MongoDB Query to Group Documents by Date

Use the MongoDB aggregation pipeline with $group and extract the date part using $dateToString, like { $group: { _id: { $dateToString: { format: "%Y-%m-%d", date: "$date" } }, count: { $sum: 1 } } } to group documents by date.

Examples

Input[{ "date": ISODate("2024-06-01T10:00:00Z") }, { "date": ISODate("2024-06-01T15:00:00Z") }, { "date": ISODate("2024-06-02T09:00:00Z") }]

Output[{ "_id": "2024-06-01", "count": 2 }, { "_id": "2024-06-02", "count": 1 }]

Input[{ "date": ISODate("2024-06-03T00:00:00Z") }, { "date": ISODate("2024-06-03T23:59:59Z") }]

Output[{ "_id": "2024-06-03", "count": 2 }]

Input[]

Output[]

How to Think About It

To group documents by date, first extract only the date part (year, month, day) from the full timestamp. Then use $group to collect documents sharing the same date. This way, you count or aggregate data per day, ignoring time details.

Algorithm

1

Get the collection of documents with date fields.

2

Use an aggregation pipeline to process documents.

3

Extract the date part (year-month-day) from each document's date field.

4

Group documents by this extracted date.

5

Calculate the count or other aggregates for each date group.

6

Return the grouped results.

Code

mongodb

db.collection.aggregate([
  {
    $group: {
      _id: { $dateToString: { format: "%Y-%m-%d", date: "$date" } },
      count: { $sum: 1 }
    }
  }
])

Output

[
  { "_id": "2024-06-01", "count": 2 },
  { "_id": "2024-06-02", "count": 1 }
]

Dry Run

Let's trace grouping three documents by their date field.

1

Extract date string

Convert ISODate("2024-06-01T10:00:00Z") to "2024-06-01"

2

Group by date string

Group documents with _id "2024-06-01" and count how many fall into this group

3

Output grouped counts

Return array with counts per date string

Date String	Count
2024-06-01	2
2024-06-02	1

Why This Works

Step 1: Extract date part

The $dateToString operator formats the full date to just year-month-day, removing time.

Step 2: Group documents

The $group stage groups documents by the extracted date string as the key.

Step 3: Count documents

The $sum: 1 counts how many documents belong to each date group.

Alternative Approaches

Group by year, month, day fields separately

mongodb

db.collection.aggregate([
  {
    $group: {
      _id: {
        year: { $year: "$date" },
        month: { $month: "$date" },
        day: { $dayOfMonth: "$date" }
      },
      count: { $sum: 1 }
    }
  }
])

This groups by separate fields instead of a formatted string, useful if you want to keep date parts separate.

Use $project to add a date string field first

mongodb

db.collection.aggregate([
  {
    $project: {
      dateOnly: { $dateToString: { format: "%Y-%m-%d", date: "$date" } }
    }
  },
  {
    $group: {
      _id: "$dateOnly",
      count: { $sum: 1 }
    }
  }
])

This separates date extraction and grouping into two stages, which can improve readability.

Complexity: O(n) time, O(k) space

Time Complexity

The aggregation scans all n documents once, grouping them by date, so time is O(n).

Space Complexity

Space depends on k, the number of unique dates, to store grouped results.

Which Approach is Fastest?

Using $dateToString in $group is concise and efficient; separating with $project may be clearer but slightly slower.

Approach	Time	Space	Best For
$group with $dateToString	O(n)	O(k)	Simple date grouping
Group by year, month, day fields	O(n)	O(k)	When separate date parts needed
$project then $group	O(n)	O(k)	Readability and stepwise processing