MongoDB time series collections provide optimized storage and querying for time-stamped measurement data. Master metaField, bucket pattern, granularity tuning, window functions, and TTL expiration for IoT, monitoring, and analytics workloads.
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A developer creates a MongoDB collection with timeseries: { timeField: "timestamp", metaField: "sensorId" }. What does the metaField option specify?
The metaField identifies the field that labels the data source (e.g., sensor ID, device ID, stock ticker). MongoDB uses this field to co-locate measurements from the same source within the same internal bucket, dramatically improving storage efficiency and query performance for time series workloads.
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MongoDB time series collections use a bucket pattern internally. What does bucketing achieve?
The bucket pattern stores multiple measurements from the same series (same metaField value, close in time) within a single internal document rather than one document per measurement. This reduces per-document overhead (no repeated metaField, fewer index entries), significantly improving both storage efficiency and read performance for range queries.
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A query on a MongoDB time series collection uses $setWindowFields with a time-based window. What capability does this operator provide?
$setWindowFields enables window function computations in MongoDB aggregation — similar to SQL's OVER(PARTITION BY ... ORDER BY ...). For time series, it can compute moving averages, cumulative sums, rank within a window, or lead/lag values over time-based or document-based windows without materializing intermediate results.
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A MongoDB Atlas time series collection has granularity: "hours" set. What does granularity control?
Granularity hints to MongoDB how frequently data arrives, influencing the target time span of each internal bucket. "seconds" targets ~1-minute buckets, "minutes" targets ~1-hour buckets, "hours" targets ~1-day buckets. Matching granularity to actual data frequency maximizes bucket fill rate and compression ratios.
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A time series collection has an expireAfterSeconds: 2592000 option. What does this option configure?
expireAfterSeconds configures automatic TTL (Time-To-Live) data expiration for time series collections. MongoDB automatically deletes documents (and their buckets) where the timeField value is older than the specified duration. This enables rolling data retention without manual cleanup jobs.