Time series databases have long been the cornerstone of a robust metrics system, but the existing options are often difficult to manage in production. In this episode Jeroen van der Heijden explains his motivation for writing a new database, SiriDB, the challenges that he faced in doing so, and how it works under the hood.
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- Your host is Tobias Macey and today I’m interviewing Jeroen van der Heijden about SiriDB, a next generation time series database
- How did you get involved in the area of data engineering?
- What is SiriDB and how did the project get started?
- What was the inspiration for the name?
- What was the landscape of time series databases at the time that you first began work on Siri?
- How does Siri compare to other time series databases such as InfluxDB, Timescale, KairosDB, etc.?
- What do you view as the competition for Siri?
- How is the server architected and how has the design evolved over the time that you have been working on it?
- Can you describe how the clustering mechanism functions?
- Is it possible to create pools with more than two servers?
- What are the failure modes for SiriDB and where does it fall on the spectrum for the CAP theorem?
- In the documentation it mentions needing to specify the retention period for the shards when creating a database. What is the reasoning for that and what happens to the individual metrics as they age beyond that time horizon?
- One of the common difficulties when using a time series database in an operations context is the need for high cardinality of the metrics. How are metrics identified in Siri and is there any support for tagging?
- What have been the most challenging aspects of building Siri?
- In what situations or environments would you advise against using Siri?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA