db.collection.updateOne()

upsert on a Sharded Collection

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, 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

Shard Key Modification

You can update a document's shard key value unless the shard key field is the immutable _id field.

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.

Missing Shard Key

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:

See also:

• upsert on a Sharded Collection

• Missing Shard Key Fields

Upsert within Transactions

You can create collections and indexes inside a distributed transaction if the transaction is not a cross-shard write transaction.

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.

Write Concerns and Transactions

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.

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 students collection with the following documents:

db.students.insertMany( [
   { "_id" : 1, "student" : "Skye", "points" : 75, "commentsSemester1" : "great at math", "commentsSemester2" : "loses temper", "lastUpdate" : ISODate("2019-01-01T00:00:00Z") },
   { "_id" : 2, "student" : "Elizabeth", "points" : 60, "commentsSemester1" : "well behaved", "commentsSemester2" : "needs improvement", "lastUpdate" : ISODate("2019-01-01T00:00:00Z") }
] )

Assume that instead of separate commentsSemester1 and commentsSemester2 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 commentsSemester1 and commentsSemester2 fields for all documents in the collection.

Make sure that the filter in the update command targets a unique document. The field id in the code below is an example of such a filter:

db.students.updateOne(
   { _id: 1 },
   [
      { $set: { status: "Modified", comments: [ "$commentsSemester1", "$commentsSemester2" ], lastUpdate: "$$NOW" } },
      { $unset: [ "commentsSemester1", "commentsSemester2" ] }
   ]
)

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 commentsSemester1 and commentsSemester2 fields.

After the command, the collection contains the following documents:

{ "_id" : 2, "student" : "Elizabeth", "status" : "Modified", "points" : 60, "lastUpdate" : ISODate("2020-01-23T05:11:45.784Z"), "comments" : [ "well behaved", "needs improvement" ] }
{ _id: 1, student: 'Skye', points: 75, commentsSemester1: 'great at math', commentsSemester2: 'loses temper', lastUpdate: ISODate("2019-01-01T00:00:00.000Z") }

Note that after introducing a sort, only the first document encountered in the sort order is modified and the remaining documents are left untouched.

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.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"
     } } } }
   ]
)

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" }

Update Elements Match arrayFilters Criteria

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 $[<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 ] }

Update Specific Elements of an Array of Documents

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 $[<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 }
   ]
}