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db.collection.insertMany()

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db.collection.insertMany()

Important

mongosh Method

This page documents a mongosh method. This is not the documentation for database commands or language-specific drivers, such as Node.js.

For the database command, see the insert command.

For MongoDB API drivers, refer to the language-specific MongoDB driver documentation.

For the legacy mongo shell documentation, refer to the documentation for the corresponding MongoDB Server release:

mongo shell v4.4

Inserts multiple documents into a collection.

Returns:

A document containing:

  • An acknowledged boolean, set to true if the operation ran with write concern or false if write concern was disabled

  • An insertedIds array, containing _id values for each successfully inserted document

You can use db.collection.insertMany() for deployments hosted in the following environments:

  • MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud

The insertMany() method has the following syntax:

db.collection.insertMany(
[ <document 1> , <document 2>, ... ],
{
writeConcern: <document>,
ordered: <boolean>
}
)
Parameter
Type
Description
document
document
An array of documents to insert into the collection.
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.

ordered
boolean
Optional. A boolean specifying whether the mongod instance should perform an ordered or unordered insert. Defaults to true.

Given an array of documents, insertMany() inserts each document in the array into the collection.

By default, documents are inserted in the order they are provided.

If ordered is set to true and an insert fails, the server does not continue inserting records.

If ordered is set to false and an insert fails, the server continues inserting records. Documents may be reordered by mongod to increase performance. Applications should not depend on ordering of inserts if using an unordered insertMany().

The number of operations in each group cannot exceed the value of the maxWriteBatchSize of the database. The default value of maxWriteBatchSize is 100,000. This value is shown in the hello.maxWriteBatchSize field.

This limit prevents issues with oversized error messages. If a group exceeds this limit, the client driver divides the group into smaller groups with counts less than or equal to the value of the limit. For example, with the maxWriteBatchSize value of 100,000, if the queue consists of 200,000 operations, the driver creates 2 groups, each with 100,000 operations.

Note

The driver only divides the group into smaller groups when using the high-level API. If using db.runCommand() directly (for example, when writing a driver), MongoDB throws an error when attempting to execute a write batch which exceeds the limit.

If the error report for a single batch grows too large, MongoDB truncates all remaining error messages to the empty string. If there are at least two error messages with total size greater than 1MB, they are trucated.

The sizes and grouping mechanics are internal performance details and are subject to change in future versions.

Executing an ordered list of operations on a sharded collection will generally be slower than executing an unordered list since with an ordered list, each operation must wait for the previous operation to finish.

If the collection does not exist, then insertMany() creates the collection on successful write.

If the document does not specify an _id field, then mongod adds the _id field and assign a unique ObjectId() for the document. Most drivers create an ObjectId and insert the _id field, but the mongod will create and populate the _id if the driver or application does not.

If the document contains an _id field, the _id value must be unique within the collection to avoid duplicate key error.

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

Inserts throw a BulkWriteError exception.

Excluding write concern errors, ordered operations stop after an error, while unordered operations continue to process any remaining write operations in the queue.

Write concern errors are displayed in the writeConcernErrors field, while all other errors are displayed in the writeErrors field. If an error is encountered, the number of successful write operations are displayed instead of a list of inserted _ids. Ordered operations display the single error encountered while unordered operations display each error in an array.

db.collection.insertMany() can be used inside distributed transactions.

Important

In most cases, a distributed transaction incurs a greater performance cost over single document writes, and the availability of distributed 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 distributed transactions.

For additional transactions usage considerations (such as runtime limit and oplog size limit), see also Production Considerations.

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

If you specify an insert on a non-existing collection in a transaction, MongoDB creates the collection implicitly.

Tip

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.

If an operation inserts a large amount of random data (for example, hashed indexes) on an indexed field, insert performance may decrease. Bulk inserts of random data create random index entries, which increase the size of the index. If the index reaches the size that requires each random insert to access a different index entry, the inserts result in a high rate of WiredTiger cache eviction and replacement. When this happens, the index is no longer fully in cache and is updated on disk, which decreases performance.

To improve the performance of bulk inserts of random data on indexed fields, you can either:

  • Drop the index, then recreate it after you insert the random data.

  • Insert the data into an empty unindexed collection.

Creating the index after the bulk insert sorts the data in memory and performs an ordered insert on all indexes.

If a db.collection.insertMany() operation successfully inserts one or more documents, the operation adds an entry on the oplog (operations log) for each inserted document. If the operation fails, the operation does not add an entry on the oplog.

The following examples insert documents into the products collection.

The following example uses db.collection.insertMany() to insert documents that do not contain the _id field:

try {
db.products.insertMany( [
{ item: "card", qty: 15 },
{ item: "envelope", qty: 20 },
{ item: "stamps" , qty: 30 }
] );
} catch (e) {
print (e);
}

The operation returns the following document:

{
"acknowledged" : true,
"insertedIds" : [
ObjectId("562a94d381cb9f1cd6eb0e1a"),
ObjectId("562a94d381cb9f1cd6eb0e1b"),
ObjectId("562a94d381cb9f1cd6eb0e1c")
]
}

Because the documents did not include _id, mongod creates and adds the _id field for each document and assigns it a unique ObjectId() value.

The ObjectId values are specific to the machine and time when the operation is run. As such, your values may differ from those in the example.

The following example/operation uses insertMany() to insert documents that include the _id field. The value of _id must be unique within the collection to avoid a duplicate key error.

try {
db.products.insertMany( [
{ _id: 10, item: "large box", qty: 20 },
{ _id: 11, item: "small box", qty: 55 },
{ _id: 12, item: "medium box", qty: 30 }
] );
} catch (e) {
print (e);
}

The operation returns the following document:

{ "acknowledged" : true, "insertedIds" : [ 10, 11, 12 ] }

Inserting a duplicate value for any key that is part of a unique index, such as _id, throws an exception. The following attempts to insert a document with a _id value that already exists:

try {
db.products.insertMany( [
{ _id: 13, item: "envelopes", qty: 60 },
{ _id: 13, item: "stamps", qty: 110 },
{ _id: 14, item: "packing tape", qty: 38 }
] );
} catch (e) {
print (e);
}

Since _id: 13 already exists, the following exception is thrown:

BulkWriteError({
"writeErrors" : [
{
"index" : 0,
"code" : 11000,
"errmsg" : "E11000 duplicate key error collection: inventory.products index: _id_ dup key: { : 13.0 }",
"op" : {
"_id" : 13,
"item" : "stamps",
"qty" : 110
}
}
],
"writeConcernErrors" : [ ],
"nInserted" : 1,
"nUpserted" : 0,
"nMatched" : 0,
"nModified" : 0,
"nRemoved" : 0,
"upserted" : [ ]
})

Note that one document was inserted: The first document of _id: 13 will insert successfully, but the second insert will fail. This will also stop additional documents left in the queue from being inserted.

With ordered to false, the insert operation would continue with any remaining documents.

The following attempts to insert multiple documents with _id field and ordered: false. The array of documents contains two documents with duplicate _id fields.

try {
db.products.insertMany( [
{ _id: 10, item: "large box", qty: 20 },
{ _id: 11, item: "small box", qty: 55 },
{ _id: 11, item: "medium box", qty: 30 },
{ _id: 12, item: "envelope", qty: 100},
{ _id: 13, item: "stamps", qty: 125 },
{ _id: 13, item: "tape", qty: 20},
{ _id: 14, item: "bubble wrap", qty: 30}
], { ordered: false } );
} catch (e) {
print (e);
}

The operation throws the following exception:

BulkWriteError({
"writeErrors" : [
{
"index" : 2,
"code" : 11000,
"errmsg" : "E11000 duplicate key error collection: inventory.products index: _id_ dup key: { : 11.0 }",
"op" : {
"_id" : 11,
"item" : "medium box",
"qty" : 30
}
},
{
"index" : 5,
"code" : 11000,
"errmsg" : "E11000 duplicate key error collection: inventory.products index: _id_ dup key: { : 13.0 }",
"op" : {
"_id" : 13,
"item" : "tape",
"qty" : 20
}
}
],
"writeConcernErrors" : [ ],
"nInserted" : 5,
"nUpserted" : 0,
"nMatched" : 0,
"nModified" : 0,
"nRemoved" : 0,
"upserted" : [ ]
})

While the document with item: "medium box" and item: "tape" failed to insert due to duplicate _id values, nInserted shows that the remaining 5 documents were inserted.

Given a three member replica set, the following operation specifies a w of majority and wtimeout of 100:

try {
db.products.insertMany(
[
{ _id: 10, item: "large box", qty: 20 },
{ _id: 11, item: "small box", qty: 55 },
{ _id: 12, item: "medium box", qty: 30 }
],
{ 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,
"insertedIds" : [
ObjectId("562a94d381cb9f1cd6eb0e1a"),
ObjectId("562a94d381cb9f1cd6eb0e1b"),
ObjectId("562a94d381cb9f1cd6eb0e1c")
]
}

If the total time required for all required nodes in the replica set to acknowledge the write operation is greater than wtimeout, the following writeConcernError is displayed when the wtimeout period has passed.

This operation returns:

WriteConcernError({
"code" : 64,
"errmsg" : "waiting for replication timed out",
"errInfo" : {
"wtimeout" : true,
"writeConcern" : { // Added in MongoDB 4.4
"w" : "majority",
"wtimeout" : 100,
"provenance" : "getLastErrorDefaults"
}
}
})
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