You need to know how your sales changed over the last 90 days.
You open your database. Run a query. Wait.
And wait. The query is scanning every single record to find the ones in your date range.
Your database treats time like any other column. It shouldn't.
DATA INFRASTRUCTURE - How you store time-stamped data determines how fast you can query it.
Regular databases store data in whatever order it arrives. When you ask for "the last 30 days," they have to check every row. Time-series databases store data ordered by time from the start.
This matters when you have millions of data points. Your IoT sensors log every second. Your payment system records every transaction. Your website tracks every page view. Each one gets a timestamp.
When you query by time range (which is almost always), a time-series database jumps directly to the right section. It doesn't scan rows from 2019 when you asked for data from this morning.
The difference between "query took 30 seconds" and "query took 30 milliseconds" often comes down to whether your storage understands that time-stamped data should be stored chronologically.
When data has an inherent ordering (like time), storing it in that order makes range queries nearly instant instead of scanning everything.
Organize data by its natural sequence. Put an index on the ordering dimension. Queries that follow the sequence become seeks instead of scans. This pattern appears anywhere data has an inherent ordering.
You have 12,000 sensor readings spanning 12 months. Select a time range and watch how unordered storage scans everything while time-partitioned storage skips irrelevant months.
Run a query to see which partitions get scanned.
Data split into time chunks
Instead of one giant table, data is split into partitions by time period (hourly, daily, monthly). Query for "last week"? The database only looks at recent partitions, ignoring years of old data entirely.
Same values stored once
Time-series data often repeats. Sensor ID stays constant. Status is usually "OK." Columnar storage groups identical values together and compresses them. 10GB of raw data becomes 500MB on disk.
Old data gets summarized
Do you need per-second data from last year? Usually not. Time-series databases can automatically roll up old data: keep hourly averages after 30 days, daily after a year. Storage stays bounded.
Your finance lead needs this for the board deck tomorrow. With proper time-series storage, the dashboard loads in 200ms. Without it, the query times out after 30 seconds because it's scanning millions of unordered rows.
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You stored your IoT data in Postgres. Worked fine with 10,000 rows. Now you have 50 million rows and the dashboard takes 45 seconds to load. Adding an index on timestamp helps, but it's still scanning too much.
Instead: Use a purpose-built time-series database (TimescaleDB, InfluxDB, QuestDB) when you expect millions of time-stamped records and will query by time range constantly.
You kept every millisecond of sensor data for 3 years "just in case." Now you have 12TB of data, queries are slow, and your storage costs are out of control. Nobody has ever queried 2-year-old millisecond data.
Instead: Define retention policies upfront. Keep high-resolution data for recent periods, downsample older data to hourly/daily summaries, delete what you'll never need.
You stored all sensor data in one time-series. Now you query for "sensor A, last hour" but the database still scans all 10,000 sensors to find it. Time partitioning alone isn't enough.
Instead: Partition by both time AND your most common filter dimension (sensor_id, customer_id, region). Queries that filter on both become instant.
You know how to store time-stamped data efficiently. The natural next step is learning how to summarize that data into meaningful insights.