Time Series Database: what is it, what is it for?

A Time Series DataBase is a database optimized for storing time-stamped data, such as data generated by the Internet of Things. Discover the complete definition of a Time Series DataBase, its differences from other databases, its specific features, its benefits for the enterprise, and a ranking of the most popular TimeSeries databases.

Before defining what a Time Series Data Base is, it is necessary to to explain what Time Series Data is.. It is a sequence of data points collected at regular intervals over a period of time. In simple terms, they are any time-stamped data.

Examples include data generated by connected objects, health information, network data, clicks, market transactions, or application performance. The major difference between this data and traditional data is that the user has questions about this data over time.

In order to be exploited, these data must be aggregated and analysed. In the past, this data had to be stored, but with the rise of the Internet of Things and the explosion of data generated by sensors, it is no longer possible to store it on traditional databases. The need for performance, fault tolerance, availability and scalability results in the need to use Time Series Databases.

Time Series Database: what is it?

A Time Series Database (TSDB) is a database optimized for time series data. It is specially designed for handle time-stamped measurements and events (time-stamped).

This type of database measures changes over time. It provides data lifecycle management, summarization, and scanning of large rows of records.

Why has Time Series Database become important?

iot time series data

Time Series Databases are not new. However, the first generation of TSDBs focused mainly on financial data and the volatility of equity trading. However, computer science has evolved a lot over the last few years. Monolithic mainframes have disappeared to make way for serverless servers, microservers and containers.

Moreover, with the rise of the Internet of Things, more and more real-world objects are being equipped with sensors vehicles, appliances, clothing, production machinery… even humans will soon be implanting sensors. In this context, the number of time series data has literally exploded and these data are produced in an uninterrupted flow.

This is why Time Series databases have become very important and widely used. For support the huge volume of time-stamped data from multiple sources, data infrastructures must evolve along with the development, monitoring, control and management of IT systems.

What are the differences between a Time Series Database and a classic database?

classic database vs. time series database

Time Series Databases have major differences from traditional databases. Their specific properties are storage and compression of time-stamped dataThe new system offers a wide range of features, such as data lifecycle management, data aggregation, the ability to scan large amounts of records, and the ability to support time-stamped queries.

For example, it is possible to require aggregation of data over a long period of time. To do this, the TSDB takes into account a large number of data points in order to calculate, for example, a percentage increase over one month compared to the previous six months. In just a few milliseconds, the Time Series database can browse aggregate data over several months, which is much more complex for a conventional database.

Time Series Database vs. Elasticsearch: What are the differences?

A Time Series Database has several advantages over Elasticsearch. For research, Elasticsearch is a very good solution. However, it is not suitable for Time Series data. First of all, it is difficult to use its API, which slows down developers.

In addition, its performances are much lower to those in a Time Series Database. For writing, a TimeSeries database is generally 5 to 10 times faster than Elasticsearch. The query speed on specific time series can be up to 100 times faster than with Elasticsearch.

Time Series Database vs. MongoDB: what are the differences?

The creators of MongoDB regularly claim that this open source document oriented NoSQL database, written in C and C++, is suitable for Time Series workloads even if it is not a Time Series database. Indeed, MongoDB offers timestamps and bucketing functionalitiesThis allows users to store Time Series data and perform queries on that data.

However, MongoDB is before designed for general data storage. It is possible to store documents of various structures. In fact, this database is only not optimized for time series data storage. It is necessary to take the time to configure it to store this type of data.

TimeSeries Database vs Cassandra

Initially developed by Facebook, Apache Cassandra is a distributed non-relational database written in Java. It is a general-purpose platform that excels at developing scalable distributed databases, but it is lacks most of the main features of a Time Series Database.

To use it for Time Series data, it is therefore need to create additional applications to make up for the missing features. Its use for the Time Series therefore requires a lot of easily dispensable effort.

What are the essential features of a Time Series Data Base?

time series database features

A Time Series Database is intended to support time-stamped data and queries. It must therefore offer several essential features and functions.

Firstly, data with a similar time stamp must be stored on the same physical storage within a database cluster. This will allow queries to be performed more quickly and therefore data analysis to be performed more efficiently.

Time Series Databases must also allow quick and easy querying of queries. To do this, data with a similar time stamp must be stored on the same physical medium. Otherwise, the large volume of data to be browsed may cause errors.

The database must also offer high writing performance. Most conventional databases do not allow queries to be answered quickly during peak load times. It is preferable to choose for a NoSQL masterless distributed database to ensure high availability and a high level of read and write performance. These databases are designed to remain available even in case of high demand.

Finally, in order to enable data to be stored and retrieved in an efficient mannerThe TSDB must allow the data to be compressed according to the needs of the company. A NoSQL database that makes it easy to choose the right level of Data Compaction allows good read and write performance to be maintained over the long term.

What are the advantages of a Time Series Database?

A Time Series Database has several advantages. First of all, it allows you to massive scalability and high performanceThis is essential to support data generated by millions of IoT devices or a continuous stream of data points and perform real-time analysis.

These databases can also reduce downtime. Even in the event of a hardware failure or network partition, the data remains available. In addition, the TSDBs make it possible to cut costs. This is because high fault resilience reduces the amount of resources needed to manage outages, and scalability reduces the operating and hardware costs required for scaling.

Finally, the Time Series Databases allow you to make better decisionsby allowing companies to analyze data in real time. Organizations are able to take faster and more accurate action to adjust energy consumption, device maintenance, infrastructure changes, or other important decisions that may impact the business.

What are the most popular Time Series Databases?

With so many options available, it can be difficult to choose which Time Series Database to use. A good way to make your choice is to examine which Time Series Databases are the most popular. To help you do this, the DB-Engines website classifies Time Series databases according to their popularity.

To determine the popularity of a database, the site is based on several criteria The number of searches on the web, the number of mentions on social networks, job offers related to the database in question, or the amount of technical discussions about it.

db engines tsdb

Based on this methodology, here is the ranking of the most popular Time Series databases:

  1. InfluxDB
  2. Kdb+
  3. RRDTool
  4. Graphite
  5. OpenTSDB
  6. Prometheus
  7. Druid
  8. KairosDB eXtremeDB
  9. Riak TS
  10. Hawkular Metrics
  11. Blueflood
  12. Axibase
  13. Warp 10
  14. TimescaleDB

The DB-Engines site also reveals that the Time Series Database are the databases that have seen the strongest growth in recent months. This is proof that more and more companies are looking for a solution to manage their time-stamped data.

tsdb popularity growth

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