January 4, 2017
Logging in JSON transforms your logs from raw text lines to a database of fields you can search, filter, and analyze. This gives you way more power than you would get with only raw logs. Here are some tips and choices to consider when implementing logging in JSON. It’s
December 16, 2016
Occasionally, I’ll talk to a developer or operations engineer who says they only use APM or logs as a monitoring solution. They’ll say something like, “We don’t look at logs much since we have New Relic.” Even worse, some rely on their customers to know when something is wrong: “We just check Google Analytics to see if popular pages or actions have gone down.” The surprising thing isn’t necessarily that they have a favorite go-to tool, but that they think it’s sufficient for all their needs. Have we finally achieved the holy grail: a single pane of glass that tells us everything we need to know about operations and performance?
September 21, 2016
JSON is the most popular log type used by Loggly customers because it makes it relatively easy for you to benefit from Loggly’s automated parsing and analytics; and selfishly, it makes sense as we handle JSON better than anyone else. JSON allows you to treat your logs as a structured database on which you can easily filter and report, even if there are millions of events. The chart below shows the JSON fields on which Loggly customers perform the highest number of searches based on a sample of over 60,000 searches.
October 1, 2015
This guide will help software developers and system administrators become experts at using logs to better run their systems. This is a vendor-neutral, community effort featuring examples from a variety of solutions. Each guide includes: A basic overview of what’s in the logs and where to find them, how to search or analyze logs, how to centralize logs in a large distributed system, and how to troubleshoot common issues.
