$avg accumulator in MongoDB - Time & Space Complexity

Time Complexity: $avg accumulator

O(n)

Understanding Time Complexity

When using the $avg accumulator in MongoDB, it's important to understand how the time to calculate the average changes as the amount of data grows.

We want to know how the work done by $avg scales when more documents are processed.

Scenario Under Consideration

Analyze the time complexity of the following MongoDB aggregation snippet.

db.sales.aggregate([
  { $group: { _id: "$store", averageSales: { $avg: "$amount" } } }
])
    

This groups sales by store and calculates the average sales amount for each store.

Identify Repeating Operations

Look at what repeats as the data grows.

• Primary operation: Summing the amount values for each document in a group.
• How many times: Once for each document in the input collection.

How Execution Grows With Input

As the number of documents increases, the amount of work to sum and count grows proportionally.

Input Size (n)	Approx. Operations
10	10 sums and counts
100	100 sums and counts
1000	1000 sums and counts

Pattern observation: The work grows in a straight line with the number of documents.

Final Time Complexity

Time Complexity: O(n)

This means the time to calculate the average grows directly with the number of documents processed.

Common Mistake

[X] Wrong: "Calculating an average is instant no matter how many documents there are."

[OK] Correct: Even though the average is just one number, MongoDB must look at every document's value to add them up and count them first.

Interview Connect

Understanding how aggregation stages like $avg scale helps you explain performance in real projects and shows you think about data size impact clearly.

Self-Check

What if we added a $match stage before $group to filter documents? How would that affect the time complexity?

Practice

1. What does the $avg accumulator do in a MongoDB aggregation pipeline?

easy

A. Calculates the average value of numeric fields in grouped documents

B. Counts the total number of documents in a collection

C. Finds the maximum value in a group of documents

D. Sorts documents in ascending order

Solution

1. Step 1: Understand the purpose of $avg

   The $avg accumulator is designed to calculate the mean (average) of numeric values within grouped documents in an aggregation pipeline.

2. Step 2: Compare with other accumulators

   Other accumulators like $sum, $max, and $count perform different tasks, so $avg specifically calculates averages.

3. Final Answer:

   Calculates the average value of numeric fields in grouped documents -> Option A

4. Quick Check:

   $avg = average calculation [OK]

Hint: Remember: $avg means average, not count or max [OK]

Common Mistakes:

• Confusing $avg with $sum or $count
• Thinking $avg sorts data
• Assuming $avg works outside aggregation

2. Which of the following is the correct syntax to calculate the average of the field score in a MongoDB aggregation $group stage?

easy

A. { $group: { _id: null, avgScore: { $average: "$score" } } }

B. { $group: { _id: null, avgScore: { $avg: "$score" } } }

C. { $group: { _id: null, avgScore: { avg: "$score" } } }

D. { $group: { _id: null, avgScore: { $sum: "$score" } } }

Solution

1. Step 1: Identify the correct accumulator name

   The correct MongoDB accumulator for average is $avg, not $average or avg.

2. Step 2: Check the syntax structure

   The syntax requires the field name as a string with a $ prefix inside $avg, and the whole inside a $group stage with an _id.

3. Final Answer:

   { $group: { _id: null, avgScore: { $avg: "$score" } } } -> Option B

4. Quick Check:

   Correct syntax uses $avg with field string [OK]

Hint: Use $avg exactly, with field as "$fieldName" [OK]

Common Mistakes:

• Using $average instead of $avg
• Omitting quotes around field name
• Using $sum instead of $avg

3. Given the collection students with documents:

{ "name": "Alice", "score": 80 }, { "name": "Bob", "score": 90 }, { "name": "Charlie", "score": 70 }

What will be the output of this aggregation?

[{ $group: { _id: null, averageScore: { $avg: "$score" } } }]

medium

A. [{ "_id": null, "averageScore": 240 }]

B. [{ "_id": null, "averageScore": 70 }]

C. [{ "_id": null, "averageScore": 80 }]

D. [{ "_id": null, "averageScore": 90 }]

Solution

1. Step 1: Calculate the sum of scores

   Sum = 80 + 90 + 70 = 240.

2. Step 2: Calculate the average

   Average = 240 / 3 = 80.

3. Final Answer:

   [{ "_id": null, "averageScore": 80 }] -> Option C

4. Quick Check:

   Average of 80, 90, 70 = 80 [OK]

Hint: Add all scores, divide by count for average [OK]

Common Mistakes:

• Using sum instead of average
• Picking minimum or maximum score
• Forgetting to divide by number of documents

4. Consider this aggregation pipeline:

[{ $group: { _id: "$class", avgScore: { $avg: score } } }]

What is the error in this pipeline?

medium

A. Cannot use $avg inside $group

B. Missing $match stage before $group

C. The _id field cannot be "$class"

D. Field name 'score' should be a string with $ prefix: "$score"

Solution

1. Step 1: Check the field reference inside $avg

   The field name must be a string with a $ prefix, so it should be "$score", not score.

2. Step 2: Validate other parts of the pipeline

   Using "$class" as _id is valid, and $avg is allowed inside $group. $match is optional.

3. Final Answer:

   Field name 'score' should be a string with $ prefix: "$score" -> Option D

4. Quick Check:

   Field names in $avg need "$field" format [OK]

Hint: Always quote field names with $ in accumulators [OK]

Common Mistakes:

• Using unquoted field names inside $avg
• Thinking $match is mandatory before $group
• Believing _id cannot be a field name

5. You have a collection sales with documents:

{ "region": "North", "amount": 100 }, { "region": "South", "amount": 200 }, { "region": "North", "amount": 300 }, { "region": "South", "amount": 400 }

Write an aggregation pipeline to find the average sales amount per region and identify the region with the highest average sales.

hard

A. [{ $group: { _id: "$region", avgAmount: { $avg: "$amount" } } }, { $sort: { avgAmount: -1 } }, { $limit: 1 }]

B. [{ $group: { _id: "$region", avgAmount: { $sum: "$amount" } } }, { $sort: { avgAmount: 1 } }]

C. [{ $group: { _id: "$region", avgAmount: { $avg: "$amount" } } }, { $match: { avgAmount: { $gt: 300 } } }]

D. [{ $group: { _id: "$region", avgAmount: { $avg: "$amount" } } }, { $sort: { avgAmount: 1 } }, { $limit: 1 }]

Solution

1. Step 1: Group by region and calculate average

   Use $group with _id: "$region" and $avg: "$amount" to get average sales per region.

2. Step 2: Sort descending by average and limit to top

   Sort by avgAmount descending (-1) to get highest average first, then limit to 1 document.

3. Final Answer:

   [{ $group: { _id: "$region", avgAmount: { $avg: "$amount" } } }, { $sort: { avgAmount: -1 } }, { $limit: 1 }] -> Option A

4. Quick Check:

   Group, sort descending, limit 1 = highest average [OK]

Hint: Group by region, avg, sort desc, limit 1 for highest [OK]

Common Mistakes:

• Using $sum instead of $avg for average
• Sorting ascending to get highest average
• Not limiting results to top 1