Time series databases are actually a subset of OLAP databases. The main difference between time series databases and OLTP databases is the amounts of data stored and processed. While OLTP databases can process billions of rows per node, time series databases can deal with trillions of rows per node.
The main requirements for time series databases:
- Fast data ingestion (millions of rows per second).
- Good compression for the stored data, since the amounts of time series data is usually huge (trillions of rows per node). The compression also may improve query speed, since it reduces the amounts of data that needs to be read from disk during heavy queries.
- Fast search for time series with the given labels. For instance, search for temperature measurements across all the sensors in the given country with millions of temperature sensors.
- Fast rows processing for the found time series on the given time range. Usually the number of rows to process exceeds hundreds of millions per query.
Typical OLTP databases cannot meet these requirements.
The main requirements for time series databases:
- Fast data ingestion (millions of rows per second).
- Good compression for the stored data, since the amounts of time series data is usually huge (trillions of rows per node). The compression also may improve query speed, since it reduces the amounts of data that needs to be read from disk during heavy queries.
- Fast search for time series with the given labels. For instance, search for temperature measurements across all the sensors in the given country with millions of temperature sensors.
- Fast rows processing for the found time series on the given time range. Usually the number of rows to process exceeds hundreds of millions per query.
Typical OLTP databases cannot meet these requirements.