Time Series Database
A time series database server (TSDS) is a software system that is optimized for handling time series data, arrays of numbers indexed by time (a datetime or a datetime range). In some fields these time series are called profiles, curves, or traces. A time series of stock prices might be called a price curve. A time series of energy consumption might be called a load profile. A log of temperature values over time might be called a temperature trace. Despite the disparate names, many of the same mathematical operations, queries, or database transactions are useful for analysing all of them. And the implementation of a database that can correctly, reliably, and efficiently implement these operations must be specialized for time-series data. Despite this specialized treatment, TSDSs are nonetheless subject to Brewer's Theorem which states that CAP (Consistency, Availablity, Partitionability) is a spectrum of capabilities and not a single achievable requirement, at least for large transaction bandwidth and large large data sets.
TSDSs are databases that are optimized for time series data. Software with complex logic or business rules and high transaction volume for time series data are not practical with the alternative to TSDS, relational database management systems. Flat file databases are not a viable option either, if the data and transaction volume reaches a maximum threshold determined by the capacity of individual servers (processing power and storage capacity). Queries for historical data, replete with time ranges and roll ups and arbitrary time zone conversions are difficult in a relational database. Compositions of those rules are even more difficult. This is a problem compounded by the free nature of relational systems themselves. Many relational systems are often not modelled correctly with respect to time series data. TSDS on the other hand impose a model and this allows them to provide more features for doing so.
Ideally, these repositories are often natively implemented using specialized database algorithms. However, it is possible to store time series as binary large objects (BLOBs) in a relational database or by using a VLDB approach coupled with a pure star schema. Efficiency is often improved if time is treated as a discrete quantity rather than as a continuous mathematical dimension. Database joins across multiple time series data sets is only practical when the time tag associated with each data entry spans the same set of discrete times for all data sets across which the join is performed.
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