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“Only accept features that scale” is one of Elasticsearch’s engineering principles. So how do we scale metrics stored in Elasticsearch? And is that even possible on a full-text search engine?

This talk explores:

  • How are metrics stored in Elasticsearch? And how does this translate to disk use as well as query performance?
  • What does an efficient, multi-tier architecture look like that balances speed for today’s data against density for older one?
  • How can you compress metrics and what does the mathematical model look like for that?

Demo: We are trying this hands-on during the talk since this has become much simpler recently. Follow along in the GitHub project.