kdb+ is a columnar time-series database that supports relational modeling and in-memory computing. You can learn more about the finer details of how TimescaleDB differs from PostgreSQL and how the other product offerings on the official blog. TimescaleDB also offers Promscale, a database backend for Prometheus to handle OpenTelemetry data efficiently. In the following video, Erik Nordström talks about how TimescaleDB works and how it compares with other competitors: TimescaleDB is essentially a package extension on top of PostgreSQL, solving for a specific read-write pattern with time-series data. TimescaleDB is another amongst many others. It’s not coincident that many amazing databases have been built on PostgreSQL. One of the great features of PostgreSQL is its extensibility. It is packaged as a PostgreSQL extension. TimescaleDBĪn open-source time-series SQL database optimized for fast ingest and complex queries. You can essentially use TSBS to compare most of the timeseries databases on the market. It’s been a while, but I wrote about using TSBS (Timeseries Benchmarking Suite) for benchmarking QuestDB & TimescaleDB performance. You can find the advantages and limitations of QuestDB’s PostgreSQL compatibility here.
#Time series database drivers
This Postgres compatibility also means that most of the drivers you use to connect to PostgreSQL would also work with QuestDB. This is possible in QuestDB as it supports Postgres wire protocol. Like running most of your PostgreSQL queries in Redshift, you can run most of those queries in QuestDB. Like many other timeseries databases today, QuestDB is also inspired by the open-source relational database behemoth, PostgreSQL. This database also supports relational modeling for timeseries data, which means that you can write joins and use SQL queries to read your data. QuestDB appends any new data at the bottom of each column to preserve the natural time order of the ingested data. QuestDB uses a columnar structure to store data. Vlad, CTO of QuestDB, talks about QuestDB’s architecture and use-cases at Carnegie Mellon University. Recently, Vlad talked about QuestDB’s architecture and exciting use cases at the Carnegie Mellon University Database Group: He started QuestDB as a side project to create a superfast timeseries database that works at scale. Vlad Ilyushchenko, working in the financial services industry in the early 2010s, had a good idea about the importance of low latency in the financial sector. QuestDBĪn open-source SQL database designed to process time-series data faster You can read more about their journey on this blog. Another major timeseries database, QuestDB, which supports PostgreSQL wire protocol, decided to support and push for InfluxDB line protocol because of its implementation and performance. One such example is the InfluxDB line protocol, a lightweight protocol to store and transfer timeseries data into a timeseries database. Without a doubt, InfluxDB has been instrumental in the sped-up progress of time-series databases in the last couple of years.
One fun use case is monitoring gaming metrics for Counter-Strike, as shown in the image below: InfluxDB also provides you with templates for popular use cases. Like most other TSDBs, you can deploy InfluxDB on either of the three major cloud platforms - AWS, Google Cloud, and Azure. InfluxData earns money by offering enterprise versions of this database on-prem and in the cloud. This is, by far, the most popular and most used time-series database in the world. Scalable datastore for metrics, events, and real-time analytics. So, without further ado, let’s dive right into it. In this article, I will go through the most popular time-series databases and several databases that were not built to solve the time-series problem specifically and can handle it pretty well. I have also talked about specific features of some of the popular databases like InfluxDB (Influx Line Protocol), TimescaleDB (Comparison using TSBS), and QuestDB (Ingestion patterns).
I have covered their unprecedented rise in popularity and some other specialized or, as AWS likes to call them, purpose-built databases. I have written about time-series databases previously. Since then, I have been keeping myself up to date with the goings-on in the world of TSDBs.
Nothing came out of that, except that my mucking around with InfluxDB had sparked my interest in time-series databases. We did a couple of days worth of brainstorming. I grew fond of time-series databases when I started mucking around with InfluxDB in 2017 for an algorithmic options trading platform a colleague wanted to build.
Having worked with some of the significant time-series database products and having explored others from a tech enthusiast’s point of view, I thought it might be helpful to summarize what I have learned in a quick write-up. A short survey of the timeseries databases of today Background