We might be unaware of this fact, but we use time series databases all the time. They will become even more relevant as the Internet of Things (IoT) and other revolutionary technologies continue to develop. In this article, we will review what a time series database is, what its purpose, and their impact on our daily lives.
What is a Database?
A database is a collection of data that is organized and stored in a correlated manner. For example, a contact list is a physical database where we write down names, addresses, phone numbers, and email addresses. We create profiles by storing correlated information about individuals. There are several data types a database can handle, depending on their structure or their management system.
What is a Time Series Database?
A time series database (TSDB) is a database designed and optimized to register and store data that is always associated with a specific point in time or that uses a timestamp. This structure makes it easy to analyze events chronologically over time from any data source. Not only that, a TSDB can receive concurrent inputs, allowing us to analyze and store several streams of data simultaneously and even analyze them combined.
A TSDB has two primary uses:
- It serves as a historical reference point for any type of data stream.
- It performs complex data analysis and predictive modeling.
How is a Time Series Database Different?
A superficial reading of the definitions may make us wonder, what makes a TSDB different from other database systems like a Relational DatabaseManagement Systems (RDBMS) or NoSQL database? Both RDBMSs and NoSQL databases can store data in value pairs of data-time. However, structurally, they are vastly different. TSDBs are specially optimized to write data fast, have a superior compression algorithm, and have a considerably faster query engine.
In the graph below, we can see how the CPU usage correlates with RAM usage on a specific server over a period of time. This information can be efficiently stored and used to create a projection based on historical data.
TSDBs are specifically useful while researching and gathering trends of a general pattern or direction in which a particular data set is moving. Given its nature, it is also excellent at observing odd variations or data anomalies in an otherwise stable environment. Since all records are timestamped, the ledger of the data points is natively organized. These databases are configured to efficiently deliver the data to several processing engines, simulating the original data stream.
We’ve seen major growth in usage of TSDBs since it was popularized back in 2015, especially over the past 12 months, as illustrated in this graph.
We can track a massive amount of real-time data with almost instant speed and storage efficiency. This proposal sounds especially appealing for industrial applications. Indeed, industries have welcomed this technology since the beginning for applications that range from efficiently storing sensory equipment data to rapidly tracking bottlenecks during fabrication processes. Of course, this is not the only potential use of TSDBs, and a great deal of development is expanding its usage to a broader range of applications.
Other applications include the Internet of Things (IoT) concept, with sensors permanently generating data and communications within devices. A TSDB also offers a more efficient and faster working environment. In this same context, the implementation of TSDBs for web monitoring systems and performance tracking is gaining popularity among developers who use it to identify issues and trace a system’s overall stability. This data compilation also serves to model web visitor patterns to offer a better overall UI experience.
These databases are also widely used in predictive models for sales, demands, trends, cycles, and analyzing rapidly changing prices in financial markets. It has proven to be extremely useful in medical operations by storing and streaming information from inserted or wearable devices.
Popular TSDM Systems
Following the popularity of TSDBs, database management system development for time series analysis has exploded. The aim is to optimize the existing data storage algorithms used to improve client experiences. We will be discussing some of the most popular options below.
InfluxDB is the default option for time series databases. Compatible with all modern operating systems and supporting a long list of programming languages, this open-source project was introduced by InfluxData in 2013. It has been a major player in this space ever since. It is extremely well optimized and can handle concurrent processes at ease. The one major downside is that it is hard to scale.
Initially developed by SoundCloud, this open-source software offers a reliable alternative to store time series data with a focus on monitoring systems and applications. It can handle a considerable amount of volume. However, since it is built to use a single node set up, it might present scalability issues.
KDB+ is a high-performance TSDB system developed and marketed by Kx Systems. The primary design goal for this system was to process billions of entries of real-time data for the financial system. This is achieved mainly due to its extremely efficient programming language known as Q.
In this tutorial, we have explored the basics of Time Series Databases. TSDBs will become even more relevant over time as our need to continually track and store tremendous amounts of information increases. We are at the dawn of fully managed modeling systems that will be able to take effective decision-making process based on historical data in a more efficient manner. The implications this may carry are yet to be seen, but this technology has proven to be very beneficial to the way we store and process information.
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