By – Rohit Sharma

Have you ever wondered…What is that one thing which is common in self-driving cars, autonomous trading algorithms, smart homes, transportation networks that provide lightning-fast service, and tracking the daily COVID-19 statistics and air quality index in your community? – The answer is Time Series database (TSDB).

Apart from these predicting the exact time your next online purchase delivery, 2020 has shown a good example of how time-series data collection and analysis affects our daily lives. COVID-19 has made people across the world relentless users of time-series data, demanding correct information about the daily trend of COVID-19 statistics.

The patterns of software development already show the same trend. In the past two years, time-series databases (TSDBs) were the fastest-growing database:

What is a Time Series Database? 

With the help of TSDB, it is possible to efficiently process large quantities of real-time data with speed and precision. Other databases have been used for the same purpose in the past, but TSDBs consist of specific tools to deal with their specific needs.

A TSDB stores data as a combination of timestamp(s) and value(s). It is easy to analyze time series or a sequence of data points stored in order over time by storing data in this way. It can process concurrent series, measuring many different fields in parallel.

Previously TSDBs were mainly used for processing volatile financial data and streamlining securities trading. However, the requirements have changed a lot since new use cases have emerged as technology has evolved.

Why do we need a Time Series Database? 

Before using new technology, hesitation always comes to mind. For TSDBs, people ask if can we just use a “normal” (i.e., non-time-series) database? The answer is that we can, and some people do, the two main factors why TSDBs are the fastest-growing database today are :-

Scale: Real-time data accumulates very fast, and other databases are not able to handle that (in an automated way). In common, relational databases deal poorly with very large data points, but NoSQL databases are better (it has been noticed that a fine-tuned relational database for real-time data can actually perform better).

Usability: TSDBs consist of built-in tools common to time-series data analysis, such as data retention policies, queries, aggregations, etc. Scale is not a concern even if you have just started to collect time-series data at the moment, these features can still provide a better user experience and make data processing tasks easier.

What are the benefits of a Time Series Database? 

There’s a reason more and more developers and organizations are using time series databases. They deliver a number of benefits, are as follows:

Accurate time-series measurement: The main benefit of a time series database is that it makes it easy to track how data gets changed over time. With the help of a time series database, you can simultaneously view past, present, and future data for analysis that is more accurate and meaningful.

Efficient data storage: As per the nature of the data type, processing it can require large amounts of storage, that can be difficult to handle — and very costly. Time series databases consist of tools using which it is possible to aggregate data as per required time periods and ignore certain data streams as per need and use compression algorithms that use storage in an efficient way.

Fast data queries: Using time series databases, it is so easy to query and retrieve data under specific periods. For example, imagine someone who doesn’t remember the person to whom he sent money but he remembers that he sent the money three months ago. Time series databases can help the individual figure out the person using three months old data. With the help of a time series database, we can find information based on a specific period.

What are some trending time-series databases? 

Time series databases are the fastest growing database. Is there any way to determine in which time series database is the best and most popular? There are many ways of deciding popularity, but an independent website, DB-Engines, ranks databases based on search engine popularity, social media mentions, job postings, and technical discussion volume.

Here is the list of some time series databases:

1. InfluxDB
2. Kdb+ 
3. Prometheus 
4. Graphite
5. TimescaleDB
6. Apache Druid
7. RRDTool
8. OpenTSDB
9. DolphinDB
10. Fauna 
11. GridDB
12. QuestDB 
13. Amazon Timestream 
14. TDengine 
15. eXtremeDB 

To see trends over time, the following image shows the top 10 time series databases and their historical changes:

Conclusion 

Technology is continuously evolving due to which huge real-time traffic is getting generated on websites with millions of events in a day, trading data on the stock market is increasing, and the time-series database has just arrived in the field! Time-series databases must be in your production pipeline for monitoring.

Most of the above-listed time-series databases are available to self-host, so go ahead, get a cloud VM and give it a try to see what works for you!