Tools For Software Engineering Teams
The effect of poorly performing web applications echoes across various parts of the business. The more breakage there is, the more busy support teams get.
Filed under: DevOps
Mapping Plumbr to Monitoring Terminology
There are four “cornerstones” upon which the whole galaxy of monitoring services is built. These are Availability, Latency, Throughput, and Capacity. Whether our applications are
Filed under: Monitoring Performance Plumbr
The Sense And Sensibilities Of Bug Triage
When I began to program professionally, meetings were never something I’d look forward to. Of all the meetings we had, the Bug Triage was the
Filed under: Plumbr
Feature focus: Demoting errors and Transaction snapshots
Building a product is hard. Perhaps the biggest challenge in building a product is to think through all the different permutations in which a user
Filed under: Plumbr Product Updates
An Engineer’s Guide To SLA, SLO, and SLI.
Engineers want software systems to be massive, yet be agile, to perform at the highest class, and to not compromise on security. They want software
Filed under: Monitoring
Feedback and Monitoring in DevOps
The goal with today’s post is to further reaffirm the notion that Plumbr, as we envision it, is a key ingredient in a good DevOps
Filed under: DevOps Monitoring
Real-user Monitoring in a DevOps Toolchain
One of the current trends in software development is a strong push towards DevOps. As a concept, DevOps has been spoken about for nearly ten
Filed under: DevOps Plumbr
Outage == Outrage
Let’s own up – we’ve all been part of teams that have faced failures and/or slow performance in web applications. The Google SRE book highlights
Filed under: Monitoring Performance Plumbr Product Updates
hello, (again) world!
“Quality is a customer determination … based on the customer’s actual experience with the product or service…” -A. V. Feigenbaum, “Total Quality Control”, McGraw-Hill, 1983.
Filed under: Plumbr Uncategorized
Lies, damn lies and “our performance overhead is 2%”
Measuring the performance overhead of a Java agent happens to be a lot more complex exercise than it might originally seem. This post explains how system saturation is the key source for performance issues to be caused and gives specific examples how certain aspects introduced by agents can impact the performance of the system.
Filed under: Java Performance