So, what exactly does OverOps do? OverOps pinpoints the exact cause of critical exceptions in your application with the variable state for context. Armed with that information, you don’t have to waste countless hours on detective work sorting through logs or your APM to figure out what happened with your application and what precisely needs to be fixed. For example, a programmer typically sees an exception with the related stack trace like the following.
Ever create the perfect Spark application with automated tests just to have it fail when it is distributed and run on a big data set? If so then this article is for you. My name is Chris Caspanello and before I joined OverOps, I worked on Pentaho, a leading visual data transformation tool. One of its powerful features is to develop a transformation locally and then run that transformation on your big data cluster with Spark. Sounds cool, right?
Automated garbage collection (along with the JIT HotSpot Compiler) is one of the most advanced and most valued components of the JVM, but many developers and engineers are far less familiar with Garbage Collection (GC), how it works and how it impacts application performance. First, what is GC even for? Garbage collection is the memory management process for objects in the heap.
What is the ultimate alerting strategy to make sure your alerts are meaningful and not just noise? Production monitoring is critical for your application’s success, we know that. But how can you be sure that the right information is getting to the right people? Automating the monitoring process can only be effective when actionable information gets to the right person. The answer is automated alerting.
Over the past few years, we have experienced a shift in the way we deliver applications. We are being forced to innovate faster and deliver higher quality software more frequently. Applications are now commonly built on microservices and using Kubernetes. In response to this, the way we approach the design and delivery of software has changed. Most would identify the cloud as a main component of this change. While that’s true, let’s dig deeper.
Per Forbes, the distributed workforce is here to stay. Over half of Americans now work either full-time or part-time from somewhere that isn’t an office. We can only expect that number to keep growing, and while other countries haven’t jumped into fully remote work quite as quickly as the United States, the global trends are undoubtedly headed in the same direction.
As the general population becomes more app-savvy and less patient, app performance has become a major issue for any company needing to keep the customer’s attention. Need statistical proof? 47% of consumers expect a page to load in two seconds or less, and 40% will abandon a website that takes more than three seconds to load. Three seconds is of course an eternity in the age of 5G and IoT. A LOT can happen in those three seconds.
According to Gartner, the average cost of IT downtime is $5,600 per minute and typically ranges from $140,000 per hour on the low end to $540,000 per hour at the higher end. And that’s not even taking into account the “hidden” costs of downtime, such as erosion of trust—something that can happen especially fast when it comes to financial services applications.
As DevOps teams release and automate with increasing frequency, performance and availability problems have soared, leading to more time troubleshooting and less time developing amazing apps. This means reducing Mean Time to Identify (MTTI) and Mean Time to Resolve (MTTR) is more important than ever, especially in the financial services industry where massive disruption has become the norm.
97% of Logged Errors are Caused by 10 Unique Errors It’s 2021 and one thing hasn’t changed in 30 years. DevOps teams still rely on log files to troubleshoot application issues. We trust log files implicitly because we think the truth is hidden within them. If you just grep hard enough, or write the perfect regex query, the answer will magically present itself in front of you.
