Intermediate LevelMongoDB & Mongoose
What are MongoDB Time-Series Collections and how do they compress metrics?
❓ Interview Prompt: Explain Time-Series Collections, timeField, metaField, granularity (seconds, minutes, hours), columnar storage format, and disk compression.
MongoDB Time-Series Collections efficiently store sequences of measurement data over time (IoT sensors, financial stock ticks, application APM metrics):
Storage Mechanics:
Converts documents internally into a high-density columnar format, reducing disk storage footprints by up to 70% and accelerating temporal aggregation queries!
Code Implementation Example
typescript
import mongoose from 'mongoose';
async function createSensorTimeSeriesCollection() {
const db = mongoose.connection.db;
await db!.createCollection('sensor_metrics', {
timeseries: {
timeField: 'timestamp',
metaField: 'sensorId',
granularity: 'seconds'
}
});
}💡Creating an optimized Time-Series collection in MongoDB.
🎯 Interview Key Takeaway
Use Time-Series collections for metric data to leverage internal columnar compression and fast temporal queries.
#Time-Series#Columnar Storage#Metrics#Intermediate
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