We often say that Honeycomb helps you find a needle in your haystack. But how exactly is that done? This post walks you through when and how to visualize your data with heatmaps, creating a log scale to surface data you might otherwise miss, and using BubbleUp to quickly discover the patterns behind why certain data points are different.
Sometimes an organization gets bogged down with the details. It happens. You have all of this fantastic data in SCOM, and you’re trying to share it, but your users don’t care. That’s not true. They care, but what they don’t care about is the server. To put it another way, they care if the service or application they depend on is working. But here’s the catch, you can’t do this in SCOM.
Snowflake offers a cloud-based data storage and analytics service, generally termed “data warehouse-as-a-service.” The main benefit of Snowflake is that you pay for compute and storage that you “actually use,” so it’s not “just another database.” Snowflake has become very popular over the last few years, culminating in a huge IPO just a couple of weeks ago, by allowing enterprise users to affordably store and analyze data using cloud-based hardware and software
Once your monitoring is operational for a while, it becomes evident that infrastructure monitoring alone is not enough. Sure, SCOM is excellent when focused on an infrastructure level problem. Do you have an alert that your Windows server is running out of space? Check. Can you check to see if your SQL Server has had a lot of deadlocking recently? Check. Do you know if your Linux server is out of swap space? Can you report on how fast it has been running out? Check.
Most of us have learned the hard way that it’s usually cheaper to fix something before it breaks and needs an expensive emergency repair. Because of that, I like to keep track of what’s happening in my house so I know as early as possible if something is wrong. As part of that effort, I have a temperature sensor in my attic attached to a Raspberry Pi, which Prometheus scrapes every 15 seconds so I can view the data in Grafana.
If you’ve been following the blog updates on the development of Cortex – the long-term distributed storage for Prometheus – you surely noticed the recent release of Cortex 1.4, which focuses on making support for “blocks engine” production-ready. Marco Pracucci has already written about the blocks support in Cortex, how it reduces operational complexity for running Prometheus at massive scale, and why Grafana Labs has invested in all of that work.
If you’re building applications and running services in Microsoft Azure, SquaredUp for Azure is a presentation layer that sits on top of the Azure APIs enabling you to produce effective, informative and engaging dashboards.
A few months ago, I blogged about memory-mapping of full chunks of the head block from disk. The feature, which was introduced in Prometheus v2.19.0, brings down memory usage and restart time. Additionally, there’s another Prometheus feature in progress that snapshots in-memory data during shutdown for faster restarts; it’s expected to cut down the restart times by a big factor.
As you add more panels to your dashboard, more requests are being made, potentially leading to your dashboard taking longer to load. While you can limit the data requested in each query, one of the best ways to reduce the loading time is to reduce the number of requests being made to the data source. Grafana makes a data source query for each panel in your dashboard, even if those queries are identical.
