Time-series collections in MongoDB - Step-by-Step Execution

Concept Flow - Time-series collections

Create time-series collection

↓

Insert data with timestamp

↓

MongoDB stores data optimized

↓

Query data by time range

↓

Return matching time-series documents

↓

End

This flow shows creating a time-series collection, inserting timestamped data, storing it efficiently, querying by time, and returning results.

Execution Sample

MongoDB

db.createCollection("temps", {
  timeseries: { timeField: "timestamp" }
});
db.temps.insertMany([
  { timestamp: ISODate("2024-06-01T10:00:00Z"), temp: 22 },
  { timestamp: ISODate("2024-06-01T11:00:00Z"), temp: 23 }
]);
db.temps.find({ timestamp: { $gte: ISODate("2024-06-01T10:30:00Z") } })

Creates a time-series collection, inserts two temperature records with timestamps, then queries for records after 10:30.

Execution Table

Step	Action	Input/Condition	Result/Output
1	Create collection	timeseries with timeField 'timestamp'	Collection 'temps' created as time-series
2	Insert document	{timestamp: 2024-06-01T10:00:00Z, temp: 22}	Document stored in 'temps'
3	Insert document	{timestamp: 2024-06-01T11:00:00Z, temp: 23}	Document stored in 'temps'
4	Query documents	timestamp >= 2024-06-01T10:30:00Z	Returns document with timestamp 11:00
5	Query documents	timestamp >= 2024-06-01T10:30:00Z	Does not return document with timestamp 10:00
6	End	No more steps	Query complete, results returned

💡 Query ends after returning all documents matching the time condition

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
temps collection	empty	1 document (10:00)	2 documents (10:00, 11:00)	2 documents (10:00, 11:00)	2 documents (10:00, 11:00)
query result	none	none	none	1 document (11:00)	1 document (11:00)

Key Moments - 2 Insights

Why does the query only return the document with timestamp 11:00 and not 10:00?

Because the query condition is timestamp >= 2024-06-01T10:30:00Z, so only documents with timestamp at or after 10:30 are returned (see execution_table rows 4 and 5).

What is special about creating a time-series collection compared to a normal collection?

A time-series collection is optimized to store and query data with a timeField, improving performance for time-based queries (see execution_table row 1).

Visual Quiz - 3 Questions

Look at the execution_table, what is the state of the 'temps collection' after step 3?

A 2 documents with timestamps 10:00 and 11:00

B 1 document with timestamp 10:00

C Empty collection

D 3 documents with timestamps 10:00, 11:00, and 12:00

💡 Hint

Check variable_tracker row for 'temps collection' after Step 3

At which step does the query return documents matching the time condition?

A Step 2

B Step 4

C Step 1

D Step 3

💡 Hint

Look at execution_table rows describing query actions and results

If the query condition changed to timestamp >= 2024-06-01T09:00:00Z, what would happen?

A No documents would be returned

B Only the document with timestamp 11:00 would be returned

C Both documents with timestamps 10:00 and 11:00 would be returned

D Only the document with timestamp 10:00 would be returned

💡 Hint

Consider the timestamps of inserted documents and the new query condition

Concept Snapshot

Time-series collections store data with a timestamp field.
Create with db.createCollection and specify timeseries and timeField.
Insert documents with timestamps.
Query by time ranges efficiently.
MongoDB optimizes storage and queries for time-series data.

Full Transcript

This visual execution trace shows how to create a MongoDB time-series collection named 'temps' with a timeField called 'timestamp'. Two documents with timestamps 10:00 and 11:00 are inserted. A query is run to find documents with timestamp greater or equal to 10:30. The query returns only the document with timestamp 11:00 because the other document is before 10:30. The variable tracker shows the collection state after each step and the query result. Key moments clarify why only some documents match the query and what makes time-series collections special. The quiz tests understanding of collection state and query results at different steps.

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