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Comparing InfluxDB, TimescaleDB, and QuestDB Time Series Databases

We're living in the golden age of databases, as money flows into the industry at historical rates (e.g., Snowflake, MongoDB, Cockroach Labs, Neo4j). If the debate between relational vs. non-relational or online analytical processing (OLAP) vs. online transaction processing (OLTP) ruled the past decade, a new type of database has been steadily growing in popularity. According to DB-Engines, an initiative to collect and present information on database management systems, time series databases are the fastest growing sector since 2020:

Data vs. Database Type Popularities

Why Use a Time Series Database?

Time series databases (TSDB) are databases optimized to ingest, process, and store timestamped data. Such data may include metrics from servers and applications, readings from IoT sensors, user interaction on a website or an app, or trading activity on financial markets.

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Running QuestDB on GKE Autopilot


Recently, I’ve been experimenting with QuestDB as the primary time-series database to stream and analyze IoT/financial data:

While I was able to validate the power of QuestDB in storing massive amounts of data and querying them quickly in those two projects, I was mostly running them on my laptop via Docker. In order to scale my experiments, I wanted to create a more production-ready setup, including monitoring and disaster recovery on Kubernetes. So in this guide, we’ll walk through setting up QuestDB on GKE with Prometheus and Velero.