Pipeline mental model (stages flow) in MongoDB - Step-by-Step Execution

Concept Flow - Pipeline mental model (stages flow)

Input Documents

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Stage 1: $match

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Stage 2: $group

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Stage 3: $sort

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Stage 4: $project

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Output Documents

Documents flow through each pipeline stage in order, transforming data step-by-step until the final output is produced.

Execution Sample

MongoDB

db.sales.aggregate([
  { $match: { status: "A" } },
  { $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
  { $sort: { total: -1 } },
  { $project: { total: 1, _id: 0 } }
])

This pipeline filters sales with status 'A', groups by customer ID summing amounts, sorts by total descending, then projects only the total.

Execution Table

Step	Stage	Input Documents	Action	Output Documents
1	$match	[{cust_id:1, status:'A', amount:100}, {cust_id:2, status:'B', amount:200}, {cust_id:1, status:'A', amount:50}]	Filter documents where status = 'A'	[{cust_id:1, status:'A', amount:100}, {cust_id:1, status:'A', amount:50}]
2	$group	[{cust_id:1, status:'A', amount:100}, {cust_id:1, status:'A', amount:50}]	Group by cust_id and sum amounts	[{_id:1, total:150}]
3	$sort	[{_id:1, total:150}]	Sort by total descending (only one document, no change)	[{_id:1, total:150}]
4	$project	[{_id:1, total:150}]	Include only total field, exclude _id	[{total:150}]
5	End	[{total:150}]	Pipeline complete, output final documents	[{total:150}]

💡 All stages processed, pipeline ends with final transformed documents.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
documents	[{cust_id:1, status:'A', amount:100}, {cust_id:2, status:'B', amount:200}, {cust_id:1, status:'A', amount:50}]	[{cust_id:1, status:'A', amount:100}, {cust_id:1, status:'A', amount:50}]	[{_id:1, total:150}]	[{_id:1, total:150}]	[{total:150}]	[{total:150}]

Key Moments - 3 Insights

Why does the $match stage reduce the number of documents?

Because $match filters documents based on a condition (status = 'A'), only documents meeting this condition pass to the next stage, as shown in execution_table row 1.

How does $group change the shape of documents?

$group aggregates documents by a key (cust_id) and computes sums, producing new documents with _id and total fields, replacing original fields (see execution_table row 2).

Why does $project exclude the _id field in the final output?

$project controls which fields appear in output documents; setting _id:0 removes it, so only total remains, as shown in execution_table row 4.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the total amount for cust_id 1?

A 150

B 100

C 50

D 200

💡 Hint

Check the output documents column at step 2 in the execution_table.

At which step does the number of documents reduce from 3 to 2?

A Step 3 ($sort)

B Step 2 ($group)

C Step 1 ($match)

D Step 4 ($project)

💡 Hint

Look at the Input and Output Documents columns in execution_table rows 1 and 2.

If we remove the $project stage, what would the final output documents include?

A Only total field

B Both _id and total fields

C Only _id field

D No fields

💡 Hint

Compare output documents at steps 4 and 5 in execution_table.

Concept Snapshot

MongoDB aggregation pipeline processes documents through ordered stages.
Each stage transforms documents (filter, group, sort, project).
Documents flow from input to output step-by-step.
Stages like $match filter, $group aggregates, $sort orders, $project shapes output.
Final output is the result after all stages run.

Full Transcript

The MongoDB aggregation pipeline works by passing documents through a series of stages. Each stage takes the documents from the previous stage, applies a transformation, and passes the results to the next stage. For example, $match filters documents by a condition, reducing the number of documents. Then $group aggregates documents by a key and computes sums, changing the document structure. $sort orders the documents, and $project selects which fields to keep in the output. This flow continues until all stages are processed, producing the final output documents. The execution table shows each step's input, action, and output, helping visualize how documents change through the pipeline.