId
containing the_id
for the upserted document.A boolean
acknowledged
astrue
if the operation ran with write concern orfalse
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[ , { $set: { status: "D" }, $inc: { quantity: 2 } }, ... ]
Starting in MongoDB 4.2, the db.collection.updateOne[]
method can accept an
aggregation pipeline [ , , ... ]
that specifies the modifications to perform. The pipeline can consist of the following stages:
$addFields
and its alias$set
$project
and its alias$unset
$replaceRoot
and its alias$replaceWith
.
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[ , [ { $set: { status: "Modified", comments: [ "$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: , : 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:
To set to |
|
To set to a non- |
|
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 CollectionMissing Shard Key Fields
updateOne[]
is not compatible with
db.collection.explain[]
.
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.
Tip
See also:
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:
$addFields
and its alias$set
$project
and its alias$unset
$replaceRoot
and its alias$replaceWith
.
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 thelastUpdate
field.remove the
misc1
andmisc2
fields for all documents in the collection.
db.members.updateOne[ { _id: 1 }, [ { $set: { status: "Modified", comments: [ "$misc1", "$misc2" ], lastUpdate: "$$NOW" } }, { $unset: [ "misc1", "misc2" ] } ] ]
Note
The $set
and $unset
used in the pipeline refers to the aggregation stages $set
and
$unset
respectively, and not the update operators $set
and $unset
.
The
$set
stage:
creates a new array field
comments
whose elements are the current content of themisc1
andmisc2
fields andsets the field
lastUpdate
to the value of the aggregation variableNOW
. The aggregation variableNOW
resolves to the current datetime value and remains the same throughout the pipeline. To access aggregation variables, prefix the variable with double dollar signs$$
and enclose in quotes.
$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.insertMany[ [ { "_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: "$$NOW" } }, { $set: { grade: { $switch: { branches: [ { case: { $gte: [ "$average", 90 ] }, then: "A" }, { case: { $gte: [ "$average", 80 ] }, then: "B" }, { case: { $gte: [ "$average", 70 ] }, then: "C" }, { case: { $gte: [ "$average", 60 ] }, then: "D" } ], default: "F" } } } } ] ]
Note
The $set
used in the pipeline refers to the aggregation stage
$set
, and not the update operators $set
.
The $set
stage:
calculates a new field
average
based on the average of thetests
field. See$avg
for more information on the$avg
aggregation operator and$trunc
for more information on the$trunc
truncate aggregation operator.sets the field
lastUpdate
to the value of the aggregation variableNOW
. The aggregation variableNOW
resolves to the current datetime value and remains the same throughout the pipeline. To access aggregation variables, prefix the variable with double dollar signs$$
and enclose in quotes.
$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" }
Tip
See also:
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
:
| The write concern was specified in the application. |
| The write concern originated from a custom defined default value. See |
| The write concern originated from the replica set's |
| The write concern originated from the server in absence of all other write concern specifications. |
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 } } ];
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.insertMany[ [ { "_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 $[]
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.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 $[]
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 } } ] ]