MongoDB Aggregate Pipeline

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Aggregation Pipeline

The aggregation pipeline is a framework for data aggregation modelled on the concept of data processing pipelines. What this means, is documents enter a multi-stage pipeline that transforms the documents into aggregated results.

This is similar to using GROUP BY in SQL, where you might aggregate the average grades of all students taking a module.

The MongoDB aggregation pipeline consists of stages and each stage transforms the documents as they pass through the pipeline. A stage can generate new documents or filter out documents. A stage can also appear several times in the pipeline.

The syntax is:

db.collectionName.aggregate( [ { <stage> }, ... ] )

The pipeline for instance, could:

• project out certain details from each document, such as the employees;
• group the projected details by a certain fields and then using an aggregate function, such as group by the deptno and then counting the number of occurrences;
• sorting the results in order;
• limiting the results to a certain number, such as the first 10;

These are represented by the following operators: $project,$group, $sort or $limit.

A number of operations exist for the aggregation pipeline, details of which can be found in the MongoDB manual:

https://docs.mongodb.com/manual/reference/operator/aggregation/

Other Functions

Count

The power of the aggregation pipeline is to do processing on the data.

Lets count how many employees are in department 10:

db.emp.count({deptno: 10})

You can also add count() to a find query to count the records returned, instead of listing them:

db.dept.find({dname:"SALES"}).count()

Distinct

Sometimes you want to find the distinct values for a specified column (similar to distinct in SQL):

db.emp.distinct("deptno")

Next Step

Updating the collection