Video and Presentation: Indexing and Searching Logs with Elasticsearch or Solr

Interested in log indexing using Elasticsearch or Solr?  Also interested in searching and analyzing logs in real time?

This topic really hits home for us since we released our log analytics tool, Logsene and we also offer consulting services for logging infrastructure.  If you are reading this and looking for a new opportunity then you might be interested to hear that we are hiring worldwide.

If you are into logging like we are, then you will want to check out this presentation delivered by Sematext’s own Radu Gheorghe to the NYC Search, Discovery and Analytics Meetup held recently at Pivotal Labs.  For the purposes of this presentation the term “logs” ranges from server logs and application events to metrics and even social media information.

The presentation has three parts:

  1. Overview of logging tools that play nicely with Elasticseach and Solr (like Logstash, Apache Flume or rsyslog)
  2. Performance tuning and scaling Elasticsearch and Solr
  3. Demo of an end-to-end solution

Here you go – enjoy!

Video: Using Solr for Logs with Rsyslog, Flume, Fluentd and Logstash

A while ago we published the slides from our talk at Lucene Revolution about using Solr for indexing and searching logs. This topic is of special interest for us, since we’ve released Logsene and we’re also offering consulting services for logging infrastructure. If you’re also into working with search engines or logs, please note that we’re hiring worldwide.

The video for that talk is now available, and you can watch it below. The talk is made of three parts:

  • one that discusses the general concepts of what a log is, structured logging and indexing logs in general, whether it’s Solr or Elasticsearch
  • one that shows how to use existing tools to send logs to Solr: Rsyslog and Fluentd to send structured events (yes, structured syslog!); Apache Flume and Logstash to take unstructured data, make it structured via Morphlines and Grok, and then send it to Solr
  • one that shows how to optimize Solr’s performance for handling logs. From tuning the commit frequency and merge factor to using time-based collections with aliases