Operations | Monitoring | ITSM | DevOps | Cloud

It is no longer sufficient for IT operations teams to monitor resources like CPU, memory and disk utilization. The success of any IT initiative these days is measured based on user experience. Irrespective of the type of application in question – whether email, Citrix/VDI, SAP, web application – application availability and response time are key measures of user experience. Application outages not only affect users, they can also impact the business.

It’s five o’clock on a Friday afternoon. There are no new bug reports and everything is looking smooth. Your plan of a relaxing weekend is in sight when you get a call—the website you look after isn’t responding. Yikes. AWS Lambda minimizes the chance of this truly terrifying event from happening by taking care of server maintenance while you focus on coding robust applications.

When gaming online, your “ping” refers to the time it takes for any input from your mouse, keyboard, or controller to register with the server that is hosting your game. Ping rate is measured in milliseconds and anything below or around 20ms is ideal, however, even slight increases above this can result in noticeable differences. Even with a ping rate of around 100ms, games are still playable, but where you will start seeing significant delays is around 150ms.

As Stephen Marsland once said, “if data had mass, the earth would be a black hole.” A vast part of the immense amount of structured and unstructured data that we call “Big Data” is nothing but machine-originated log data. Logs are generated for a lot of different purposes – from security to debugging and troubleshooting. They constitute a gold mine of useful information and actionable insights if properly stored, managed, and analyzed.

Back in Part I of Deploying a Serverless Data Processing Workflow with AWS Step Functions, Nuatu mentioned one key benefit of using step functions is their visibility into business critical workflows. Outside stakeholders, support staff, and other engineers can look at a state machine execution in AWS or Stackery, and can easily understand the process.

Last week I was at Monitorama and it was an amazing time! Not only where the talks fantastic, but I also had the opportunity to join Liz Fong-Jones from Honeycomb.io for a podcast with The New Stack to dig a little deeper into some of the topics in monitoring and site reliability engineering.

Welcome to our series of blog posts about all the nitty-gritty details that go into building a great debug experience at scale. Today, we’re looking at Symbolicator, the service that processes all native crash reports and minidumps at Sentry.

What are the essential things to monitor in your infrastructure? Sure, CPU utilization, memory usage, and IO throughput. However, once you notice a significant load somewhere in your infrastructure you want to know what is causing it, and that typically boils down to needing to find the process that’s using too much CPU or memory or that’s doing disk or network IO like there’s no tomorrow.

Mattermost 5.12 is packed with new features designed to increase team productivity and improve your Mattermost experience.

Hi all! I am a part of the architecture team at Avito.ru, one of the world’s top classifieds (read more about Avito here). In this post I want to share our experience in implementing kubernetes at scale. Kubernetes is a powerful orchestration tool that helps us manage dozens of microservices, support robust and fast deploy. It’s really cool that we don’t have to manage resources manually, think about service discovery and so on.