What is this "time-series data" that we keep talking about, and how and why is it different from other data?
Many applications or databases actually take an overly narrow view, and equate time-series data with something like server metrics of a specific form:
Name: CPU Tags: Host=MyServer, Region=West Data: 2017-01-01 01:02:00 70 2017-01-01 01:03:00 71 2017-01-01 01:04:00 72 2017-01-01 01:05:01 68
But in fact, in many monitoring applications, different metrics are often collected together (e.g., CPU, memory, network statistics, battery life). So, it does not always make sense to think of each metric separately. Consider this alternative "wider" data model that maintains the correlation between metrics collected at the same time.
Metrics: CPU, free_mem, net_rssi, battery Tags: Host=MyServer, Region=West Data: 2017-01-01 01:02:00 70 500 -40 80 2017-01-01 01:03:00 71 400 -42 80 2017-01-01 01:04:00 72 367 -41 80 2017-01-01 01:05:01 68 750 -54 79
This type of data belongs in a much broader category, whether temperature readings from a sensor, the price of a stock, the status of a machine, or even the number of logins to an app.
Time-series data is data that collectively represents how a system, process, or behavior changes over time.
If you look closely at how it’s produced and ingested, there are important characteristics that time-series databases like TimescaleDB typically leverage:
The frequency or regularity of data is less important though; it can be collected every millisecond or hour. It can also be collected at regular or irregular intervals (e.g., when some event happens, as opposed to at pre-defined times).
But haven't databases long had time fields? A key difference between time-series data (and the databases that support them), compared to other data like standard relational "business" data, is that changes to the data are inserts, not overwrites.
Time-series data is everywhere, but there are environments where it is especially being created in torrents.
Monitoring computer systems: VM, server, container metrics (CPU, free memory, net/disk IOPs), service and application metrics (request rates, request latency).
Financial trading systems: Classic securities, newer cryptocurrencies, payments, transaction events.
Internet of Things: Data from sensors on industrial machines and equipment, wearable devices, vehicles, physical containers, pallets, consumer devices for smart homes, etc.
Eventing applications: User/customer interaction data like clickstreams, pageviews, logins, signups, etc.
Business intelligence: Tracking key metrics and the overall health of the business.
Environmental monitoring: Temperature, humidity, pressure, pH, pollen count, air flow, carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10).
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