Where and Why is Prometheus Used?
Prometheus Kubernetes is a monitoring tool that can be deployed on AWS, Azure, or GCloud Kubernetes clusters. It is considered as an essential tool in modern infrastructure. Modern DevOps is becoming more complex to handle manually and therefore needs more automation, so you typically have multiple servers that run containerized applications.
There are hundreds of different processes running on that infrastructure where all entities are interconnected, so maintaining such a setup to run smoothly and without application downtimes, are very challenging. Imagine having such a complicated infrastructure with loads of servers distributed over many locations, and you have no insight into what is happening on the hardware level or the application-level like errors, response, and latency.
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Hardware down or overloaded may be running out of resources in such complex infrastructure, but more things can go wrong when you have tons of services and applications deployed. Any one of them can crash and cause other services’ failure, have so many moving pieces, and suddenly the application becomes unavailable to users. You must quickly identify what precisely out of these hundred different things went wrong, which could be difficult and time-consuming when debugging the system manually.
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Some Use Cases for using Prometheus Monitoring
For example, say one specific server ran out of memory and kicked off a running container that was responsible for providing database sync between two database pots in a Kubernetes cluster. That, in turn, caused those two database pots to fail. That database was used by an authentication service that also stopped working because the database is unavailable.
The application that depended on that authentication service couldn’t authenticate users in the UI anymore, but from a user perspective, all you see is an error in the UI. When you don’t have an insight into what’s going on inside the cluster, you don’t see that red line of the chain of events as displayed here; you just see the error. So now you start working backward from there to find the cause and fix it. But what will make this searching problem process more efficient? You could use a tool that continually monitors whether services are running and alerts popping as soon as one service crashes.
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You know exactly what happened, or even better, it identifies problems before they even occur and alerts the system administrators responsible for that infrastructure to prevent that issue. For example, in this case discussed, it would regularly check the status of memory usage on each server. When on one of the servers, it spikes over, for example, 70% for over an hour or keeps increasing, it notifies about the risk that the memory on that server might soon run out.
Or let’s consider another scenario where you stop seeing logs for your application because the elastic search doesn’t accept any new logs as the server ran out of disk space or elastic search reached the storage limit that was allocated for it. The monitoring tool would check the storage space continuously and compare it with the elastic search consumption of space of storage. It will see the risk and notify the maintainer of the possible storage issue.
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You can tell the monitoring tool what that critical point is when the alert should be triggered. If you have a critical application that absolutely can have any log data loss, you may be very strict and once take measures as soon as fifty or sixty percent capacity is reached. Adding more storage space will take a long time because it’s a bureaucratic process in your organization, where you need the approval of some IT department and several other people.
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You also want to be notified earlier about the possible storage issue so that you have more time to fix it. Or a third scenario where application suddenly becomes too slow because one service breaks down and starts sending hundreds of error messages in a loop across the network, which creates high network traffic and slows down other services to have a tool that detects such spikes in a network.
Kubernetes Service discoveries exposed to Prometheus
Main Component: Prometheus Server
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The architecture of Prometheus Kubernetes
One of the important characteristics of Prometheus Kubernetes is that it is designed to be reliable even when other systems have an outage. You can diagnose the problems and fix them. Hence each Prometheus server is self-contained, meaning it doesn’t depend on network storage or other remote services.
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It’s meant to work when other parts of the infrastructure are broken, and you don’t need to set up an extensive infrastructure to use it. However, it also has the disadvantage that Prometheus can be difficult to scale. So, when you have hundreds of servers, you might want to have multiple Prometheus servers that aggregate all these metrics data.
Configuring and scaling primitives in that way can be very difficult because of these characteristics. So, while using a single node is less complex, and you can get started very easily, it limits the number of metrics that Prometheus can monitor. To work around that, you either increase the Prometheus server’s capacity so that it can store more metrics data or limit the number of metrics that Prometheus collects from the applications to keep it down to only the relevant ones.
In-Demand Software Development Skills
You can upskill your knowledge on such topics by doing cloud computing courses on platforms like upGrad, Udemy, Coursera, etc. as this monitoring tool can be deployed on the cloud. Especially with upGrad, the courses are designed by one of the highly reputed institutions of our country IIIT-B. This will give you hands-on experience and a broader knowledge aspect.
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Kubernetes simplifies the deployment, scaling, and management of containerized applications and microservices. This assists with keeping administrations going, yet to recognize and resolve hidden issues like a slow execution, you need the capacity to accumulate and imagine top to bottom foundation application and execution information from over your condition.
Not approaching continuous data, alongside relevant information, makes it almost difficult to correspond to your condition measurements so you, too, can tackle issues more rapidly.
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