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When you edit a document in JSON view, Compass performs a findOneAndReplace operation and replaces the document.
Modifying documents is not permitted in MongoDB Compass Readonly Edition.
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➤ Use the Select your language drop-down menu in the upper-right to set the language of the following examples.
Note
Starting in MongoDB 4.2, MongoDB can accept an aggregation pipeline to specify the modifications to make instead of an update document. See the method reference page for details.
All write operations in MongoDB are atomic on the level of a single document. For more information on MongoDB and atomicity, see Atomicity and Transactions.
Once set, you cannot update the value of the _id
field nor
can you replace an existing document with a replacement document that has a different _id
field value.
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.
With write concerns, you can specify the level of acknowledgement requested from MongoDB for write operations. For details, see Write Concern.
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db.collection.updateMany[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 all documents that match the specified filter for a collection.
The updateMany[]
method has the following form:
db.collection.updateMany[ , , { upsert: , writeConcern: , collation: , arrayFilters: [ , ... ], hint: // Available starting in MongoDB 4.2.1 } ]
The
updateMany[]
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:
A boolean
acknowledged
astrue
if the operation ran with write concern orfalse
if write concern was disabledmatchedCount
containing the number of matched documentsmodifiedCount
containing the number of modified documentsupsertedId
containing the_id
for the upserted document
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.
updateMany[]
updates all matching documents in the collection that match the filter
, using the update
criteria to apply modifications.
If
upsert: true
and no documents match the filter
, db.collection.updateMany[]
creates a new document based on the filter
and update
parameters.
If you specify upsert: true
on a sharded collection, you must include the full shard key in the filter
. For additional
db.collection.updateMany[]
behavior, see Sharded Collections.
See Update Multiple Documents with Upsert.
For the modification specification, the db.collection.updateMany[]
method can accept a document that
only contains update operator expressions to perform.
For example:
db.collection.updateMany[ , { $set: { status: "D" }, $inc: { quantity: 2 } }, ... ]
Starting in MongoDB 4.2, the db.collection.updateMany[]
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.updateMany[ , [ { $set: { status: "Modified", comments: [ "$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 an update operation changes the document size, the operation will fail.
You cannot use the updateMany[]
method on a time series
collection.
For a db.collection.updateMany[]
operation that includes upsert: true
and is on a sharded collection, you must include the full shard key in the filter
.
updateMany[]
is not compatible with db.collection.explain[]
.
db.collection.updateMany[]
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.updateMany[]
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", "violations" : 3 } { "_id" : 2, "name" : "Rock A Feller Bar and Grill", "violations" : 2 } { "_id" : 3, "name" : "Empire State Sub", "violations" : 5 } { "_id" : 4, "name" : "Pizza Rat's Pizzaria", "violations" : 8 }
The following operation updates all documents where violations
are greater than 4
and $set
a flag for review:
try { db.restaurant.updateMany[ { violations: { $gt: 4 } }, { $set: { "Review" : true } } ]; } catch [e] { print[e]; }
The operation returns:
{ "acknowledged" : true, "matchedCount" : 2, "modifiedCount" : 2 }
The collection now contains the following documents:
{ "_id" : 1, "name" : "Central Perk Cafe", "violations" : 3 } { "_id" : 2, "name" : "Rock A Feller Bar and Grill", "violations" : 2 } { "_id" : 3, "name" : "Empire State Sub", "violations" : 5, "Review" : true } { "_id" : 4, "name" : "Pizza Rat's Pizzaria", "violations" : 8, "Review" : true }
If no matches were found, the operation instead returns:
{ "acknowledged" : true, "matchedCount" : 0, "modifiedCount" : 0 }
Setting upsert: true
would insert a document if no match was found.
Starting in MongoDB 4.2, the db.collection.updateMany[]
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, "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 thelastUpdate
field.remove the
misc1
andmisc2
fields for all documents in the collection.
db.members.updateMany[ { }, [ { $set: { status: "Modified", comments: [ "$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["2020-01-23T05:50:49.247Z"], "comments" : [ "note to self: confirm status", "Need to activate" ] } { "_id" : 2, "member" : "xyz123", "status" : "Modified", "points" : 60, "lastUpdate" : ISODate["2020-01-23T05:50:49.247Z"], "comments" : [ "reminder: ping me at 100pts", "Some random comment" ] }
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 ], "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.updateMany[ { }, [ { $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 ], "lastUpdate" : ISODate["2020-01-24T17:31:01.670Z"], "average" : 92, "grade" : "A" } { "_id" : 2, "tests" : [ 94, 88, 90 ], "lastUpdate" : ISODate["2020-01-24T17:31:01.670Z"], "average" : 90, "grade" : "A" } { "_id" : 3, "tests" : [ 70, 75, 82 ], "lastUpdate" : ISODate["2020-01-24T17:31:01.670Z"], "average" : 75, "grade" : "C" }
Tip
See also:
The inspectors
collection contains the following documents:
{ "_id" : 92412, "inspector" : "F. Drebin", "Sector" : 1, "Patrolling" : true }, { "_id" : 92413, "inspector" : "J. Clouseau", "Sector" : 2, "Patrolling" : false }, { "_id" : 92414, "inspector" : "J. Clouseau", "Sector" : 3, "Patrolling" : true }, { "_id" : 92415, "inspector" : "R. Coltrane", "Sector" : 3, "Patrolling" : false }
The following operation updates all documents with Sector
greater than 4 and inspector
equal to "R. Coltrane"
:
try { db.inspectors.updateMany[ { "Sector" : { $gt : 4 }, "inspector" : "R. Coltrane" }, { $set: { "Patrolling" : false } }, { upsert: true } ]; } catch [e] { print[e]; }
The operation returns:
{ "acknowledged" : true, "matchedCount" : 0, "modifiedCount" : 0, "upsertedId" : ObjectId["56fc5dcb39ee682bdc609b02"] }
The collection now contains the following documents:
{ "_id" : 92412, "inspector" : "F. Drebin", "Sector" : 1, "Patrolling" : true }, { "_id" : 92413, "inspector" : "J. Clouseau", "Sector" : 2, "Patrolling" : false }, { "_id" : 92414, "inspector" : "J. Clouseau", "Sector" : 3, "Patrolling" : true }, { "_id" : 92415, "inspector" : "R. Coltrane", "Sector" : 3, "Patrolling" : false }, { "_id" : ObjectId["56fc5dcb39ee682bdc609b02"], "inspector" : "R. Coltrane", "Patrolling" : false }
Since no documents matched the filter, and upsert
was true
, updateMany[]
inserted the document with a generated _id
, the equality conditions from the filter
, and the update
modifiers.
Given a three member replica set, the following operation specifies a w
of majority
and wtimeout
of 100
:
try { db.restaurant.updateMany[ { "name" : "Pizza Rat's Pizzaria" }, { $inc: { "violations" : 3}, $set: { "Closed" : true } }, { w: "majority", wtimeout: 100 } ]; } catch [e] { print[e]; }
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.updateMany[ { 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 update all elements that are greater than or equal to 100
in the grades
array, use the filtered
positional operator $[]
with the arrayFilters
option:
db.students.updateMany[ { grades: { $gte: 100 } }, { $set: { "grades.$[element]" : 100 } }, { 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 ] }
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
:
db.students2.updateMany[ { }, { $set: { "grades.$[elem].mean" : 100 } }, { 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 4.2.1.
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.updateMany[ { "points": { $lte: 20 }, "status": "P" }, { $set: { "misc1": "Need to activate" } }, { hint: { status: 1 } } ]
The update command returns the following:
{ "acknowledged" : true, "matchedCount" : 3, "modifiedCount" : 3 }
To view the indexes used, you can use the
$indexStats
pipeline:
db.members.aggregate[ [ { $indexStats: { } }, { $sort: { name: 1 } } ] ]