Advanced Usage of Aggregation Pipelines in MongoDB

Introduction

The aggregation pipeline is a powerful framework for performing data aggregation operations in MongoDB. It consists of a series of stages, each of which transforms the documents as they pass through the pipeline. This tutorial covers advanced usage of the aggregation pipeline, including common stages and practical examples.

Common Aggregation Stages

Here are some common aggregation stages and their purposes:

• $match: Filters documents to pass only those that match the specified condition.
• $group: Groups documents by a specified expression and applies an accumulator to each group.
• $project: Reshapes each document by including, excluding, or adding fields.
• $sort: Sorts documents by a specified field or fields.
• $limit: Limits the number of documents passed to the next stage.
• $lookup: Performs a left outer join to another collection.

Example: Aggregation Pipeline

Here is an example of an aggregation pipeline that performs several operations on a collection of sales data:

pipeline = [
    {"$match": {"status": "A"}},
    {"$group": {
        "_id": "$cust_id",
        "total": {"$sum": "$amount"}
    }},
    {"$sort": {"total": -1}},
    {"$limit": 5}
]

result = db.sales.aggregate(pipeline)
for doc in result:
    print(doc)
            

Advanced Usage

Advanced usage of the aggregation pipeline includes using expressions, conditional logic, and custom functions. Here are some examples:

Using Expressions

Expressions allow you to compute values in the aggregation pipeline:

pipeline = [
    {"$project": {
        "item": 1,
        "discountedPrice": {"$multiply": ["$price", 0.9]}
    }}
]

result = db.products.aggregate(pipeline)
for doc in result:
    print(doc)
            

Conditional Logic

Use conditional logic to apply different operations based on conditions:

pipeline = [
    {"$project": {
        "item": 1,
        "discount": {
            "$cond": {
                "if": {"$gte": ["$price", 100]},
                "then": 20,
                "else": 10
            }
        }
    }}
]

result = db.products.aggregate(pipeline)
for doc in result:
    print(doc)
            

Custom Functions

You can use custom JavaScript functions in the $function stage:

pipeline = [
    {"$project": {
        "item": 1,
        "customField": {
            "$function": {
                "body": "function(price) { return price * 1.1; }",
                "args": ["$price"],
                "lang": "js"
            }
        }
    }}
]

result = db.products.aggregate(pipeline)
for doc in result:
    print(doc)
            

Conclusion

In this tutorial, you have learned about advanced usage of the aggregation pipeline in MongoDB. The aggregation pipeline provides a flexible and powerful framework for data analysis and transformation, making it an essential tool for working with MongoDB data.