A time series database is like a specialized filing cabinet for time-stamped data. Imagine you have a cabinet with drawers labeled with different time intervals, such as minutes, hours, or days. Each drawer contains neatly organized files representing data points collected at specific times. This setup makes it easy to quickly retrieve and analyze data for specific time periods, like checking the temperature every hour or monitoring website traffic throughout the day.
For example, if you're building a real-time dashboard to display CPU usage over the past 24 hours, you can use a time series database to efficiently store and retrieve these data points. This enables you to generate visualizations and perform calculations on the data without sifting through large volumes of unrelated information.
Time series databases are particularly powerful when working with metrics but are not quite up to the task for more modern observability uscases with distributed tracing.