Operations | Monitoring | ITSM | DevOps | Cloud

Containers provide a nifty solution to package up applications along with their dependencies, and for the whole encapsulated process to be run on a host system. This technology is undeniably popular due to its ability to allow developers to create flexible, scalable, reliable solutions in a quicker amount of time. It has enabled more freedom in choosing the technology we use in our applications and has brought development and production environments closer to parity.

This week we’ll learn about the new Google Stackdriver core datasource in Grafana, dive into the new Postgres query editor and share some best practices.

Sifting through all your logs to find what you need can be challenging—especially during an outage, when time is critical and you’re flooded with WARN and ERROR messages. To help you immediately surface useful information from large volumes of logs, we developed Log Patterns.

In Elasticsearch parlance, a document is serialized JSON data. In a typical ELK setup, when you ship a log or metric, it is typically sent along to Logstash which groks, mutates, and otherwise handles the data, as defined by the Logstash configuration. The resulting JSON is indexed in Elasticsearch.

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.