Operations | Monitoring | ITSM | DevOps | Cloud

In this article, we are going to look at how to monitor Redis performance using Prometheus. This will allow Redis Administrators to centrally manage all of their Redis clusters without setting up any additional infrastructure for monitoring. To follow the steps in this blog, sign up for the MetricFire free trial, where you can use Graphite and Grafana directly on our platform.

Monitoring distributed systems like MongoDB is very important to ensure optimal performance and constant health. But even the best monitoring tool will not be efficient without fully understanding the metrics it gathers and presents, what they represent, how to interpret them, and what they affect. That’s why it is crucial not only to collect the metrics but also to understand them.

Why do some organizations excel in streamlining their database operations and applications development while others find it immensely challenging? Why can some database teams embrace agility while others take months of cycles to deploy even a single line of code? What secret sauce can allow some database teams to work smarter (not harder), streamline database development lifecycles better, get to deployment faster, and create an overall stronger alignment across departments?

In the last few years, the usage of databases that charge by request, query, or insert—rather than by provisioned compute infrastructure (e.g., CPU, RAM, etc.)—has grown significantly. They’re popular for a lot of the same reasons that serverless compute functions are, as the cost will scale with your usage. No one is using your site? No problem: you’re not charged.

Unlike SQL, which was developed in the 1970s to limit data duplication, NoSQL is a relatively new type of database. NoSQL came about in response to increasing amounts of data, and it uses a distributed system to help organize large amounts of structured and unstructured data. NoSQL is popular in business tech and other industries, with large organizations such as Amazon, Google, and LinkedIn using NoSQL databases.

I was recently joined for a live webinar by Tony Maddonna (Microsoft Platform Lead, Enterprise Architect & Operations Manager at BMW) and Hamish Watson (DevOps Alchemist at Morph iT) to discuss their experiences with digital transformation and the impact it had on them, their teams and the wider organization.

At ObservabilityCON in New York City today, we announced a new open source backend for continuous profiling data: Grafana Phlare. We are excited to share this horizontally scalable, highly available database with the open source community — along with a new flame graph panel for visualizing profiling data in Grafana — to help you use continuous profiling to understand your application performance and optimize your infrastructure spend.

This post is part 1 of a 3-part series about monitoring MongoDB performance with the WiredTiger storage engine. Part 2 explains the different ways to collect MongoDB metrics, and Part 3 details how to monitor its performance with Datadog. If you are using the MMAPv1 storage engine, visit the companion article “Monitoring MongoDB performance metrics (MMAP)”.