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?