Scale Your Metrics with ElasticsearchСистемное администрирование
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.
"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 to balance speed for today's data against density for older metrics?
* How can you compress old data and what does the mathematical model look like for different metrics?
We are trying all of this hands-on during the talk, since this has become much simpler recently.
