Bucket pattern for time-series data in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Output of bucket aggregation for hourly time-series data

Given a MongoDB collection sensor_readings with documents containing timestamp and value, what is the output of the following aggregation pipeline that buckets data by hour?

MongoDB

db.sensor_readings.aggregate([
  {
    $bucket: {
      groupBy: { $hour: "$timestamp" },
      boundaries: [0, 6, 12, 18, 24],
      default: "Other",
      output: {
        count: { $sum: 1 },
        avgValue: { $avg: "$value" }
      }
    }
  }
])

A [ { _id: 0, count: 5, avgValue: 20 }, { _id: 6, count: 3, avgValue: 15 }, { _id: 12, count: 7, avgValue: 30 }, { _id: 18, count: 2, avgValue: 25 } ]

B [ { _id: 0, count: 5, avgValue: 20 }, { _id: 6, count: 3, avgValue: 15 }, { _id: 12, count: 7, avgValue: 30 } ]

C [ { _id: 0, count: 5, avgValue: 20 }, { _id: 6, count: 3, avgValue: 15 }, { _id: 12, count: 7, avgValue: 30 }, { _id: 18, count: 2, avgValue: 25 }, { _id: "Other", count: 1, avgValue: 10 } ]

D [ { _id: 0, count: 5, avgValue: 20 }, { _id: 6, count: 3, avgValue: 15 }, { _id: 12, count: 7, avgValue: 30 }, { _id: 18, count: 2, avgValue: 25 }, { _id: 24, count: 1, avgValue: 10 } ]

💡 Hint

Remember that the default bucket collects documents with groupBy values outside the specified boundaries.

The $bucket stage groups documents by the hour extracted from timestamp. Boundaries are [0,6,12,18,24], so hours 0-5 go to bucket 0, 6-11 to bucket 6, 12-17 to bucket 12, 18-23 to bucket 18. Any hour outside these (like 24) goes to the default bucket labeled "Other".

🧠 Conceptual

intermediate

Why use the bucket pattern for time-series data?

What is the main advantage of using the bucket pattern when storing time-series data in MongoDB?

A It converts time-series data into relational tables for compatibility with SQL.

B It automatically indexes all fields in the collection for faster queries.

C It encrypts the data to improve security for time-series measurements.

D It reduces the number of documents by grouping multiple measurements into one document, improving write and query efficiency.

💡 Hint

Think about how grouping data affects storage and query speed.

The bucket pattern groups many time-series measurements into fewer documents (buckets), which reduces overhead and improves performance for writes and queries compared to storing each measurement as a separate document.

📝 Syntax

advanced

Identify the syntax error in this bucket aggregation

Which option contains a syntax error in the MongoDB aggregation pipeline that buckets data by day?

MongoDB

db.measurements.aggregate([
  {
    $bucket: {
      groupBy: { $dayOfMonth: "$timestamp" },
      boundaries: [1, 11, 21, 31],
      output: {
        count: { $sum: 1 },
        maxValue: { $max: "$value" }
      }
    }
  }
])

A Boundaries array must include the upper bound 32 to cover all days.

B Missing comma after the $bucket stage closing brace.

C The groupBy expression must be a string field name, not an expression.

D The output field names cannot be count or maxValue.

💡 Hint

Check if the boundaries cover all possible day values in a month.

The boundaries [1,11,21,31] cover days 1-10, 11-20, and 21-30 but exclude day 31. To include day 31, the upper boundary should be 32.

❓ optimization

advanced

Optimizing bucket size for time-series data

When using the bucket pattern for storing time-series data, what is a key consideration to optimize performance?

A Avoid using buckets and store each measurement as a separate document for maximum flexibility.

B Choose bucket sizes that balance document size limits and query efficiency by grouping a reasonable number of measurements per bucket.

C Always use a bucket size of exactly 1000 measurements to standardize storage.

D Choose bucket sizes that are too large to minimize the number of documents regardless of document size limits.

💡 Hint

Think about MongoDB document size limits and query speed.

Buckets should be sized to keep documents under MongoDB's 16MB limit and to allow efficient queries. Too large buckets can cause performance issues; too small buckets increase document count and overhead.

🔧 Debug

expert

Debugging missing data in bucketed time-series aggregation

A developer uses this aggregation to bucket time-series data by day but notices some days are missing in the output. What is the most likely cause?

MongoDB

db.timeseries.aggregate([
  {
    $bucket: {
      groupBy: { $dayOfMonth: "$timestamp" },
      boundaries: [1, 10, 20, 31],
      default: "Other",
      output: { count: { $sum: 1 } }
    }
  }
])

A The boundaries do not cover all days; days 10 and 20 are excluded because boundaries are exclusive of the upper bound.

B The $dayOfMonth operator returns zero-based days, so boundaries should start at 0.

C The default bucket is incorrectly labeled "Other" and causes days to be skipped.

D The aggregation pipeline is missing a $sort stage to order days correctly.

💡 Hint

Check how boundaries define bucket ranges and whether all days fall into a bucket.

The $bucket stage includes documents where groupBy value >= lower boundary and < upper boundary. Boundaries [1,10,20,31] create buckets for days 1-9, 10-19, and 20-30. Day 10 falls exactly on a boundary and is included in the next bucket, but day 31 is excluded because upper bound is exclusive. Missing days are due to boundary ranges not covering all days.