Bucket pattern for time-series data in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a bucket field that groups timestamps by hour.

MongoDB

db.sensorData.aggregate([{ $addFields: { bucket: { $dateTrunc: { date: "$timestamp", unit: [1] } } } }])

Drag options to blanks, or click blank then click option'

Ahour

Bminute

Cday

Dsecond

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'minute' or 'second' will create too many small buckets.

Using 'day' groups data by whole days, not hours.

The $dateTrunc operator truncates the timestamp to the specified unit. Using "hour" groups data into hourly buckets.

2fill in blank

medium

Complete the code to group documents by the bucket field and count entries per bucket.

MongoDB

db.sensorData.aggregate([{ $group: { _id: "$[1]", count: { $sum: 1 } } }])

Drag options to blanks, or click blank then click option'

Atimestamp

BsensorId

Cbucket

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by 'timestamp' groups by exact time, not buckets.

Grouping by 'sensorId' groups by sensor, not time.

Grouping by the bucket field groups documents into time buckets, allowing counting entries per bucket.

3fill in blank

hard

Fix the error in the pipeline stage that filters documents within a specific bucket range.

MongoDB

db.sensorData.aggregate([{ $match: { bucket: { $gte: ISODate("2023-01-01T00:00:00Z"), [1]: ISODate("2023-01-01T23:59:59Z") } } }])

Drag options to blanks, or click blank then click option'

A$lte

B$lt

C$eq

D$ne

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt excludes documents exactly at the upper bound.

Using $eq or $ne does not specify a range.

To filter documents between two dates inclusive, use $gte and $lte operators.

4fill in blank

hard

Fill both blanks to create a bucket pattern that groups data by day and calculates average value.

MongoDB

db.sensorData.aggregate([{ $addFields: { bucket: { $dateTrunc: { date: "$timestamp", unit: [1] } } } }, { $group: { _id: "$bucket", avgValue: { $[2]: "$value" } } }])

Drag options to blanks, or click blank then click option'

Aday

Bhour

Cavg

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'hour' groups by hour, not day.

Using 'sum' adds values instead of averaging.

Grouping by day buckets groups data daily. Using avg calculates the average value per day.

5fill in blank

hard

Fill all three blanks to create a bucket pattern that groups data by month, counts entries, and filters buckets with more than 100 entries.

MongoDB

db.sensorData.aggregate([{ $addFields: { bucket: { $dateTrunc: { date: "$timestamp", unit: [1] } } } }, { $group: { _id: "$bucket", count: { $[2]: 1 } } }, { $match: { count: { $[3]: 100 } } }])

Drag options to blanks, or click blank then click option'

Amonth

Bsum

C$gt

Dday

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'day' instead of 'month' groups by day.

Using '$lt' instead of '$gt' filters wrong buckets.

Using 'avg' instead of 'sum' does not count entries.

Grouping by month buckets groups data monthly. Using sum counts entries. Filtering with $gt selects buckets with more than 100 entries.

Practice

1.

What is the main purpose of using the bucket pattern in MongoDB for time-series data?

easy

A. To group many time-series records into fewer documents for efficiency

B. To delete old time-series data automatically

C. To create indexes on every single time record

D. To convert time-series data into text format

Solution

1. Step 1: Understand the bucket pattern concept

   The bucket pattern groups multiple time-series records into fewer documents to reduce storage and improve query speed.

2. Step 2: Compare options with the bucket pattern purpose

   Only To group many time-series records into fewer documents for efficiency correctly describes grouping records into fewer documents for efficiency.

3. Final Answer:

   To group many time-series records into fewer documents for efficiency -> Option A

4. Quick Check:

   Bucket pattern groups records = A [OK]

Hint: Bucket pattern groups many records into fewer documents [OK]

Common Mistakes:

• Thinking bucket pattern deletes data automatically
• Confusing bucket pattern with indexing every record
• Believing bucket pattern changes data format

2.

Which of the following is the correct way to use $bucket to group documents by time ranges in MongoDB?

{ $bucket: { groupBy: "$timestamp", boundaries: [ISODate("2024-01-01"), ISODate("2024-02-01"), ISODate("2024-03-01")], default: "Other" } }

easy

A. Using $bucket without groupBy field

B. Using $bucket without boundaries array

C. Using $bucket with string boundaries instead of dates

D. Using $bucket with groupBy on timestamp and specifying boundaries as dates

Solution

1. Step 1: Check the required fields for $bucket

   The $bucket stage requires a groupBy field and a boundaries array to define bucket ranges.

2. Step 2: Validate the boundaries and groupBy usage

   Using $bucket with groupBy on timestamp and specifying boundaries as dates correctly uses groupBy on timestamp and boundaries as ISODate objects, which is valid for time-series bucketing.

3. Final Answer:

   Using $bucket with groupBy on timestamp and specifying boundaries as dates -> Option D

4. Quick Check:

   $bucket needs groupBy and boundaries = A [OK]

Hint: Always include groupBy and boundaries in $bucket stage [OK]

Common Mistakes:

• Omitting boundaries array causes error
• Using strings instead of date objects for time boundaries
• Missing groupBy field in $bucket stage

3.

Given the following aggregation pipeline using $bucket on a collection with timestamps:

[{ $bucket: { groupBy: "$timestamp", boundaries: [ISODate("2024-01-01"), ISODate("2024-02-01"), ISODate("2024-03-01")], default: "Other", output: { count: { $sum: 1 } } } }]

What will be the output if the collection has 5 documents with timestamps in January 2024 and 3 documents in March 2024?

medium

A. [{_id: ISODate("2024-01-01"), count: 5}, {_id: ISODate("2024-02-01"), count: 0}, {_id: "Other", count: 3}]

B. [{_id: ISODate("2024-01-01"), count: 5}, {_id: ISODate("2024-02-01"), count: 3}]

C. [{_id: ISODate("2024-01-01"), count: 8}]

D. [{_id: "Other", count: 8}]

Solution

1. Step 1: Understand bucket boundaries and grouping

   Documents with timestamps from 2024-01-01 to before 2024-02-01 go to first bucket; 2024-02-01 to before 2024-03-01 to second; others to default "Other".

2. Step 2: Assign documents to buckets based on timestamps

   5 docs in January fall in first bucket; 3 docs in March fall outside boundaries, so go to "Other"; second bucket has 0 docs.

3. Final Answer:

   [{_id: ISODate("2024-01-01"), count: 5}, {_id: ISODate("2024-02-01"), count: 0}, {_id: "Other", count: 3}] -> Option A

4. Quick Check:

   Docs in Jan = 5, in Other = 3, Feb bucket empty = C [OK]

Hint: Check which bucket each timestamp fits, others go to default [OK]

Common Mistakes:

• Ignoring the default bucket for out-of-bound timestamps
• Assuming March docs fall in February bucket
• Summing all docs into one bucket

4.

Consider this aggregation pipeline snippet using $bucket:

{ $bucket: { groupBy: "$time", boundaries: [ISODate("2024-01-01")], output: { count: { $sum: 1 } } } }

What is the error in this pipeline?

medium

A. The groupBy field name is incorrect; it should be 'timestamp'

B. The boundaries array is missing the upper boundary for the last bucket

C. The boundaries array must have at least two elements defining ranges

D. The output field 'count' is not allowed in $bucket stage

Solution

1. Step 1: Check boundaries array requirements

   The boundaries array must have at least two elements to define bucket ranges properly, including an upper boundary for the last bucket.

2. Step 2: Identify missing upper boundary

   The boundaries array has only one date, so it cannot define any bucket ranges, causing an error.

3. Final Answer:

   The boundaries array must have at least two elements defining ranges -> Option C

4. Quick Check:

   Boundaries must have at least 2 elements = A [OK]

Hint: Boundaries array must have one more element than buckets [OK]

Common Mistakes:

• Using boundaries array with fewer than two elements
• Assuming groupBy field name must be 'timestamp'
• Thinking output fields are not allowed in $bucket

5.

You want to store sensor readings every minute for a month using the bucket pattern. Each bucket should hold data for one hour. Which boundaries array correctly defines the buckets for January 1, 2024?

hard

A. [ISODate("2024-01-01T00:00:00Z"), ISODate("2024-01-01T00:59:59Z"), ISODate("2024-01-01T01:59:59Z")]

B. [ISODate("2024-01-01T00:00:00Z"), ISODate("2024-01-01T01:00:00Z"), ISODate("2024-01-01T02:00:00Z"), ..., ISODate("2024-01-01T23:00:00Z"), ISODate("2024-01-02T00:00:00Z")]

C. [ISODate("2024-01-01"), ISODate("2024-01-02")]

D. [ISODate("2024-01-01T00:00:00Z"), ISODate("2024-01-01T00:30:00Z"), ISODate("2024-01-01T01:00:00Z")]

Solution

1. Step 1: Understand bucket boundaries for hourly buckets

   Each bucket covers one hour, so boundaries must be every hour mark from midnight to next day midnight.

2. Step 2: Check options for correct hourly boundaries

   [ISODate("2024-01-01T00:00:00Z"), ISODate("2024-01-01T01:00:00Z"), ISODate("2024-01-01T02:00:00Z"), ..., ISODate("2024-01-01T23:00:00Z"), ISODate("2024-01-02T00:00:00Z")] lists hourly ISODate boundaries from 00:00 to 24:00, correctly defining 24 one-hour buckets.

3. Final Answer:

   [ISODate("2024-01-01T00:00:00Z"), ISODate("2024-01-01T01:00:00Z"), ISODate("2024-01-01T02:00:00Z"), ..., ISODate("2024-01-01T23:00:00Z"), ISODate("2024-01-02T00:00:00Z")] -> Option B

4. Quick Check:

   Hourly boundaries every hour for 24 hours = D [OK]

Hint: Boundaries must mark start of each hour plus next day start [OK]

Common Mistakes:

• Using half-hour or irregular intervals for hourly buckets
• Using only start and end of day as boundaries
• Using end times like 00:59:59 instead of next hour start