App usage has escalated for the last ten years, the same for consumers and businesses. Not only are apps used more frequently in every aspect of their daily lives, but a far broader range of them are also used to serve different functions. This does not affect too much – app management is just simple such as adding/removing from a desktop or smart devices, for individuals. But it is far more complex for a greater number of users and larger amount of data.

To troubleshoot and optimise their infrastructure, many enterprises are turning to application performance monitoring (APM) platforms. But in fact, many APM tools are overwhelmed by the complexity of the issue, and useless in providing the actionable insights that organisations need.

Composing enterprise applications

It is composable for enterprise applications in modern days — which are built from individual containers and co-ordinated by platforms across local and cloud environments. In particular, individual containers and the servers can be spun up or down elastically as needed. This makes the app flexible and efficient in resource use, even incredibly complex applications with thousands of components.

Actually, what looks to the user like a single transaction can actually involve millions of nesting method called behind the scenes. Since there are many components where something might go wrong in the transaction execution course, when solving the transactions, you need to check all of those nodes, because the problem could happen anywhere.

It is often difficult for APM tools to handle the ‘three V-s’ consequently – the volume of data related to the speed where it flows through the application, and the variance of metadata in individual transactions.

An unfinished symphony

Some popular tricks are used to break the limitations of these application performance management methodologies. For example, it is impossible to instrument all platforms to tens of thousands of components. Or only monitoring components capture data based on trigger if a trigger condition is met.

But the disadvantage of this technique is that a complete picture is not brought. A pivotal problem might be missed in the sampled transactions with application performance. If components are instrumented particularly, it often take time and effort for the deep layers of the app where the root problems lie are ignored, and figuring out the inner cause, which causes a constant trade-off between data quality and scale. And it needs a very intimate knowledge of coding to decide which components concern the specific issue.

Fine-tuning APM

Fortunately, you can avoid this trade-off. Sampling is no more needed in Best-in-class APM solutions. Alternatively, they can capture everything, even the volume, with clustered architecture that delivers a 10x ability to scale. Tens of thousands of agents capturing billions of transactions per day can be supported with an analysis server, while logging and storing every app transaction, along with system metrics at a half intervals, with all of the relevant metadata, down to the deepest levels of the call stack.

But the crucial importance of ability of sunning APM at scale is crucial because of the real-world business benefits it provides:

This causes faster mean time to resolution, proactively resolved production problems, and proper priority given to high-value efforts. Without blind spots, business decision makers can make sure that mission-critical technology performance as organisations scale. Sequentially, departments and employees can maximise productivity and keep up in a challenging and competitive digital landscape.