Learning MongoDB Aggregations in 10 Minutes
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This document provides a quick guide to MongoDB's aggregation framework, explaining what aggregations are and why they are useful for data analysis and reporting. It describes the aggregation pipeline, key stages like $match, $group, $sort, $project, and $limit, along with example queries for practical understanding. The document concludes with performance tips and resources for further learning.
![Aggregation Pipeline Overview
Aggregation Pipeline:
A multi-stage pipeline that transforms
documents.
Each stage performs an operation and
passes results to the next stage.
Structure:
[{ stage1 }, { stage2 }, ... { stageN }]
Stages are executed sequentially.](https://image.slidesharecdn.com/mongodbaggregationsin10minutes-240916214119-8a06092c/85/Learning-MongoDB-Aggregations-in-10-Minutes-4-320.jpg)
![Stage Example 1: $match and $group
$match and $group Example:
db.sales.aggregate([
{ $match: { status: 'completed' } },
{ $group: { _id: '$product', total: { $sum: '$amount' } } }
])
Explanation:
Filters documents where status is 'completed'.
Groups by product and sums up the amount.](https://image.slidesharecdn.com/mongodbaggregationsin10minutes-240916214119-8a06092c/85/Learning-MongoDB-Aggregations-in-10-Minutes-7-320.jpg)
![Stage Example 2: $project and $sort
$project and $sort Example:
db.sales.aggregate([
{ $project: { product: 1, amount: 1, profit: { $subtract: ['$revenue', '$cost'] } } },
{ $sort: { profit: -1 } }
])
Explanation:
Projects product, amount, and calculates profit.
Sorts documents by profit in descending order.](https://image.slidesharecdn.com/mongodbaggregationsin10minutes-240916214119-8a06092c/85/Learning-MongoDB-Aggregations-in-10-Minutes-8-320.jpg)
![Real-World Use Case: Monthly Sales Report
Problem:
Generate a monthly sales report showing total sales and average sale amount for each category.
Solution:
db.sales.aggregate([
{ $match: { date: { $gte: new ISODate('2023-01-01'), $lt: new ISODate('2023-02-01') } } },
{ $group: { _id: '$category', totalSales: { $sum: '$amount' }, avgSale: { $avg: '$amount' } } }
])
Explanation:
Filters sales for January 2023.
Groups by category and calculates total and average sales.](https://image.slidesharecdn.com/mongodbaggregationsin10minutes-240916214119-8a06092c/85/Learning-MongoDB-Aggregations-in-10-Minutes-9-320.jpg)
Learning MongoDB Aggregations in 10 Minutes
- 1. Joe Nyirenda MongoDB Aggregations in 10 Minutes A Quick Guide to Mastering MongoDB’s Aggregation Framework
- 2. Agenda Introduction to Aggregations Aggregation Pipeline Common Stages in Aggregation Example Queries Use Cases for Aggregations Conclusion
- 3. Introduction to MongoDB Aggregations What are Aggregations? A way to process data and return computed results. Similar to SQL’s GROUP BY or SUM but more powerful. Why use Aggregations? Useful for data analysis, reporting, and transforming documents.
- 4. Aggregation Pipeline Overview Aggregation Pipeline: A multi-stage pipeline that transforms documents. Each stage performs an operation and passes results to the next stage. Structure: [{ stage1 }, { stage2 }, ... { stageN }] Stages are executed sequentially.
- 5. Discrete Processing Stages ● Stages can be streaming or blocking; favour the usage of streaming stages ● The order of stages matters, apply best practices ● Stages are easier to debug and maintain
- 6. Common Aggregation Stages Key Aggregation Stages: 1. $match – Filters documents based on conditions. 2. $group – Groups documents by a field and applies aggregate functions. 3. $sort – Sorts documents by a field. 4. $project – Reshapes documents, including/excluding fields. 5. $limit – Limits the number of documents returned.
- 7. Stage Example 1: $match and $group $match and $group Example: db.sales.aggregate([ { $match: { status: 'completed' } }, { $group: { _id: '$product', total: { $sum: '$amount' } } } ]) Explanation: Filters documents where status is 'completed'. Groups by product and sums up the amount.
- 8. Stage Example 2: $project and $sort $project and $sort Example: db.sales.aggregate([ { $project: { product: 1, amount: 1, profit: { $subtract: ['$revenue', '$cost'] } } }, { $sort: { profit: -1 } } ]) Explanation: Projects product, amount, and calculates profit. Sorts documents by profit in descending order.
- 9. Real-World Use Case: Monthly Sales Report Problem: Generate a monthly sales report showing total sales and average sale amount for each category. Solution: db.sales.aggregate([ { $match: { date: { $gte: new ISODate('2023-01-01'), $lt: new ISODate('2023-02-01') } } }, { $group: { _id: '$category', totalSales: { $sum: '$amount' }, avgSale: { $avg: '$amount' } } } ]) Explanation: Filters sales for January 2023. Groups by category and calculates total and average sales.
- 10. Aggregation Performance Tips Performance Tips: Use indexes effectively with $match and $sort. Minimize the number of documents passed between stages. Use $limit early in the pipeline to reduce processing time.
- 11. Conclusion and Further Learning Conclusion Aggregations in MongoDB provide powerful data transformation tools. Pipelines combine multiple stages for complex queries. Practice with real datasets to master these concepts. Further Reading: MongoDB Documentation on Aggregations MongoDB University Courses