Application performance metrics
Application performance metrics (APMs) is a technology that lets you observe application metrics such as resource usage, latency, performance speed, etc, which is very helpful in finding out why you're application is running slow, or why some bug is occurring. This ultimately helps developers improve their applications and makes it easier and less time-consuming to find and fix bugs.
While APMs have been around for nearly a decade, it has become difficult to integrate them into modern infrastructure as workloads are getting bigger, and spread across multiple environments. This makes it difficult to integrate APMs and scale them. These solutions offer a full-blown tracing system or nothing at all which makes it very expensive. Imagine having 5 distributed Kubernetes environments, each running nearly 500 nodes. This kind of scale can easily raise costs and it becomes difficult to implement APMs.
Estimated at over $50 billion, observability is one of the largest and fastest-growing markets in infrastructure software, with teams willing to allocate up to 10% of their IT spending to observability. Giant companies have been leading the APM sector, yet due to growing data volumes and intricate technology stacks, the cost has risen and these solutions have become hard to integrate and demanding to maintain. The result is clear: over 70% of teams do not have an APM tier in place
While speaking in a Kubernetes context, we usually have our observability solution working at a cluster level which can get difficult to scale when you have hundreds of nodes or a distributed environment.
What if your observability solution worked at a kernel level? You would then be able to view every single process going on in your Linux operating system. That includes everything in your Kubernetes cluster. This is where eBPF comes into the picture.
eBPF is a popular emerging technology used to safely and efficiently extend the capabilities of the kernel without requiring to change kernel source code or load kernel modules. eBPF was first introduced in 2014 and allows programs to run directly in an isolated virtual machine inside the Linux kernel. In the last 24 months, eBPF has evolved to solve new use cases, becoming the next great promise in fields like network infrastructure, security, and observability. Check out this article to learn more about eBPF and how they work.
groundcover is a K8s application monitoring solution that reinvents the domain with eBPF. Built for modern production environments, it covers everything yet stores only what matters, allowing teams to scale away without worries. It utilizes eBPF to provide deep Kubernetes observability, using it to trace any type of event – from network and infrastructure, all the way to services and applications running in the user space.
By using eBPF to collect observability data straight from the Linux kernel, groundcover requires no R&D efforts in the process. Together with a unique edge-compute approach to collecting data efficiently, groundcover covers everything yet stores only what matters. The result is super-granular yet scalable visibility into what’s really happening inside a Kubernetes cluster.
groundcover exposes the root cause of the crash instantly by monitoring 100% of the production stack covering every application, legacy code, sidecar, or 3rd party component, with no blind spots. It taps into all application logs, metrics, traces, and Kubernetes events with zero code changes and instantaneous integration.
What makes it Unique
groundcover covers everything in your cluster yet stores only important details that matter. taps into all application logs, metrics, traces, and Kubernetes events without the need to change any code and integrates instantly. It also breaks the visibility-cost tradeoff, ensuring teams don’t have to compromise on visibility depth to manage budgets responsibly. groundcover offers a robust free tier from day one, clients get the most out of APM at a fraction of the existing cost in the market today.
Unlike monitoring tools such as Datadog, which require instrumentation of Kubernetes applications, groundcover installs an agent via a Daemonset on Kubernetes clusters and uses information flowing out of the Kubernetes API to put that data in context for each application.
groundcover's agent also performs some analytics at the cluster level and stores the data within your cluster. This reduces traffic on the network, trims the amount of data that must be stored for long-term analysis, and prioritizes alerts according to criticality.
Observability tools have become a necessity for monitoring your application status, performance, and other metrics. However, with large and distributed environments, it becomes difficult to scale an observability solution. Another pain point is that the solutions themselves can become very expensive for large workloads.
groundcover aims to utilize the power of eBPF and implement observability in your Kubernetes cluster, without actually needing any instrumentations of Kubernetes applications and observers everything, but gives you only the metrics which you need.