$merge (aggregation)

_id Field Generation

If the _id field is not present in a document from the aggregation pipeline results, the $merge stage generates it automatically.

For example, in the following aggregation pipeline, $project excludes the _id field from the documents passed into $merge. When $merge writes these documents to the "newCollection", $merge generates a new _id field and value.

db.sales.aggregate( [
   { $project: { _id: 0 } },
   { $merge : { into : "newCollection" } }
] )

Create a New Collection if Output Collection is Non-Existent

The $merge operation creates a new collection if the specified output collection does not exist.

• The output collection is created when $merge writes the first document into the collection and is immediately visible.

• If the aggregation fails, any writes completed by the $merge before the error will not be rolled back.

If the output collection does not exist, $merge requires the on identifier to be the _id field. To use a different on field value for a collection that does not exist, you can create the collection first by creating a unique index on the desired field(s) first. For example, if the output collection newDailySales201905 does not exist and you want to specify the salesDate field as the on identifier:

db.newDailySales201905.createIndex( { salesDate: 1 }, { unique: true } )
db.sales.aggregate( [
   { $match: { date: { $gte: new Date("2019-05-01"), $lt: new Date("2019-06-01") } } },
   { $group: { _id: { $dateToString: { format: "%Y-%m-%d", date: "$date" } }, totalqty: { $sum: "$quantity" } } },
   { $project: { _id: 0, salesDate: { $toDate: "$_id" }, totalqty: 1 } },
   { $merge : { into : "newDailySales201905", on: "salesDate" } }
] )

Output to a Sharded Collection

The $merge stage can output to a sharded collection. When the output collection is sharded, $merge uses the _id field and all the shard key fields as the default on identifier. If you override the default, the on identifier must include all the shard key fields:

{ $merge: {
   into: "<shardedColl>" or { db:"<sharding enabled db>", coll: "<shardedColl>" },
   on: [ "<shardkeyfield1>", "<shardkeyfield2>",... ], // Shard key fields and any additional fields
   let: <variables>,                                         // Optional
   whenMatched: <replace|keepExisting|merge|fail|pipeline>,  // Optional
   whenNotMatched: <insert|discard|fail>                     // Optional
} }

For example, in a database that has sharding enabled, use the sh.shardCollection() method to create a new sharded collection newrestaurants with the postcode field as the shard key.

sh.enableSharding("exampledb");     // Sharding must be enabled in the database
sh.shardCollection(
   "exampledb.newrestaurants",      // Namespace of the collection to shard
   { postcode: 1 },                 // Shard key
);

The newrestaurants collection will contain documents with information on new restaurant openings by month (date field) and postcode (shard key); specifically, the on identifier is ["date", "postcode"] (the ordering of the fields does not matter). Because $merge requires a unique index with keys that correspond to the on identifier fields, create the unique index (the ordering of the fields do not matter): [1]

use exampledb
db.newrestaurants.createIndex( { postcode: 1, date: 1 }, { unique: true } )

With the sharded collection restaurants and the unique index created, you can use $merge to output the aggregation results to this collection, matching on [ "date", "postcode" ] as in this example:

use exampledb
db.openings.aggregate([
   { $group: {
      _id: { date: { $dateToString: { format: "%Y-%m", date: "$date" } }, postcode: "$postcode" },
      restaurants: { $push: "$restaurantName" } } },
   { $project: { _id: 0, postcode: "$_id.postcode", date: "$_id.date", restaurants: 1 } },
   { $merge: { into: "newrestaurants", "on": [ "date", "postcode" ], whenMatched: "replace", whenNotMatched: "insert" } }
])

Replace Documents ($merge) vs Replace Collection ($out)

$merge can replace an existing document in the output collection if the aggregation results contain a document or documents that match based on the on specification. As such, $merge can replace all documents in the existing collection if the aggregation results include matching documents for all existing documents in the collection and you specify "replace" for whenMatched.

However, to replace an existing collection regardless of the aggregation results, use $out instead.

Existing Documents and _id and Shard Key Values

The $merge errors if the $merge results in a change to an existing document's _id value.

Unique Index Constraints

If the unique index used by $merge for on field(s) is dropped mid-aggregation, there is no guarantee that the aggregation will be killed. If the aggregation continues, there is no guarantee that documents do not have duplicate on field values.

If the $merge attempts to write a document that violates any unique index on the output collection, the operation generates an error. For example:

• Insert a non-matching document that violates a unique index other than the index on the on field(s).

• Fail if there is a matching document in the collection. Specifically, the operation attempts to insert the matching document which violates the unique index on the on field(s).

• Replace an existing document with a new document that violates a unique index other than the index on the on field(s).

• Merge the matching documents that results in a document that violates a unique index other than the index on the on field(s).

Schema Validation

If your collection uses schema validation and has validationAction set to error, inserting an invalid document or updating a document with invalid values with $merge throws a MongoServerError and the document is not written to the target collection. If there are multiple invalid documents, only the first invalid document encountered throws an error. All valid documents are written to the target collection, and all invalid documents fail to write.

whenMatched Pipeline Behavior

If all of the following are true for a $merge stage, $merge inserts the document directly into the output collection:

• The value of whenMatched is an aggregation pipeline,

• The value of whenNotMatched is insert, and

• There is no match for a document in the output collection,

$merge inserts the document directly into the output collection.

$merge and $out Comparison

With the introduction of $merge, MongoDB provides two stages, $merge and $out, for writing the results of the aggregation pipeline to a collection:

Output to the Same Collection that is Being Aggregated

$merge can output to the same collection that is being aggregated. You can also output to a collection which appears in other stages of the pipeline, such as $lookup.

On-Demand Materialized View: Initial Creation

If the output collection does not exist, the $merge creates the collection.

For example, a collection named salaries in the zoo database is populated with the employee salary and department history:

db.getSiblingDB("zoo").salaries.insertMany([
   { "_id" : 1, employee: "Ant", dept: "A", salary: 100000, fiscal_year: 2017 },
   { "_id" : 2, employee: "Bee", dept: "A", salary: 120000, fiscal_year: 2017 },
   { "_id" : 3, employee: "Cat", dept: "Z", salary: 115000, fiscal_year: 2017 },
   { "_id" : 4, employee: "Ant", dept: "A", salary: 115000, fiscal_year: 2018 },
   { "_id" : 5, employee: "Bee", dept: "Z", salary: 145000, fiscal_year: 2018 },
   { "_id" : 6, employee: "Cat", dept: "Z", salary: 135000, fiscal_year: 2018 },
   { "_id" : 7, employee: "Gecko", dept: "A", salary: 100000, fiscal_year: 2018 },
   { "_id" : 8, employee: "Ant", dept: "A", salary: 125000, fiscal_year: 2019 },
   { "_id" : 9, employee: "Bee", dept: "Z", salary: 160000, fiscal_year: 2019 },
   { "_id" : 10, employee: "Cat", dept: "Z", salary: 150000, fiscal_year: 2019 }
])

You can use the $group and $merge stages to initially create a collection named budgets (in the reporting database) from the data currently in the salaries collection:

db.getSiblingDB("zoo").salaries.aggregate( [
   { $group: { _id: { fiscal_year: "$fiscal_year", dept: "$dept" }, salaries: { $sum: "$salary" } } },
   { $merge : { into: { db: "reporting", coll: "budgets" }, on: "_id",  whenMatched: "replace", whenNotMatched: "insert" } }
] )

• $group stage to group the salaries by the fiscal_year and dept.

• $merge stage writes the output of the preceding $group stage to the budgets collection in the reporting database.

To view the documents in the new budgets collection:

db.getSiblingDB("reporting").budgets.find().sort( { _id: 1 } )

The budgets collection contains the following documents:

{ "_id" : { "fiscal_year" : 2017, "dept" : "A" }, "salaries" : 220000 }
{ "_id" : { "fiscal_year" : 2017, "dept" : "Z" }, "salaries" : 115000 }
{ "_id" : { "fiscal_year" : 2018, "dept" : "A" }, "salaries" : 215000 }
{ "_id" : { "fiscal_year" : 2018, "dept" : "Z" }, "salaries" : 280000 }
{ "_id" : { "fiscal_year" : 2019, "dept" : "A" }, "salaries" : 125000 }
{ "_id" : { "fiscal_year" : 2019, "dept" : "Z" }, "salaries" : 310000 }

On-Demand Materialized View: Update/Replace Data

The following example uses the collections in the previous example.

The example salaries collection contains the employee salary and department history:

{ "_id" : 1, employee: "Ant", dept: "A", salary: 100000, fiscal_year: 2017 },
{ "_id" : 2, employee: "Bee", dept: "A", salary: 120000, fiscal_year: 2017 },
{ "_id" : 3, employee: "Cat", dept: "Z", salary: 115000, fiscal_year: 2017 },
{ "_id" : 4, employee: "Ant", dept: "A", salary: 115000, fiscal_year: 2018 },
{ "_id" : 5, employee: "Bee", dept: "Z", salary: 145000, fiscal_year: 2018 },
{ "_id" : 6, employee: "Cat", dept: "Z", salary: 135000, fiscal_year: 2018 },
{ "_id" : 7, employee: "Gecko", dept: "A", salary: 100000, fiscal_year: 2018 },
{ "_id" : 8, employee: "Ant", dept: "A", salary: 125000, fiscal_year: 2019 },
{ "_id" : 9, employee: "Bee", dept: "Z", salary: 160000, fiscal_year: 2019 },
{ "_id" : 10, employee: "Cat", dept: "Z", salary: 150000, fiscal_year: 2019 }

The example budgets collection contains the cumulative yearly budgets:

{ "_id" : { "fiscal_year" : 2017, "dept" : "A" }, "salaries" : 220000 }
{ "_id" : { "fiscal_year" : 2017, "dept" : "Z" }, "salaries" : 115000 }
{ "_id" : { "fiscal_year" : 2018, "dept" : "A" }, "salaries" : 215000 }
{ "_id" : { "fiscal_year" : 2018, "dept" : "Z" }, "salaries" : 280000 }
{ "_id" : { "fiscal_year" : 2019, "dept" : "A" }, "salaries" : 125000 }
{ "_id" : { "fiscal_year" : 2019, "dept" : "Z" }, "salaries" : 310000 }

During the current fiscal year (2019 in this example), new employees are added to the salaries collection and new head counts are pre-allocated for the next year:

db.getSiblingDB("zoo").salaries.insertMany([
   { "_id" : 11,  employee: "Wren", dept: "Z", salary: 100000, fiscal_year: 2019 },
   { "_id" : 12,  employee: "Zebra", dept: "A", salary: 150000, fiscal_year: 2019 },
   { "_id" : 13,  employee: "headcount1", dept: "Z", salary: 120000, fiscal_year: 2020 },
   { "_id" : 14,  employee: "headcount2", dept: "Z", salary: 120000, fiscal_year: 2020 }
])

To update the budgets collection to reflect the new salary information, the following aggregation pipeline uses:

• $match stage to find all documents with fiscal_year greater than or equal to 2019.

• $group stage to group the salaries by the fiscal_year and dept.

• $merge to write the result set to the budgets collection, replacing documents with the same _id value (in this example, a document with the fiscal year and dept). For documents that do not have matches in the collection, $merge inserts the new documents.

db.getSiblingDB("zoo").salaries.aggregate( [
   { $match : { fiscal_year:  { $gte : 2019 } } },
   { $group: { _id: { fiscal_year: "$fiscal_year", dept: "$dept" }, salaries: { $sum: "$salary" } } },
   { $merge : { into: { db: "reporting", coll: "budgets" }, on: "_id",  whenMatched: "replace", whenNotMatched: "insert" } }
] )

After the aggregation is run, view the documents in the budgets collection:

db.getSiblingDB("reporting").budgets.find().sort( { _id: 1 } )

The budgets collection incorporates the new salary data for fiscal year 2019 and adds new documents for fiscal year 2020:

{ "_id" : { "fiscal_year" : 2017, "dept" : "A" }, "salaries" : 220000 }
{ "_id" : { "fiscal_year" : 2017, "dept" : "Z" }, "salaries" : 115000 }
{ "_id" : { "fiscal_year" : 2018, "dept" : "A" }, "salaries" : 215000 }
{ "_id" : { "fiscal_year" : 2018, "dept" : "Z" }, "salaries" : 280000 }
{ "_id" : { "fiscal_year" : 2019, "dept" : "A" }, "salaries" : 275000 }
{ "_id" : { "fiscal_year" : 2019, "dept" : "Z" }, "salaries" : 410000 }
{ "_id" : { "fiscal_year" : 2020, "dept" : "Z" }, "salaries" : 240000 }

Only Insert New Data

To ensure that the $merge does not overwrite existing data in the collection, set whenMatched to keepExisting or fail.

The example salaries collection in the zoo database contains the employee salary and department history:

{ "_id" : 1, employee: "Ant", dept: "A", salary: 100000, fiscal_year: 2017 },
{ "_id" : 2, employee: "Bee", dept: "A", salary: 120000, fiscal_year: 2017 },
{ "_id" : 3, employee: "Cat", dept: "Z", salary: 115000, fiscal_year: 2017 },
{ "_id" : 4, employee: "Ant", dept: "A", salary: 115000, fiscal_year: 2018 },
{ "_id" : 5, employee: "Bee", dept: "Z", salary: 145000, fiscal_year: 2018 },
{ "_id" : 6, employee: "Cat", dept: "Z", salary: 135000, fiscal_year: 2018 },
{ "_id" : 7, employee: "Gecko", dept: "A", salary: 100000, fiscal_year: 2018 },
{ "_id" : 8, employee: "Ant", dept: "A", salary: 125000, fiscal_year: 2019 },
{ "_id" : 9, employee: "Bee", dept: "Z", salary: 160000, fiscal_year: 2019 },
{ "_id" : 10, employee: "Cat", dept: "Z", salary: 150000, fiscal_year: 2019 }

A collection orgArchive in the reporting database contains historical departmental organization records for the past fiscal years. Archived records should not be modified.

{ "_id" : ObjectId("5cd8c68261baa09e9f3622be"), "employees" : [ "Ant", "Gecko" ], "dept" : "A", "fiscal_year" : 2018 }
{ "_id" : ObjectId("5cd8c68261baa09e9f3622bf"), "employees" : [ "Ant", "Bee" ], "dept" : "A", "fiscal_year" : 2017 }
{ "_id" : ObjectId("5cd8c68261baa09e9f3622c0"), "employees" : [ "Bee", "Cat" ], "dept" : "Z", "fiscal_year" : 2018 }
{ "_id" : ObjectId("5cd8c68261baa09e9f3622c1"), "employees" : [ "Cat" ], "dept" : "Z", "fiscal_year" : 2017 }

The orgArchive collection has a unique compound index on the fiscal_year and dept fields. Specifically, there should be at most one record for the same fiscal year and department combination:

db.getSiblingDB("reporting").orgArchive.createIndex ( { fiscal_year: 1, dept: 1 }, { unique: true } )

At the end of current fiscal year (2019 in this example), the salaries collection contain the following documents:

{ "_id" : 1, "employee" : "Ant", "dept" : "A", "salary" : 100000, "fiscal_year" : 2017 }
{ "_id" : 2, "employee" : "Bee", "dept" : "A", "salary" : 120000, "fiscal_year" : 2017 }
{ "_id" : 3, "employee" : "Cat", "dept" : "Z", "salary" : 115000, "fiscal_year" : 2017 }
{ "_id" : 4, "employee" : "Ant", "dept" : "A", "salary" : 115000, "fiscal_year" : 2018 }
{ "_id" : 5, "employee" : "Bee", "dept" : "Z", "salary" : 145000, "fiscal_year" : 2018 }
{ "_id" : 6, "employee" : "Cat", "dept" : "Z", "salary" : 135000, "fiscal_year" : 2018 }
{ "_id" : 7, "employee" : "Gecko", "dept" : "A", "salary" : 100000, "fiscal_year" : 2018 }
{ "_id" : 8, "employee" : "Ant", "dept" : "A", "salary" : 125000, "fiscal_year" : 2019 }
{ "_id" : 9, "employee" : "Bee", "dept" : "Z", "salary" : 160000, "fiscal_year" : 2019 }
{ "_id" : 10, "employee" : "Cat", "dept" : "Z", "salary" : 150000, "fiscal_year" : 2019 }
{ "_id" : 11, "employee" : "Wren", "dept" : "Z", "salary" : 100000, "fiscal_year" : 2019 }
{ "_id" : 12, "employee" : "Zebra", "dept" : "A", "salary" : 150000, "fiscal_year" : 2019 }
{ "_id" : 13, "employee" : "headcount1", "dept" : "Z", "salary" : 120000, "fiscal_year" : 2020 }
{ "_id" : 14, "employee" : "headcount2", "dept" : "Z", "salary" : 120000, "fiscal_year" : 2020 }

To update the orgArchive collection to include the fiscal year 2019 that has just ended, the following aggregation pipeline uses:

• $match stage to find all documents with fiscal_year equal to 2019.

• $group stage to group the employees by the fiscal_year and dept.

• $project stage to suppress the _id field and add separate dept and fiscal_year field. When the documents are passed to $merge, $merge automatically generates a new _id field for the documents.

• $merge to write the result set to orgArchive.

  The $merge stage matches documents on the dept and fiscal_year fields and fails when matched. That is, if a document already exists for the same department and fiscal year, the $merge errors.

db.getSiblingDB("zoo").salaries.aggregate( [
    { $match: { fiscal_year: 2019 }},
    { $group: { _id: { fiscal_year: "$fiscal_year", dept: "$dept" }, employees: { $push: "$employee" } } },
    { $project: { _id: 0, dept: "$_id.dept", fiscal_year: "$_id.fiscal_year", employees: 1 } },
    { $merge : { into : { db: "reporting", coll: "orgArchive" }, on: [ "dept", "fiscal_year" ], whenMatched: "fail" } }
] )

After the operation, the orgArchive collection contains the following documents:

{ "_id" : ObjectId("5caccc6a66b22dd8a8cc419f"), "employees" : [ "Ahn", "Bess" ], "dept" : "A", "fiscal_year" : 2017 }
{ "_id" : ObjectId("5caccc6a66b22dd8a8cc419e"), "employees" : [ "Ahn", "Gee" ], "dept" : "A", "fiscal_year" : 2018 }
{ "_id" : ObjectId("5caccd0b66b22dd8a8cc438e"), "employees" : [ "Ahn", "Zeb" ], "dept" : "A", "fiscal_year" : 2019 }
{ "_id" : ObjectId("5caccc6a66b22dd8a8cc41a0"), "employees" : [ "Carl" ], "dept" : "Z", "fiscal_year" : 2017 }
{ "_id" : ObjectId("5caccc6a66b22dd8a8cc41a1"), "employees" : [ "Bess", "Carl" ], "dept" : "Z", "fiscal_year" : 2018 }
{ "_id" : ObjectId("5caccd0b66b22dd8a8cc438d"), "employees" : [ "Bess", "Carl", "Wen" ], "dept" : "Z", "fiscal_year" : 2019 }

If the orgArchive collection already contained a document for 2019 for department "A" and/or "B", the aggregation fails because of the duplicate key error. However, any document inserted before the error will not be rolled back.

If you specify keepExisting for the matching document, the aggregation does not affect the matching document and does not error with duplicate key error. Similarly, if you specify replace, the operation would not fail; however, the operation would replace the existing document.

Merge Results from Multiple Collections

By default, if a document in the aggregation results matches a document in the collection, the $merge stage merges the documents.

An example collection purchaseorders is populated with the purchase order information by quarter and regions:

db.purchaseorders.insertMany( [
   { _id: 1, quarter: "2019Q1", region: "A", qty: 200, reportDate: new Date("2019-04-01") },
   { _id: 2, quarter: "2019Q1", region: "B", qty: 300, reportDate: new Date("2019-04-01") },
   { _id: 3, quarter: "2019Q1", region: "C", qty: 700, reportDate: new Date("2019-04-01") },
   { _id: 4, quarter: "2019Q2", region: "B", qty: 300, reportDate: new Date("2019-07-01") },
   { _id: 5, quarter: "2019Q2", region: "C", qty: 1000, reportDate: new Date("2019-07-01") },
   { _id: 6, quarter: "2019Q2", region: "A", qty: 400, reportDate: new Date("2019-07-01") },
] )

Another example collection reportedsales is populated with the reported sales information by quarter and regions:

db.reportedsales.insertMany( [
   { _id: 1, quarter: "2019Q1", region: "A", qty: 400, reportDate: new Date("2019-04-02") },
   { _id: 2, quarter: "2019Q1", region: "B", qty: 550, reportDate: new Date("2019-04-02") },
   { _id: 3, quarter: "2019Q1", region: "C", qty: 1000, reportDate: new Date("2019-04-05") },
   { _id: 4, quarter: "2019Q2", region: "B", qty: 500, reportDate: new Date("2019-07-02") },
] )

Assume that, for reporting purposes, you want to view the data by quarter in the following format:

{ "_id" : "2019Q1", "sales" : 1950, "purchased" : 1200 }
{ "_id" : "2019Q2", "sales" : 500, "purchased" : 1700 }

You can use the $merge to merge in results from the purchaseorders collection and the reportedsales collection to create a new collection quarterlyreport.

To create the quarterlyreport collection, you can use the following pipeline:

db.purchaseorders.aggregate( [
   { $group: { _id: "$quarter", purchased: { $sum: "$qty" } } },  // group purchase orders by quarter
   { $merge : { into: "quarterlyreport", on: "_id",  whenMatched: "merge", whenNotMatched: "insert" } }
])

First stage:

The $group stage groups by the quarter and uses $sum to add the qty fields into a new purchased field. For example:

To create the quarterlyreport collection, you can use this pipeline:

{ "_id" : "2019Q2", "purchased" : 1700 }
{ "_id" : "2019Q1", "purchased" : 1200 }

Second stage:

The $merge stage writes the documents to the quarterlyreport collection in the same database. If the stage finds an existing document in the collection that matches on the _id field, the stage merges the matching documents. Otherwise, the stage inserts the document. For the initial creation, no documents should match.

To view the documents in the collection, run the following operation:

db.quarterlyreport.find().sort( { _id: 1 } )

The collection contains the following documents:

{ "_id" : "2019Q1", "sales" : 1200, "purchased" : 1200 }
{ "_id" : "2019Q2", "sales" : 1700, "purchased" : 1700 }

Similarly, run the following aggregation pipeline against the reportedsales collection to merge the sales results into the quarterlyreport collection.

db.reportedsales.aggregate( [
   { $group: { _id: "$quarter", sales: { $sum: "$qty" } } },  // group sales by quarter
   { $merge : { into: "quarterlyreport", on: "_id",  whenMatched: "merge", whenNotMatched: "insert" } }
])

First stage:

The $group stage groups by the quarter and uses $sum to add the qty fields into a new sales field. For example:

{ "_id" : "2019Q2", "sales" : 500 }
{ "_id" : "2019Q1", "sales" : 1950 }

Second stage:

The $merge stage writes the documents to the quarterlyreport collection in the same database. If the stage finds an existing document in the collection that matches on the _id field (the quarter), the stage merges the matching documents. Otherwise, the stage inserts the document.

To view the documents in the quarterlyreport collection after the data has been merged, run the following operation:

db.quarterlyreport.find().sort( { _id: 1 } )

The collection contains the following documents:

{ "_id" : "2019Q1", "sales" : 1950, "purchased" : 1200 }
{ "_id" : "2019Q2", "sales" : 500, "purchased" : 1700 }

Use the Pipeline to Customize the Merge

The $merge can use a custom update pipeline when documents match. The whenMatched pipeline can have the following stages:

• $addFields and its alias $set

• $project and its alias $unset

• $replaceRoot and its alias $replaceWith

An example collection votes is populated with the daily vote tally. Create the collection with the following documents:s

db.votes.insertMany( [
   { date: new Date("2019-05-01"), "thumbsup" : 1, "thumbsdown" : 1 },
   { date: new Date("2019-05-02"), "thumbsup" : 3, "thumbsdown" : 1 },
   { date: new Date("2019-05-03"), "thumbsup" : 1, "thumbsdown" : 1 },
   { date: new Date("2019-05-04"), "thumbsup" : 2, "thumbsdown" : 2 },
   { date: new Date("2019-05-05"), "thumbsup" : 6, "thumbsdown" : 10 },
   { date: new Date("2019-05-06"), "thumbsup" : 13, "thumbsdown" : 16 }
] )

Another example collection monthlytotals has the up-to-date monthly vote totals. Create the collection with the following document:

db.monthlytotals.insertOne(
   { "_id" : "2019-05", "thumbsup" : 26, "thumbsdown" : 31 }
)

At the end of each day, that day's votes is inserted into the votes collection:

db.votes.insertOne(
   { date: new Date("2019-05-07"), "thumbsup" : 14, "thumbsdown" : 10 }
)

You can use $merge with an custom pipeline to update the existing document in the collection monthlytotals:

db.votes.aggregate([
   { $match: { date: { $gte: new Date("2019-05-07"), $lt: new Date("2019-05-08") } } },
   { $project: { _id: { $dateToString: { format: "%Y-%m", date: "$date" } }, thumbsup: 1, thumbsdown: 1 } },
   { $merge: {
         into: "monthlytotals",
         on: "_id",
         whenMatched:  [
            { $addFields: {
                thumbsup: { $add:[ "$thumbsup", "$$new.thumbsup" ] },
                thumbsdown: { $add: [ "$thumbsdown", "$$new.thumbsdown" ] }
            } } ],
         whenNotMatched: "insert"
   } }
])

First stage:

The $match stage finds the specific day's votes. For example:

{ "_id" : ObjectId("5ce6097c436eb7e1203064a6"), "date" : ISODate("2019-05-07T00:00:00Z"), "thumbsup" : 14, "thumbsdown" : 10 }

Second stage:

The $project stage sets the _id field to a year-month string. For example:

{ "thumbsup" : 14, "thumbsdown" : 10, "_id" : "2019-05" }

Third stage:

The $merge stage writes the documents to the monthlytotals collection in the same database. If the stage finds an existing document in the collection that matches on the _id field, the stage uses a pipeline to add the thumbsup votes and the thumbsdown votes.

• This pipeline cannot directly accesses the fields from the results document. To access the thumbsup field and the thumbsdown field in the results document, the pipeline uses the $$new variable; i.e. $$new.thumbsup and $new.thumbsdown.

• This pipeline can directly accesses the thumbsup field and the thumbsdown field in the existing document in the collection; i.e. $thumbsup and $thumbsdown.

The resulting document replaces the existing document.

To view documents in the monthlytotals collection after the merge operation, run the following operation:

db.monthlytotals.find()

The collection contains the following document:

{ "_id" : "2019-05", "thumbsup" : 40, "thumbsdown" : 41 }

Use Variables to Customize the Merge

You can use variables in the $merge stage whenMatched field. Variables must be defined before they can be used.

Define variables in one or both of the following:

• $merge stage let

• aggregate command let (starting in MongoDB 5.0)

To use variables in whenMatched:

Specify the double dollar sign ($$) prefix together with the variable name in the form $$<variable_name>. For example, $$year. If the variable is set to a document, you can also include a document field in the form $$<variable_name>.<field>. For example, $$year.month.

The tabs below demonstrate behavior when variables are defined in the merge stage, the aggregate command, or both.

db.cakeSales.insertOne( [
   { _id: 1, flavor: "chocolate", salesTotal: 1580,
     salesTrend: "up" }
] )
db.runCommand( {
   aggregate: db.cakeSales.getName(),
   pipeline: [ {
      $merge: {
         into: db.cakeSales.getName(),
         let : { year: "2020" },
         whenMatched: [ {
            $addFields: { "salesYear": "$$year" }
         } ]
      }
   } ],
   cursor: {}
} )
db.cakeSales.find()

{ "_id" : 1, "flavor" : "chocolate", "salesTotal" : 1580,
  "salesTrend" : "up", "salesYear" : "2020" }

db.cakeSales.insertOne(
   { _id: 1, flavor: "chocolate", salesTotal: 1580,
     salesTrend: "up" }
)
db.runCommand( {
   aggregate: db.cakeSales.getName(),
      pipeline: [ {
         $merge: {
            into: db.cakeSales.getName(),
            whenMatched: [ {
               $addFields: { "salesYear": "$$year" } }
            ] }
         }
   ],
   cursor: {},
   let : { year: "2020" }
} )
db.cakeSales.find()

{ "_id" : 1, "flavor" : "chocolate", "salesTotal" : 1580,
  "salesTrend" : "up", "salesYear" : "2020" }

db.cakeSales.insertOne(
   { _id: 1, flavor: "chocolate", salesTotal: 1580,
     salesTrend: "up" }
)
db.runCommand( {
   aggregate: db.cakeSales.getName(),
      pipeline: [ {
         $merge: {
            into: db.cakeSales.getName(),
            let : { year: "2020" },
            whenMatched: [ {
               $addFields: { "salesYear": "$$year" }
            } ]
         }
      } ],
   cursor: {},
   let : { year: "2019" }
} )
db.cakeSales.find()

{
  _id: 1,
  flavor: 'chocolate',
  salesTotal: 1580,
  salesTrend: 'up',
  salesYear: '2020'
}