In Part 1 we’ve described what container monitoring is and why you need it. Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and the applications they run. These servers and applications running on them are typically very static, with very long uptimes.
Even though containers have been around for ages, it wasn’t until Docker showed up that containers really became widely adopted. Docker has made it easier, faster, and cheaper to deploy containerized applications. However, organizations that adopt container orchestration tools for application deployment face new maintenance challenges.
Working with Java applications has a lot of benefits. Especially when compared to languages like C/C++. In the majority of cases, you get interoperability between operating systems and various environments. You can move your applications from server to server, from operating system to operating system, without major effort or in rare cases with minor changes.
In this tutorial, we will talk about how different Java Garbage Collectors work and what you can expect from them. This will give us the necessary background to start tuning the garbage collection algorithm of your choice. Before going into Java Garbage Collection tuning we need to understand two things. First of all, how garbage collection works in theory and how it works in the system we are going to tune.
When working with Java or any other JVM-based programming language we get certain functionalities for free. One of those functionalities is clearing the memory. If you’ve ever used languages like C/C++ you probably remember functions like malloc, calloc, realloc and free. We needed to take care of the assignment of each byte in memory and take care of releasing the assigned memory when it was no longer needed.
Big data, AI, machine learning, and numerous others are all buzzwords we seem to throw around lightly in recent years. Even though they are hugely different from one another, they all have one thing in common. Data! Huge amounts of data that need to be managed. The downside of that is that the more data you have the more of a headache it is to store, query, and make sense of.
Elastic Stack Features (formerly X-Pack) is an Elastic Stack extension that bundles security, alerting, monitoring, reporting, and graph capabilities. One could use either all or specific components. People love Splunk. But not its price. So people are always on a lookout for a good Splunk alternative. Many of those people migrated from Splunk to ELK Stack or Hosted ELK Stack solutions like Logsene.
Logging or monitoring? If you deploy and manage an application, these are the two key techniques available to you for helping to ensure that the application meets availability and performance expectations. One of them is Application Performance Management, or APM, though you can also find it referred to as ‘Application Performance Monitoring’ or simply ‘monitoring’. The other is log analytics and management or just ‘logging’.
When it comes to Java applications, to make sure they run at peak performance, it’s critical to close the resource gap between the code and the virtual machine it’s running on – if there is one. The way to do that is by peaking into and fine-tuning the Java Virtual Machine (JVM). However, that’s easier said than done. Many factors can influence JVM performance, and during tuning, you must consider all of them. Though, there are ways around that to make it not be such a pain.
Kubernetes is gaining popularity every day. Using an Ingress controller is the preferred method of allowing external access to the services in a cluster. This makes ingress logs incredibly important for tracking the performance of your services, issues, bugs, and even the security of your cluster.
