Logstash Tutorial: A Quick Getting Started Guide
Looking to learn about Logstash as quickly as possible? This article is for you: we’ll install Logstash and push some Apache logs to Elasticsearch in less than 5 minutes.
Looking to learn about Logstash as quickly as possible? This article is for you: we’ll install Logstash and push some Apache logs to Elasticsearch in less than 5 minutes.
Logging is an important part of understanding the behavior of your applications. Your logs contain essential records of application operations including database queries, server requests, and errors. With proper logging, you always have comprehensive, context-rich insights into application usage and performance. In this post, we’ll walk through logging options for Rails applications and look at some best practices for creating informative logs.
In a previous post, we walked through how you can configure logging for Rails applications, create custom logs, and use Lograge to convert the standard Rails log output into a more digestible JSON format. In this post, we will show how you can forward these application logs to Datadog and keep track of application behavior with faceted log search and analytics, custom processing pipelines, and log-based alerting.
Today we are releasing Grafana 5.2.3 and Grafana 4.6.4. These patch releases includes a very important security fix for all Grafana installations which are configured to use LDAP or OAuth authentication.
As we are fond of saying here at Honeycomb, context is king, and one of our favorite ways to share the context in our brains is with Boards. We recommend using Boards to share query structures you’ve developed for reuse, share visual graphs for ongoing review of systems, share your brain with your colleagues…and your future self.
Datadog makes it easy to correlate, compare, and visualize metrics from your infrastructure and applications. Some metrics, however, are inherently so noisy that the graphs become unreadable (the dreaded spaghettification problem), and you lose the ability to extract essential information about trends and large-scale deviations. For cases like these, we provide several smoothing functions that help you identify trends in your metrics.