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Time Series Database


A "Time Series" is a collection of data points with timestamps connected to them. A "Time Series Database" (TSDB) is a computer system created to store and access data records that are part of a "Time Series". Each data point's relationship to other data points is critically framed by the timestamps. Time Series data frequently consist of a continuous stream of information, such as sensor measurements and intraday stock prices. Large amounts of Time-stamped data can be stored in a Time Series Database in a format that supports quick insertion and quick retrieval to support complicated analysis of the data.

When compared to other datasets, Time Series datasets are uniformly large and typically consist of a timestamp and the related data. Additionally, Time Series datasets don't need to be stored indefinitely and might have fewer linkages between data records in various tables. Time Series Databases can significantly outperform general purpose databases in terms of storage capacity and performance thanks to the special characteristics of Time Series Datasets.

A software system known as a Time Series Database (TSDB) is tailored for the storage and delivery of time series using related pairs of time(s) and value (s). Time series may also be referred to as profiles, curves, traces, or trends in some disciplines. Early time series databases, sometimes known as data historians, were often employed in support of industrial applications that could effectively retain measured values from sensory equipment. Today, however, they are utilized in support of a considerably wider range of applications.

For instance, because time series data is uniform, specialized compression methods can outperform conventional compression algorithms created to handle less uniform data. Unlike ordinary databases, which are built to keep data indefinitely, time series databases can be programmed to periodically erase outdated data. The performance of queries can also be improved by using unique database indices.

Working of Time Series Database:

TSDBs function by recording a combination of fixed and dynamic values. One set of data points might be labelled "Oil Pressure Rig Number 1" in an oil well where numerous metrics about the rig are recorded, and the related dynamic values would be the pressure measurement and the timestamp. This sample time series data is beneficial for observing patterns in the oil pressure, which, when examined in conjunction with other parameters, may help determine whether to abandon the well or not. These data files are stored on a medium using a format that enables quick time-based reads and writes.

The sequence of the data points is a natural property of the data because all entries are timestamped. A stream processing engine that can consider the ordered data as if it were a data stream can receive the data using this order. Leveraging a quick stream processing engine is typically ideal because one of the key objectives of TSDBs is to be quick. An example of a quick stream processing engine that benefits from in-memory architecture is Hazelcast Jet. Hazelcast Jet can connect with TSDBs, allowing for the rapid processing of time series data provided by the TSDB.

Alternatives to Time Series Database:

Time series data can be stored and accessed using Relational Database Management Systems (RDBS), which are frequently regarded as General-purpose Database Systems. They can store the same data as a TSDB due to the flexibility of RDBMSs; the main distinction is in how the data is transmitted to the storage medium. RDBMSs are not optimized for time series data and are typically slower for adding and accessing time series data because they have various design goals than TSDBs.

Time series data are frequently stored in NoSQL databases, another type of database. NoSQL databases are useful for recording time series data from a variety of different sources since they are more adaptable in terms of the data structure for each record. A NoSQL implementation for time series data is frequently a great option for TSDBs. It can also offer features that are applicable to other types of data in addition to time series data.

Examples of Time Series Database:

  1. InfluxDB
  2. Prometheus
  3. QuestDB
  4. Aerospike
  5. DataStax
  6. GridDB
  7. Druid
  8. Warp 10
  9. Trendalyze
  10. Amazon Timestream
  11. dataPARC
  12. BangDB
  13. createDB
  14. Google Cloud Big Table
  15. CortexDB
  16. Axibase Time Series Database
  17. Heroic
  18. Kinetica
  19. QuasarDB
  20. SiriDB
  21. Tinybird
  22. KX
  23. ExtremeDB

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