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db.collection.updateMany()

Definition

db.collection.updateMany(filter, update, options)

New in version 3.2.

Updates all documents that match the specified filter for a collection.

Syntax

The updateMany() method has the following form:

db.collection.updateMany(
   <filter>,
   <update>,
   {
     upsert: <boolean>,
     writeConcern: <document>,
     collation: <document>,
     arrayFilters: [ <filterdocument1>, ... ]
   }
)

Parameters

The updateMany() method takes the following parameters:

Parameter Type Description
filter document

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

Specify an empty document { } to update all documents in the collection.

update document

The modifications to apply.

The value can be either:

For more information on the update modification parameter, see Update with an Update Operator Expressions Document and Update with an Aggregation Pipeline.

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

upsert boolean

Optional. When true, updateMany() either:

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

To avoid multiple upserts, ensure that the filter fields are uniquely indexed.

Defaults to false.

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:

[
  { "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:

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

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

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

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

New in version 3.6.

Returns

The method returns a document that contains:

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

Access Control

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.

Behavior

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, updateMany() creates a new document based on the filter and update parameters. See Update Multiple Documents with Upsert.

Update with an Update Operator Expressions Document

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

Update with an Aggregation Pipeline

Starting in MongoDB 4.2, the db.collection.updateMany() 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.updateMany(
   <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.

Capped Collections

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

Explainability

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

Use update() instead.

Transactions

db.collection.updateMany() supports multi-document transactions.

If the operation results in an upsert, the collection must already exist.

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.

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.

Examples

Update Multiple Documents

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.

Update with Aggregation Pipeline

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:

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).

Example 1

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" },
   { "_id" : 2, "member" : "xyz123", "status" : "A", "points" : 60, "misc1" : "reminder: ping me at 100pts", "misc2" : "Some random comment" }
])

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 remove the misc1 and misc2 fields for all documents in the collection.

db.members.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.

First Stage
The $set stage creates a new array field comments whose elements are the current content of the misc1 and misc2 fields.
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, "comments" : [ "note to self: confirm status", "Need to activate" ] }
{ "_id" : 2, "member" : "xyz123", "status" : "Modified", "points" : 60, "comments" : [ "reminder: ping me at 100pts", "Some random comment" ] }

Example 2

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 ] },
   { "_id" : 2, "tests" : [ 94, 88, 90 ] },
   { "_id" : 3, "tests" : [ 70, 75, 82 ] }
]);

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

db.students3.updateMany(
   { },
   [
     { $set: { average : { $avg: "$tests" } } },
     { $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.
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" : 92.33333333333333, "grade" : "A" }
{ "_id" : 2, "tests" : [ 94, 88, 90 ], "average" : 90.66666666666667, "grade" : "A" }
{ "_id" : 3, "tests" : [ 70, 75, 82 ], "average" : 75.66666666666667, "grade" : "C" }

Update Multiple Documents with Upsert

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.

Update with Write Concern

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:

WriteConcernError({
   "code" : 64,
   "errInfo" : {
      "wtimeout" : true
   },
   "errmsg" : "waiting for replication timed out"
}) :
undefined

The wtimeout error only indicates that the operation did not complete on time. The write operation itself can still succeed outside of the set time limit.

Specify Collation

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

Specify arrayFilters for an Array Update Operations

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.

Update Elements Match arrayFilters Criteria

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 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.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 ] }

Update Specific Elements of an Array of Documents

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:

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 }
   ]
}