MongoDB Academy · Lesson

$group: Aggregating and Computing Totals

Learners will group documents by a key and compute sums, averages, counts, and other accumulators with $group.

$group: Aggregating and Computing Totals is a free MongoDB Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MongoDB Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

What Does $group Do?

The $group stage collapses multiple input documents into fewer output documents based on a grouping key. Documents that share the same key value are merged into a single output document, and accumulator operators compute aggregate values for each group. Think of it as MongoDB's equivalent of SQL's GROUP BY clause combined with aggregate functions like SUM and COUNT.

// Count orders per user
db.orders.aggregate([
  { $group: {
    _id: '$userId',         // group by userId
    orderCount: { $sum: 1 }  // count each document as 1
  }}
]);
// Output: one doc per unique userId with their order count

The _id Field in $group

Every $group stage requires an _id field that defines the grouping key. The _id can be a field reference ('$field'), a computed expression, an object with multiple fields (for compound grouping), or null (to aggregate the entire collection into one document). The _id value in the output is the group key.

// Group by single field
{ $group: { _id: '$status' } }

// Group by multiple fields (compound key)
{ $group: { _id: { year: { $year: '$createdAt' }, status: '$status' } } }

// Group all documents into one (grand total)
{ $group: { _id: null, grandTotal: { $sum: '$amount' } } }

// Group by computed expression
{ $group: { _id: { $toLower: '$category' } } }

$sum: Counting and Summing

$sum is the most used accumulator. Pass 1 to count documents in each group, or a field reference to sum numeric field values. You can also pass an expression that evaluates to a number. $sum ignores non-numeric values and missing fields (they count as zero), making it safe to use on optional numeric fields.

db.sales.aggregate([{
  $group: {
    _id: '$region',
    // Count documents in each group
    transactionCount: { $sum: 1 },
    // Sum the 'amount' field
    totalRevenue: { $sum: '$amount' },
    // Sum a computed expression
    totalWithTax: { $sum: { $multiply: ['$amount', 1.1] } }
  }
}]);

$avg, $min, $max

$avg computes the arithmetic mean, $min finds the smallest value, and $max finds the largest value across all documents in a group. All three work on numeric values and also on strings and dates (for min/max). They ignore null and missing field values.

db.orders.aggregate([{
  $group: {
    _id: '$productId',
    avgRating: { $avg: '$rating' },          // average rating
    minPrice: { $min: '$price' },            // lowest price ever sold
    maxPrice: { $max: '$price' },            // highest price ever sold
    firstOrder: { $min: '$createdAt' },      // earliest date
    latestOrder: { $max: '$createdAt' }      // most recent date
  }
}]);

$count in $group

While you typically count by using { $sum: 1 } in a $group stage, MongoDB also provides a standalone $count stage to count the total number of documents in the stream. The $count stage emits a single document with the count under the field name you specify. It's equivalent to a $group with _id: null and a $sum: 1.

// Count total published articles
db.articles.aggregate([
  { $match: { status: 'published' } },
  { $count: 'totalPublished' }  // emits { totalPublished: N }
]);

// Equivalent using $group:
db.articles.aggregate([
  { $match: { status: 'published' } },
  { $group: { _id: null, totalPublished: { $sum: 1 } } }
]);

Compound Grouping Keys

To group by multiple fields simultaneously, pass an object as the _id value. Each key in the object becomes part of the compound group key. The output documents have a nested _id object. This is the standard way to produce multi-dimensional aggregations like 'revenue by region and month'.

// Revenue breakdown by region AND year-month
db.sales.aggregate([
  { $group: {
    _id: {
      region: '$region',
      year: { $year: '$saleDate' },
      month: { $month: '$saleDate' }
    },
    revenue: { $sum: '$amount' },
    count: { $sum: 1 }
  }},
  { $sort: { '_id.year': 1, '_id.month': 1, '_id.region': 1 } }
]);
// Output: { _id: { region: 'EMEA', year: 2024, month: 3 }, revenue: 50000, count: 120 }

Accumulating Into Arrays With $push

The $push accumulator collects all values from a field across the grouped documents into an array. This is useful for gathering all order IDs under a user, all tags under a category, or all user IDs who purchased a product. The result array can contain duplicates; use $addToSet instead to collect unique values.

// Collect all order IDs per user
db.orders.aggregate([{
  $group: {
    _id: '$userId',
    orderIds: { $push: '$_id' },         // all order IDs for this user
    amounts: { $push: '$amount' },       // all amounts
    // Collect whole sub-documents
    orderSummaries: {
      $push: { orderId: '$_id', amount: '$amount', status: '$status' }
    }
  }
}]);

$first and $last Accumulators

$first and $last return the first and last field value encountered per group, in the order documents are processed. Because MongoDB does not guarantee document order unless you sort first, you should add a $sort stage before the $group stage to make $first/$last meaningful—for example, 'first order date per customer'.

// Most and least recent order per customer
db.orders.aggregate([
  { $sort: { createdAt: 1 } },  // sort BEFORE group for meaningful first/last
  { $group: {
    _id: '$customerId',
    firstOrder: { $first: '$createdAt' },
    firstOrderId: { $first: '$_id' },
    lastOrder: { $last: '$createdAt' },
    lastAmount: { $last: '$amount' }
  }}
]);

Grouping on Computed Expressions

The $group _id can be any expression, not just a field reference. You can group by a truncated date (year-week), a computed category, a mathematical bucket, or a substring of a field. This lets you create flexible analytical groupings without requiring pre-computed bucket fields in your stored documents.

// Group orders by price bucket: 0-99, 100-499, 500+
db.orders.aggregate([{
  $group: {
    _id: {
      $switch: {
        branches: [
          { case: { $lt: ['$amount', 100] }, then: 'small' },
          { case: { $lt: ['$amount', 500] }, then: 'medium' }
        ],
        default: 'large'
      }
    },
    count: { $sum: 1 },
    totalRevenue: { $sum: '$amount' }
  }
}]);

Two-Stage Grouping

Complex analytics often require two $group stages in sequence: the first groups at a detailed level (e.g., by user), and the second groups the first stage's results at a higher level (e.g., by region). This two-stage pattern avoids nested $group expressions and produces cleaner, more understandable pipelines.

// Average orders per user, per region
db.orders.aggregate([
  // Stage 1: sum per user
  { $group: {
    _id: { userId: '$userId', region: '$region' },
    userOrderCount: { $sum: 1 }
  }},
  // Stage 2: average of those sums, per region
  { $group: {
    _id: '$_id.region',
    avgOrdersPerUser: { $avg: '$userOrderCount' },
    uniqueUsers: { $sum: 1 }
  }}
]);

$group Performance: No Index Available

Unlike $match, the $group stage cannot use an index—it must process all documents passed to it. This is why reducing the input with an early $match is critical. For very large datasets where $group uses too much memory, set allowDiskUse: true to spill to disk. Alternatively, pre-computing and storing aggregated values (the Computed Pattern) avoids runtime grouping on hot paths.

// Allow disk use for large aggregations
db.orders.aggregate(
  [
    { $match: { year: 2024 } },
    { $group: { _id: '$productId', revenue: { $sum: '$amount' } } },
    { $sort: { revenue: -1 } }
  ],
  { allowDiskUse: true }  // spill to disk if memory limit exceeded
);

Quick Check

Test your understanding of the $group stage in the aggregation pipeline.

Lesson Recap

In this lesson you learned: $group collapses documents by a key defined in the _id field, accumulators like $sum, $avg, $min, $max, $push compute aggregate values per group, and two-stage grouping enables hierarchical analytics. Next up we explore $sort, $limit, and $skip to order and paginate aggregation results.

Frequently asked questions

Is the “$group: Aggregating and Computing Totals” lesson free?

Yes — the full text of “$group: Aggregating and Computing Totals” is free to read here on the web, and the MongoDB Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MongoDB Academy course, upgrade to CoddyKit PRO.

What will I learn in “$group: Aggregating and Computing Totals”?

Learners will group documents by a key and compute sums, averages, counts, and other accumulators with $group. You practise MongoDB Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start MongoDB Academy?

No prior experience is required. MongoDB Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “$group: Aggregating and Computing Totals” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this MongoDB Academy lesson?

Yes. Every MongoDB Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

Pipeline Concepts: Stages, Operators, and Expressions
$match and $project: Filter and Reshape
$group: Aggregating and Computing Totals
$sort, $limit, and $skip in the Pipeline

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