How to Remove Duplicates When Using $unionWith in MongoDB

Tutorialsrack 19/01/2025 MongoDB

Introduction

When working with MongoDB, combining multiple collections into a single result set is a common requirement. The $unionWith stage in the Aggregation Framework makes this task easy by allowing you to merge data from multiple collections. However, combining data often introduces duplicate records, which can lead to inaccuracies in reports or redundant processing.

This tutorial will guide you through effective ways to remove duplicates when using $unionWith in MongoDB, starting with the basics and progressing to advanced techniques.

What is $unionWith in MongoDB?

$unionWith is an aggregation stage in MongoDB that enables you to combine documents from multiple collections into a single result set. Think of it as SQL’s UNION operator. It allows seamless merging of datasets, but it does not automatically handle duplicate records.

Syntax:

{
  $unionWith: {
    coll: "<collectionName>",
    pipeline: [ <pipeline stages> ]
  }
}

• coll: Specifies the collection to include in the union.
• pipeline: Optional pipeline stages to filter or process data from the second collection.

Why Remove Duplicates in $unionWith?

Duplicates can occur when records with overlapping data exist in the collections being merged. Removing duplicates ensures:

• Accurate Results: Avoids overcounting or redundant entries.
• Optimized Performance: Reduces the size of the result set, improving query performance.
• Clean Data: Simplifies downstream processing and analysis.

Techniques to Remove Duplicates

1. Using $group

The $group stage is an effective way to remove duplicates by grouping records based on unique fields or a composite key.

Example

Input Collections:

Collection A:

{
  "_id": 1,
  "name": "Alice",
  "age": 25
}
{
  "_id": 2,
  "name": "Bob",
  "age": 30
}

Collection B:

{
  "_id": 3,
  "name": "Alice",
  "age": 25
}
{
  "_id": 4,
  "name": "Charlie",
  "age": 35
}

Query:

db.collectionA.aggregate([
  {
    $unionWith: "collectionB"
  },
  {
    $group: {
      _id: {
        name: "$name",
        age: "$age"
      },
      doc: { $first: "$$ROOT" }
    }
  },
  {
    $replaceRoot: { newRoot: "$doc" }
  }
])

Output:

{
  "_id": 1,
  "name": "Alice",
  "age": 25
}
{
  "_id": 2,
  "name": "Bob",
  "age": 30
}
{
  "_id": 4,
  "name": "Charlie",
  "age": 35
}

2. Using $match with Unique Fields

Filter duplicates by using the $match stage to include only distinct records based on a unique identifier.

Query:

db.collectionA.aggregate([
  {
    $unionWith: "collectionB"
  },
  {
    $match: {
      uniqueField: { $exists: true }
    }
  }
])

This works well when a unique identifier field is already present.

3. Using $set or $addFields to Create a Unique Identifier

When no natural unique key exists, you can generate a unique identifier by concatenating fields.

Query:

db.collectionA.aggregate([
  {
    $unionWith: "collectionB"
  },
  {
    $addFields: {
      uniqueId: {
        $concat: ["$name", "_", { $toString: "$age" }]
      }
    }
  },
  {
    $group: {
      _id: "$uniqueId",
      doc: { $first: "$$ROOT" }
    }
  },
  {
    $replaceRoot: { newRoot: "$doc" }
  }
])

Output:

{
  "_id": 1,
  "name": "Alice",
  "age": 25
}
{
  "_id": 2,
  "name": "Bob",
  "age": 30
}
{
  "_id": 4,
  "name": "Charlie",
  "age": 35
}

Real-World Use Cases

Merging User Data Across Collections

Combine user records from multiple collections while ensuring unique entries:

db.users.aggregate([
  {
    $unionWith: "archivedUsers"
  },
  {
    $group: {
      _id: "$email",
      doc: { $first: "$$ROOT" }
    }
  },
  {
    $replaceRoot: { newRoot: "$doc" }
  }
])

This removes duplicates based on the user’s email address.

Combining Logs from Different Sources

Aggregate logs from multiple collections while avoiding duplicate log entries:

db.logs.aggregate([
  {
    $unionWith: "archivedLogs"
  },
  {
    $addFields: {
      uniqueKey: {
        $concat: ["$timestamp", "_", "$logId"]
      }
    }
  },
  {
    $group: {
      _id: "$uniqueKey",
      doc: { $first: "$$ROOT" }
    }
  },
  {
    $replaceRoot: { newRoot: "$doc" }
  }
])

Common Challenges and Solutions

1. Handling Missing Fields: Use $ifNull to provide default values for missing fields when creating unique keys.
2. Performance Concerns: Minimize data processing by filtering with $match before $unionWith.
3. Memory Limitations: Break large datasets into smaller chunks or use MongoDB Atlas for larger aggregation pipelines.

Key Takeaways

• $unionWith is a powerful feature for merging data from multiple collections.
• Removing duplicates ensures clean, accurate, and optimized result sets.
• Use $group, $match, or $addFields to handle duplicates effectively.
• Always test with real-world datasets to identify edge cases and performance bottlenecks.

Summary

Managing duplicates when using $unionWith in MongoDB is crucial for maintaining data integrity and query efficiency. By leveraging techniques like grouping, filtering, and creating unique keys, you can ensure that your combined datasets are free from redundant entries. With these tools in hand, you’re well-equipped to handle advanced data merging scenarios in MongoDB.

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