April 7th, 2020 • By Jon de Andrés Frías In the first part of our series of blog posts on how we remove technical debt using Apache Kafka at Rollbar, we covered some important topics such as: In the second part of the series, we’ll give an overview of how our Kafka consumer works, how we monitor it, and which deployment and release process we followed so we could replace an old system without any downtime.
This Logz.io Infrastructure Monitoring tutorial will cover our latest product, our new metrics solution based on Grafana. Engineers monitor metrics to understand CPU and memory utilization for infrastructure, duration and serverless execution, or for network traffic. For more advanced metrics monitoring operations, teams can send custom metrics to monitor signals like the number of active users. Logz.io’s flagship product is Log Management, which delivers a fully-managed ELK Stack.
Unified Monitoring provides real-time understanding of what is happening, every time and everywhere on the network, and everything connected to it. With Unified Monitoring, engineers are more able to support proactive identification of root causes and deploy the actions and resources needed to maintain the health and integrity of all connected IT assets.
We've added the ability to disable the team-level notification settings on a per-site basis. This allows for even more flexibility in the way you set up and configure your alerts!
The COVID-19 crisis has put organizations in a position where 100% of employees need to work remotely from their homes. Popular technologies used for supporting remote employees include Citrix virtual application and desktops, virtual desktop technologies based on VMware Horizon, cloud-hosted desktops (DaaS), or just VPN connectivity. Until now, remote access from home was something a small percentage of employees used.
This is a guest post by Ryan Ordway, DevOps Engineer at Oregon State University. At Oregon State University Libraries & Press (OSULP) we have been using Honeycomb for about 18 months. We were in the beginnings of automating our infrastructure and needed an APM solution that we could scale with. New Relic was becoming too expensive, and we couldn’t afford to monitor our whole infrastructure and trace all of our applications anymore. Thus began our Observability journey.
Kafka is a distributed, partitioned, replicated, log service developed by LinkedIn and open sourced in 2011. Basically it is a massively scalable pub/sub message queue architected as a distributed transaction log. It was created to provide “a unified platform for handling all the real-time data feeds a large company might have”.Kafka is used by many organizations, including LinkedIn, Pinterest, Twitter, and Datadog. The latest release is version 2.4.1.
If you’ve already read our guide to key Kafka performance metrics, you’ve seen that Kafka provides a vast array of metrics on performance and resource utilization, which are available in a number of different ways. You’ve also seen that no Kafka performance monitoring solution is complete without also monitoring ZooKeeper. This post covers some different options for collecting Kafka and ZooKeeper metrics, depending on your needs.
Kafka deployments often rely on additional software packages not included in the Kafka codebase itself—in particular, Apache ZooKeeper. A comprehensive monitoring implementation includes all the layers of your deployment so you have visibility into your Kafka cluster and your ZooKeeper ensemble, as well as your producer and consumer applications and the hosts that run them all.
Jenkins is an open source, Java-based continuous integration server that helps organizations build, test, and deploy projects automatically. Jenkins is widely used, having been adopted by organizations like GitHub, Etsy, LinkedIn, and Datadog. You can set up Jenkins to test and deploy your software projects every time you commit changes, to trigger new builds upon successful completion of other builds, and to run jobs on a regular schedule.
