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Gnocchi: A Scalable Time Series Database For Your Metrics with Julien Danjou

Summary

Do you know what your servers are doing? If you have a metrics system in place then the answer should be “yes”. One critical aspect of that platform is the timeseries database that allows you to store, aggregate, analyze, and query the various signals generated by your software and hardware. As the size and complexity of your systems scale, so does the volume of data that you need to manage which can put a strain on your metrics stack. Julien Danjou built Gnocchi during his time on the OpenStack project to provide a time oriented data store that would scale horizontally and still provide fast queries. In this episode he explains how the project got started, how it works, how it compares to the other options on the market, and how you can start using it today to get better visibility into your operations.

Preface

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  • Your host as usual is Tobias Macey and today I’m interviewing Julien Danjou about Gnocchi, an open source time series database built to handle large volumes of system metrics

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Gnocchi is and how the project got started?
    • What was the motivation for moving Gnocchi out of the Openstack organization and into its own top level project?
  • The space of time series databases and metrics as a service platforms are both fairly crowded. What are the unique features of Gnocchi that would lead someone to deploy it in place of other options?
    • What are some of the tools and platforms that are popular today which hadn’t yet gained visibility when you first began working on Gnocchi?
  • How is Gnocchi architected?
    • How has the design changed since you first started working on it?
    • What was the motivation for implementing it in Python and would you make the same choice today?
  • One of the interesting features of Gnocchi is its support of resource history. Can you describe how that operates and the types of use cases that it enables?
    • Does that factor into the multi-tenant architecture?
  • What are some of the drawbacks of pre-aggregating metrics as they are being written into the storage layer (e.g. loss of fidelity)?
    • Is it possible to maintain the raw measures after they are processed into aggregates?
  • One of the challenging aspects of building a scalable metrics platform is support for high-cardinality data. What sort of labelling and tagging of metrics and measures is available in Gnocchi?
  • For someone who wants to implement Gnocchi for their system metrics, what is involved in deploying, maintaining, and upgrading it?
    • What are the available integration points for extending and customizing Gnocchi?
  • Once metrics have been stored, aggregated, and indexed, what are the options for querying and analyzing the collected data?
  • When is Gnocchi the wrong choice?
  • What do you have planned for the future of Gnocchi?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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