When developing IoT, financial or industrial applications, the choice of a good time series database is most of the time a headache, choosing between the 30+ (and growing) list of time series vendors in the industry.
When choosing a time series database, it is best to know what they have to offer and how they can suit your needs.
Are you more about directly writing SQL, or do you prefer a brand new processing language for your time series? Are you concerned about cloud based solutions, or do you have your own integration solutions?
This article will help you benchmark your different options.
Here is the list of my best time series database to use in 2019.
Built by InfluxData in 2013, InfluxDB is a completely open-source time series database working on all current operating systems. InfluxDB supports a very large set of programming languages (yes.. even Lisp and Clojure…). It is optimized for heavy writing load and works amazingly well with concurrency.
InfluxDB is schema-free : it is build on NoSQL flavors and allows for quick database schema modifications. Depending on what you are trying to build, this conceptual choice may or may not be adapted to your needs.
Why should you use InfluxDB?
- Play with it in 5 minutes
Five minutes is all it takes from the moment you download it until you are able to play with it. A good technical documentation makes it super easy to install, configure and launch InfluxDB. As a NoSQL-like database, you don’t have to setup your database in any ways : you insert your data and you are good to go.
- Integrated TICK stack
InfluxDB is part of the TICK stack : Telegraf, InfluxDB, Chronograf and Kapacitor. InfluxData provides, out of the box, a visualization tool (that can be compared to Grafana), a data processing engine that binds directly with InfluxDB, and a set of more than 50+ agents that can collect real-time metrics for a lot of different data sources.
Now let’s be fair.
InfluxDB is most of the time used with Grafana. Chronograf is not (at the moment) as good as Grafana, but InfluxData is trying to turn the ship around. By building Flux, a new processing language, and integrating it directly with Chronograf, they might offer some very unique features to it in the next months.
(Want to know more about Flux? I wrote an article about it.)
SQL is Dead, Hail to Flux
Introducing Flux, a new scripting language built by InfluxData. SQL beware, it might be one of the greatest tech trends…
InfluxDB Website — influxdata.com
InfluxData (InfluxDB) | Time Series Database Monitoring & Analytics
Publication: Towards Data Science Title: Processing Time Series Data in Real-Time with InfluxDB and Structured…
Ranked n°15 last year, TimescaleDB is making huge progress in the rankings this year.
Well if you ask me, they provide a very solid and scalable alternative to InfluxDB. TimescaleDB is also open-sourced and based on SQL premises. They also provide a very large set of supported programming languages (incl. Java and Python) for your applications to integrate directly with it.
TimescaleDB is directly tied with PostgresSQL as it scales the famous relational database to offer a unique set of time series related operations (such as fast ingest).
Why should you use TimescaleDB?
- SQL support :
One of the greatest assets of TimescaleDB is the fact that it supports the SQL language natively and allows developers to quickly jump the train without having to learn any new language. It is of course a very nice aspect for developer productivity, as you can ensure that SQL-experienced developers in your team can be immediately effective with TimescaleDB.
- PostgresSQL Integration :
The Guardian did a very nice article explaining on they went from MongoDB to PostgresSQL in the favor of scaling their architecture and encrypting their content at REST. As you can tell, big companies are relying on SQL-constraint systems (with a cloud architecture of course) to ensure system reliability and accessibility. I believe that PostgresSQL will continue to grow, so will TimescaleDB. By belonging to the PostgresSQL ecosystem, TimescaleDB will inherit from all the tools and plugins developed by this huge community.
- A debatable better performance than InfluxDB
I must emphasize that this is a ‘debatable’ better performance as systems are pretty new to the market and they were not tested on all the different cases that the industry has to offer.
As a fair-minded writer, I must point out the fact that Matvey Arye wrote a very good article comparing Flux to SQL and in a way InfluxDB to TimescaleDB. His points about query optimization in particular should be read carefully and they provide a very solid explanation on why they could be more performant (at least in theory).
Matvey Arye article — SQL vs Flux
SQL vs. Flux: Choosing the right query language for time-series data
An examination of the strengths and weaknesses of SQL, and why query planners exist, by way of comparing TimescaleDB…
TimescaleDB Website — timescale.com
Timescale | an open-source time-series SQL database optimized for fast ingest, complex queries and…
An open-source time-series database fully compatible with Postgres for fast ingest and complex queries.
OpenTSDB has been running for quite more time than its competitors and is one of the first technologies to address the need to store time series data at a very large scale. OpenTSDB promises to be able to store hundreds of billions of data rows over distributed instances of TSD servers.
OpenTSDB is a schema free database built on Apache HBase. For those who don’t know, HBase is a non-relational management system written to handle big tables storage in an elegant and efficient way.
Why should you use OpenTSDB ?
Ted Dunning (Chief Application Architect at MapR) made a quite explicative talk about how time series database should be built and how horizontal arranging of time ranges could scale a DBMS up to 20 to 30 millions writes per second. This is a huge insertion rate considering a single InfluxDB node instance could insert up to one million writes per second.
You might want to give OpenTSDB a shot if you are dealing with such insertions rates in your system.
- Integration Ecosystem
Reading the documentation, OpenTSDB integrates with a fair amount of tools such as Cassandra, BigTable, CollectD, StatsD, Chef and even Puppet for deployment management.
Ted Dunning on Time Series Database Architecture
OpenTSDB Website — opentsdb.net
OpenTSDB - A Distributed, Scalable Monitoring System
Store and serve massive amounts of time series data without losing granularity.
Graphite is a even more established and very widely used time series database system. Graphite is a powerful monitoring tool that store numeric time series data and display them on demand via its Graphite-web interface at a fair speed. Graphite is most of the time used as a system, network and application performance metric store. Big companies such as Booking.com, Reddit and GitHub use it on a daily basis to be able to easily detect outage on their architecture.
Why should you use Graphite?
- Graphite does a few things, but it does it well.
Graphite is built to deal with numeric data. As it can be a limitation in itself if you are not dealing with numeric data, Graphite provides out of the box a set of tools that makes it easy for developers to get started. Graphite Web provides a very nice interface for developers to monitor their application.
- A Good Integration Ecosystem
As OpenTSDB, Graphite connects with a lot of tools natively and makes it easy for developers to connect with their existing infrastructure. Graphite is able to easily connect with CollectD, sensu, Riemann, Windows Server, Logstash and many more.
Graphite Website — graphiteapp.org
Graphite is an enterprise-ready monitoring tool that runs equally well on cheap hardware or Cloud infrastructure. Teams…
X — Your Turn To Share!
Do you have experience with those time series databases? If so, which one would you recommend and why?
Also, if you find that some TSMS should be ranked higher or lower, feel free to give your own rankings in the comment section.
XI — Clap & Subscribe
If you liked this article, give us some claps, it always helps. As always, subscribe to our publication to be sure not to miss any software engineering article in the future.