Time-series databses were introduced as a progressive relational platforms dedicated to a specific purpose – optimized, workload-resistant storing and retrieval of time-series (or time-stamped) data. This came in as a game-changer solution across the fields where it is essential to manage time-stamped events and metrics for fail-proof, efficient processes, as well as fruitful analytics and forecasts.
In particular, this enabled companies and entrepreneurs to tackle the scanning of extensive data volumes, data summarization, and data lifecycle management. This is especially useful and relevant in the IoT, manufacturing, economics, sales, and other fields where the crucial data flows continuously in an unstructured format in real time.
Specialists at Sirin Software suggest we dive deeper into the concept by taking InfluxDB as a major example of an up-to-date time-series DB and standing point for the article.
What’s a Time-Series Database All About?
In terms of time-series database functionality, certain data points are associated with specific timestamps that define the way the data points relate to one another. Simply put, this is exactly how, for instance, the InfluxDB platform or some other TSDB may segment intense flows of unstructured data, making it easier-to-manage, comprehensive, consistent, and well-prepared for thorough monitoring, in-depth analytics, and further manipulations.
This may come in especially handy in systems of interconnected sensors that require constant monitoring and retrieval of data or in stock prices forecasts where all the data must be timely structured to serve as a comprehensive input for further analytics. Due to such essential opportunities, the concept of TSDB can be safely recognized as one of the leading trends in database technology, especially in the enterprise area of application.
With InfluxDB leading the competition, there are also solutions like TimescaleDB, Graphite, OpenTSDB, Prometheus, and other platforms that are slightly less popular, but mostly differ in specific functionality principles and sets of features.
How does it work?
To go a bit more technical, in order to structure and efficiently retrieve data, TSDB solutions capture both fixed and dynamic data value sets at the same time, autonomously associating fixed data values (descriptions and labels – e.g., “engine #9 heat sensor”) with dynamic values (e.g., measurement of the engine’s temperature with a respective timestamp).
Timely captured, retrieved, and analyzed, these exemplary values can be combined with other metrics for analytics to efficiently predict maintenance needs and serve as a sturdy base for decision-making (e.g., whether to modernize the existing hardware or abandon it whatsoever or not). And the way TSDB records data in a timestamped manner enables rapid data reads and writes based on the order of data points (which is structured on a native level right away).
What about relational and NoSQL databases?
There are two major types and “layers” of databases that are extensively used today in a variety of industries, for a variety of purposes – relational and NoSQL. InfluxDB is of NoSQL type itself, as well as many other TSDBs. Relational and non-timeseries NoSQL databases may handle similar tasks in certain cases. However, there are specifics to keep in mind.
Thus, relational database management systems (commonly considered as general-purpose storage solutions) may handle time-series data, but it retrieves and stores it in a suboptimal format, making processes much slower as opposed to TSDB.
In the long run, the main advantage of a time-series database is that it is initially optimized to work with time-stamped data. Whether it is an InfluxDB platform, TimescaleDB, or OpenTSDB – simply being of the “time-series” kind, these solutions outrun all alternatives in terms of data processing speed all across the board. This doesn’t mean that it is always the perfect choice, however – it all depends on specific purposes and many underlying factors.
InfluxDB – Ins and Outs
With all that being said, let’s return to our main hero. InfluxDB is unique in that it was originally built with the purpose of handling time-stamped data. This is a great native advantage as many other solutions of the same kind were repurposed at some point to handle similar purposes. This is why it is deemed by many as the most convenient tool for not only retrieving and storing but also for monitoring, visualizing, and managing data in various ways. It has an open-source core written by the software aces at InfluxData, which allows for outstanding versatility and speed that can be boosted via various integrations.
The main features of the platform that really stand out among the crow include:
- Query language that is very similar to SQL for data management flexibility;
- Flux as an alternative to InfluxQL focused on querying and analyzing data in an SQL-like manner;
- Queries and data ingestion via an HTTP(S) API;
- An ability to store billions of data points;
- A range of extra data protocols like collectd are supported out-of-the-box;
- Data retention policies that are managed by the database;
- Convenient tags that facilitate work with queries;
- Real-time data aggregation;
- Data series merging;
- Handy filters for stored series;
- Dedicated time-series management interface.
All of that and more other common tools and features offer vast opportunities for rapid handling of time-sensitive data that can give a great performance boost be it the field of IoT or simpler eCommerce.
The owners of InfluxDB conveniently list in detail how much the platform outruns its direct alternatives right on the official website. Thus, it has 5x faster throughput and up to 4x faster query performance as opposed to OpenTSBD; 14x faster throughput and 7x less required disk storage space than Graphite; lastly, it is 45x faster with queries and has a 4.5x faster throughput than Cassandra.
On the flip side, however, there are of course a bunch of downsides that you should also pay attention to in order to make the most informed decision when picking tools and technologies:
- Out-of-the-box security is focused on internal network operation, limiting one’s database cybersecurity capacities;
- The lack of read load-balancing systems apart from sharding;
- Data subsets cannot be backed up and recovered – you can do it for all datasets at once only;
- The lack of ALCs in the open-source version, which limits success in enterprise customers relations;
- Both documentation and community have space to grow and improve.
InfluxDB 2 and Integrations
InfluxDB2 is the latest stable version of the renowned TSDB. It was a long time coming, but now we can safely use it as a better, enhanced version of the good-old InfluxDB. Guys at InfluxData made sure that your transition from the previous versions of the database is seamless and hassle-free. In particular, by granting backward compatibility, an option of the managed Influx Cloud, and support of read-only queries of InfluxQL.
On top of that, the platform can be further integrated with a number of third-party solutions for more flexibility, including Python libraries, a bunch of Amazon and Apache plugins, and a lot more – see the full list of possible integrations. The InfluxDB client library can be easily imported to the Python-powered application, which enables many opportunities for monitoring complex infrastructures as well as third-party applications.
To top it all off, we may confidently say that the concept of time-series databases is on its ultimate rise right now and we are yet to witness as it seeps into industries and niches small and large on a global level. TSDB is a trend on its own – one of the fastest-growing out there. And it will be getting more applications especially in finance- and IoT-related fields. Sirin Software keeps a hand on the pulse of tendencies to help you stay in the know. So stay in tune for more!