Advanced

Aggregation Pipeline

Transform and analyze data using MongoDB's powerful aggregation framework.

The Aggregation Pipeline

The aggregation pipeline processes documents through a series of stages. Each stage transforms the documents and passes results to the next stage.

Common Pipeline Stages

$match — filter documents (like WHERE)
$group — group documents and compute aggregates
$project — include/exclude/reshape fields (like SELECT)
$sort — sort documents
$limit — limit number of documents
$skip — skip documents
$lookup — join with another collection
$unwind — deconstruct an array field
$addFields — add computed fields
$count — count documents

Example

javascript

// Sales analytics aggregation
const result = await db.collection('orders').aggregate([
  // Stage 1: Filter
  { $match: {
    status: 'completed',
    createdAt: { $gte: new Date('2024-01-01') }
  }},

  // Stage 2: Group by category and compute totals
  { $group: {
    _id: '$category',
    totalRevenue: { $sum: '$total' },
    orderCount: { $count: {} },
    avgOrderValue: { $avg: '$total' },
    maxOrder: { $max: '$total' }
  }},

  // Stage 3: Add computed field
  { $addFields: {
    revenuePerOrder: { $divide: ['$totalRevenue', '$orderCount'] }
  }},

  // Stage 4: Sort by revenue descending
  { $sort: { totalRevenue: -1 } },

  // Stage 5: Take top 5
  { $limit: 5 },

  // Stage 6: Reshape output
  { $project: {
    category: '$_id',
    totalRevenue: { $round: ['$totalRevenue', 2] },
    orderCount: 1,
    avgOrderValue: { $round: ['$avgOrderValue', 2] },
    _id: 0
  }}
]).toArray();

// $lookup - join with users collection
const ordersWithUsers = await db.collection('orders').aggregate([
  { $lookup: {
    from: 'users',
    localField: 'userId',
    foreignField: '_id',
    as: 'user'
  }},
  { $unwind: '$user' },
  { $project: {
    orderTotal: '$total',
    userName: '$user.name',
    userEmail: '$user.email'
  }}
]).toArray();

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