Unified monitoring view in Kubernetes : Linking infrastructure monitoring with application monitoring

Introduction

Typically you notice that there are two dimensions / viewpoints when it comes to monitoring. On one side there is a team that wants to view everything related to the “infrastructure”, like for instance the kubernetes cluster. On the other hand, there is the typical application performance monitoring that starts from the application side. Sadly enough, in a lot of cases, those two are separated islands… 😦

 

As you might know, on the Azure front you can do Application Performance Monitoring with Application Insights and there is like a really awesome integration with Azure Monitor (“Log Analytics”) from the container space (kubernetes). Though I see you thinking it… Two separate solutions. Though, what a lot of people forget, is that they are actually using “Log Analytics” under the hood. And… That you can query across workspaces in Log Analytics! Which means that you can join the two and have an aggregated view to span both worlds.

 

Let’s take a look!

For this test, I’ve created a k8s cluster which is linked to a separate log analytics work-space. Where next to it, there is an application (Azure Function) inside of a docker container that is linked to Application Insights.

 

Aggregating Metrics from multiple Azure Storage Accounts

Introduction

When working at scale, the only way to properly handle true scale is to work with horizontal scaling options. Some services (like CosmosDBCosmosDB for instance) do this out of the box and abstract it away from the user/customer. Though sometimes this is something you need to facilitate yourself… In terms of Azure Storage, we’re very open in regards to our limitations. For example, at this point in time, we’re currently facing a maximum egress of 50Gbps per storage account. Where this is more than enough for a lot or customers, at times we need to scale beyond this. Here the solution at hand is to see the storage account as a “scale unit”, and use it for horizontal scaling. So if you need 200GBps, then you can partition your data across four storage accounts.

In today’s post, we’re going to take a look at how you can aggregate these metrics into a single pane of glass. Because, at the end of the day, your operations team does not want to have a disaggregated view of all the components in play.

 

Important Note

All Azure teams are constantly looking to evolve their services. Please note that the limits mention in this post are linked to the point in time when the article was written. As many of you know, Azure keeps evolving at vast pace, so the limits might already have been changed. If you are wondering, always check the following page for the most current limits that are linked to GA (“General Available”) services!

Azure Serverless Performance / Cost Monitoring – Retrieving CosmosDB Request Charges per Function

Deploying OMS for Docker via Rancher

Introduction
Today we’ll be deploying Microsoft Operations Management Suite (OMS) for Docker via Rancher… Sound cool? It is! Basically we’re going to do the following guide and add Rancher to the twist.

For those unfamiliar with the Microsoft offering and more knowledgeable  in the OSS community. Imaging OMS as being the Microsoft counterpart of a typical ELK stack. The advantage is that it’s managed and that there are already a lot of integrations possible.

Datadog on Raspberry Pi

Introduction

Datadog is one of the more popular cloud monitoring tools in the DevOps community. It is not as strong in the APM suite as a NewRelic, though it is a very nice product. The reason I’m trying it today, is because I love my raspberry pi’s… and NewRelic does not support ARM. 😦