You can now create monitoring integrations for your technology stack easier than ever before. We’ve recently opened-sourced Sematext’s Monitoring Agent, reworked it, and made it fully pluggable, making it possible for you to collect metrics from a number of additional sources.

This recipe is similar to the previous rsyslog + Redis + Logstash one, except that we’ll use Kafka as a central buffer and connecting point instead of Redis. You’ll have more of the same advantages.

Starting from Elasticsearch 5.0, you’re able to define pipelines within it that process your data, in the same way you’d normally do it with something like Logstash. We decided to take it for a spin and see how this new functionality (called Ingest) compares with Logstash filters in both performance and functionality. Is it worth sending data directly to Elasticsearch or should we keep Logstash?

Here at Sematext we use Java and rely on Logsene, our hosted ELK logging SaaS, a lot. We like them so much that we regularly share our logging experience with everyone and help others with logging, especially, ELK stack. Centralized logging plays nice with Java (and anything else that can write pretty logs). However, there is one tricky thing that can be hard to get right: properly capturing exception stack traces.