Elasticsearch has become the go-to data store for logs, because it allows you to search and analyze tons of data in milliseconds. Using tools like Logstash and Kibana, you can start feeding logs and graphing them in a few minutes.
As with all software dealing with lots of data, the road from PoC to production is often bumpy, as you might need to scale out to many nodes, and try to squeeze the last ounce of performance out of them.
This talk will take you from the basics of centralizing logs in Elasticsearch, to all the strategies that make it scale with billions of documents in production. We’ll cover:
Rafał Kuć is a search consultant and software engineer at Sematext Group, Inc. mainly focused on Lucene, Solr, Elasticsearch, Hadoop, and Mahout. Rafał is the author of the Apache Solr Cookbook series and Elasticsearch Server. He is a father, a consultant at Sematext, and cofounder of the blog solr.pl, where he tries to share his knowledge.
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