Your app is done and the client is ready to launch! Everything looks great. But how can you ensure that you will achieve your SLA for uptime? How are you tracking revenue growth (& optimizing)? Do you know how many users you have, and what they’re doing at any given moment in the app? What you need is a monitoring platform that will be able to track these different types of data in one place.

An infrastructure, especially if it is scalable, can become extremely complex to visualize and observe. If something goes wrong, it would be difficult to fully understand the problem without a great data monitoring strategy. Information related to CPU, RAM, and statistics about SSH or HTTP servers are critical to understanding the performance of your web-application.

You’re ingesting 20,000 data points a second, in 400,000 metrics, from thousands of AWS instances – and your monitoring can’t handle the load. You need a scalable, highly-available monitoring and dashboarding solution (and you need it yesterday). Should you do it yourself with an in-house Graphite or Prometheus monitoring system? Or will you skip the headache and choose a hosted service like MetricFire?

This article will focus on using fluentd and ElasticSearch (ES) to log for Kubernetes (k8s). This article contains useful information about microservices architecture, containers, and logging. Additionally, we have shared code and concise explanations on how to implement it, so that you can use it when you start logging in your own apps. ‍Useful Terminology.

Visualization and Analytics are two of the most important aspects of monitoring without which the entire data stack could just mean NOTHING. Considering the plethora of graphing tools available in the market today, selecting the right one could often be confusing. In our blogs at Metricfire, we break down the best tools in terms of their setup efforts, usage, market competitors, pricing models, best practices, and many more. Grafana and Chronograf both are among the best tools available today.