MongoDB: Using $mergeObjects to merge multiple documents into one

Introduction

MongoDB, a NoSQL database famous for its flexibility and performance, provides several powerful aggregation pipeline operators. One such operator is $mergeObjects, which allows for the merging of multiple MongoDB documents into a single document. This feature can be particularly useful for combining fields from multiple documents or even forming new fields from existing ones.

In this tutorial, we will dive into the capabilities of $mergeObjects, beginning with basic examples before progressing to more advanced use cases. By the end of this guide, you’ll have a solid understanding of how to leverage this operator to streamline your data processing in MongoDB.

Basic Usage of $mergeObjects

At its simplest form, $mergeObjects combines the fields of multiple documents, giving precedence to the fields from the last document in case of overlaps. The following is a basic example of its usage:

db.collection.aggregate([
    { $match: { _id: someId }},
    {
        $replaceRoot: { newRoot: { $mergeObjects: ['$document1', '$document2'] } }
    }
])

This aggregation pipeline replaced the root document with merged results of document1 and document2.

Assuming document1 contains { a:1, b:1 } and document2 contains { b:2, c:2 }, the output will be:

{ "a": 1, "b": 2, "c": 2 }

Note that the value of b from document2 overrides the value from document1.

Merging Embedded Documents

When dealing with embedded documents, $mergeObjects also works effectively. For example:

db.collection.aggregate([
    { $match: { '_id': someId }},
    {
        $project: {
            mergedDocument: { 
                $mergeObjects: ['$embeddedDoc1', '$embeddedDoc2']
            }
        }
    }
])

In cases where your documents have nested structures, the merge applies recursively, so fields inside the embedded documents are also merged.

Combining Multiple Objects with $mergeObjects

$mergeObjects can be used in conjunction with the $group stage to combine fields from all the documents in a group. Here is an example:

db.collection.aggregate([
    {
        $group: {
            _id: "$category",
            combinedItem: { $mergeObjects: "$item" }
        }
    }
])

In this snippet, documents are grouped by category, and for each group, the item fields are merged into a new combinedItem field. The merging is based on the order the documents appear in the group.

Advanced Merging Patterns

For more complex scenarios, $mergeObjects can be used in conjunction with other aggregation operations to shape the documents in sophisticated ways. Following is an example of a pipeline where multiple objects are combined and then further modified:

db.collection.aggregate([
    { $match: { '_id': someId }},
    {
        $group: {
            _id: null,
            allOptions: { $push: "$options" }
        }
    },
    {
        $project: {
            unifiedOptions: {
                $reduce: {
                    input: "$allOptions",
                    initialValue: {},
                    in: { $mergeObjects: ["$value", "$this"] }
                }
            }
        }
    }
])

In the above example, options from all matched documents are combined into a single unifiedOptions document.

Handling Arrays with $mergeObjects

Merging documents with array fields needs special attention. In cases where array fields are encountered, they are treated as any other field, meaning entire arrays are overwritten. Here’s a peek at how this is managed:

db.collection.aggregate([
    { $match: { _id: someId }},
    {
        $project: {
            mergedData: { 
                $mergeObjects: ['$data', { arrayField: { $concatArrays: ['$data.arrayField', additionalArray] } }] 
            }
        }
    }
])

The $concatArrays operator can be used alongside $mergeObjects to concatenate arrays rather than overwriting them.

Conclusion

As demonstrated, $mergeObjects provides a powerful tool in the MongoDB aggregation pipeline, simplifying the task of combining documents with a high degree of flexibility and control. Understanding this operator’s nuances, such as overwrite behavior and array handling, empowers developers to write more effective and efficient queries.