2019-05-05, 11:00–11:50, Room A
"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?
We are trying this hands-on during the talk since this has become much simpler recently.
Philipp lives to demo interesting technology. Having worked as a web, infrastructure, and database engineer for more than ten years, Philipp is now working as a developer advocate at Elastic — the company behind the open source Elastic Stack consisting of Elasticsearch, Kibana, Beats, and Logstash. Based in Vienna, Austria, he is constantly traveling Europe and beyond to speak and discuss about open source software, search, databases, infrastructure, and security.