Time-series collections in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a time-series collection named 'sensorData'.

MongoDB

db.createCollection('sensorData', { [1]: { timeField: 'timestamp', metaField: 'deviceId', granularity: 'seconds' } })

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

Atimeseries

BtimeSeries

Ctime_series

Dtime-series

Attempts:

3 left

💡 Hint

Common Mistakes

Using camelCase like 'timeSeries' instead of 'timeseries'.

Adding underscores or hyphens in the option name.

The correct option is timeseries. This is the exact option name MongoDB expects to create a time-series collection.

2fill in blank

medium

Complete the code to insert a document with a timestamp and metadata into the 'sensorData' collection.

MongoDB

db.sensorData.insertOne({ timestamp: new Date(), [1]: 'device123', temperature: 22.5 })

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

Ameta

BmetaField

Cmetadata

DdeviceId

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'meta' or 'metadata' instead of the exact metaField name.

Using 'metaField' as a field name in the document.

The metadata field is named deviceId as defined in the collection options. This field stores device identification info.

3fill in blank

hard

Fix the error in the query to find documents where temperature is greater than 20.

MongoDB

db.sensorData.find({ temperature: { [1]: 20 } })

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

A$lt

B$gte

C$gt

D$lte

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt or $lte which mean less than.

Using $gte which means greater than or equal, not strictly greater.

The operator $gt means 'greater than'. It correctly filters documents with temperature above 20.

4fill in blank

hard

Fill both blanks to create an aggregation pipeline that groups data by deviceId and calculates average temperature.

MongoDB

db.sensorData.aggregate([ { $group: { _id: '[1]', avgTemp: { $avg: '[2]' } } } ])

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

AdeviceId

Btemperature

Ctimestamp

Dmeta

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by timestamp instead of deviceId.

Averaging the wrong field like timestamp or meta.

The _id field groups by deviceId. The average is calculated on the temperature field.

5fill in blank

hard

Fill all three blanks to create a time-series collection with a custom granularity and insert a sample document.

MongoDB

db.createCollection('weatherData', { [1]: { timeField: '[2]', metaField: '[3]', granularity: 'minutes' } })

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

Atimeseries

BrecordedAt

Clocation

DtimeSeries

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'timeSeries' instead of 'timeseries' for the option name.

Mixing up the timeField and metaField names.

The collection option is timeseries. The time field is recordedAt and the metadata field is location.

Practice

1. What is the main purpose of a time-series collection in MongoDB?

easy

A. To manage user authentication and roles

B. To store large binary files like images and videos

C. To efficiently store and query data that changes over time

D. To create backups of the database automatically

Solution

1. Step 1: Understand time-series data

   Time-series data is data recorded over time, like temperature or stock prices.

2. Step 2: Identify MongoDB time-series collection use

   MongoDB time-series collections optimize storage and queries for such time-based data.

3. Final Answer:

   To efficiently store and query data that changes over time -> Option C

4. Quick Check:

   Time-series collections = efficient time-based data storage [OK]

Hint: Time-series collections are for data changing over time [OK]

Common Mistakes:

• Confusing time-series collections with file storage
• Thinking time-series collections manage users
• Assuming they handle backups automatically

2. Which of the following is required when creating a time-series collection in MongoDB?

easy

A. A field that stores binary data

B. A unique index on the _id field

C. A field that stores user roles

D. A field that stores the timestamp of each record

Solution

1. Step 1: Recall time-series collection requirements

   MongoDB requires a time field to track when each data point was recorded.

2. Step 2: Check other options

   Unique _id index exists by default but is not specific to time-series; user roles and binary data fields are unrelated.

3. Final Answer:

   A field that stores the timestamp of each record -> Option D

4. Quick Check:

   Time field required = A field that stores the timestamp of each record [OK]

Hint: Time field is mandatory for time-series collections [OK]

Common Mistakes:

• Choosing unrelated fields like user roles
• Confusing default indexes with required time field
• Thinking binary data field is required

3. Given the following command, what will be the result?

db.createCollection("sensorData", {
  timeseries: {
    timeField: "timestamp",
    metaField: "deviceId"
  }
})

medium

A. Creates a time-series collection with timestamp and deviceId fields

B. Throws a syntax error due to missing options

C. Creates a regular collection named sensorData

D. Creates a capped collection instead of time-series

Solution

1. Step 1: Analyze the createCollection command

   The command uses the timeseries option with timeField and metaField specified.

2. Step 2: Understand MongoDB behavior

   This creates a time-series collection named sensorData with timestamp as time and deviceId as metadata.

3. Final Answer:

   Creates a time-series collection with timestamp and deviceId fields -> Option A

4. Quick Check:

   timeseries option creates time-series collection [OK]

Hint: timeseries option creates time-series collection [OK]

Common Mistakes:

• Thinking it creates a regular or capped collection
• Assuming syntax error due to missing options
• Confusing metaField with required timeField

4. You tried to create a time-series collection but got an error. Which of the following is the most likely cause?

db.createCollection("temps", {
  timeseries: {
    metaField: "location"
  }
})

medium

A. Missing required timeField in timeseries options

B. metaField cannot be used in time-series collections

C. Collection name is invalid

D. timeseries option is misspelled

Solution

1. Step 1: Check timeseries options in the command

   The command specifies metaField but omits the required timeField.

2. Step 2: Recall MongoDB requirements

   timeField is mandatory for time-series collections; missing it causes an error.

3. Final Answer:

   Missing required timeField in timeseries options -> Option A

4. Quick Check:

   timeField missing = error [OK]

Hint: Always include timeField when creating time-series collections [OK]

Common Mistakes:

• Thinking metaField is not allowed
• Assuming collection name is invalid
• Believing timeseries option is misspelled

5. You have a time-series collection with fields timestamp (timeField) and sensorId (metaField). You want to query the average temperature for sensorId 'S1' over the last 24 hours. Which aggregation pipeline is correct?

db.sensorData.aggregate([
  { $match: { sensorId: 'S1', timestamp: { $gte: new Date(Date.now() - 86400000) } } },
  { $group: { _id: null, avgTemp: { $avg: "$temperature" } } }
])

hard

A. Groups by sensorId instead of null

B. Correctly filters by sensorId and timestamp, then averages temperature

C. Uses $sort instead of $match for filtering

D. Filters only by timestamp, ignoring sensorId

Solution

1. Step 1: Analyze the $match stage

   The pipeline filters documents where sensorId is 'S1' and timestamp is within last 24 hours.

2. Step 2: Analyze the $group stage

   It groups all matched documents (_id: null) and calculates average temperature.

3. Final Answer:

   Correctly filters by sensorId and timestamp, then averages temperature -> Option B

4. Quick Check:

   Filter by sensorId and time, then average = Correctly filters by sensorId and timestamp, then averages temperature [OK]

Hint: Match filters, group aggregates for averages [OK]

Common Mistakes:

• Forgetting to filter by sensorId
• Using $sort instead of $match for filtering
• Grouping by sensorId when only one sensor is queried