Operations | Monitoring | ITSM | DevOps | Cloud

A summary is a metric type in Prometheus that can be used to monitor latencies (or other distributions like request sizes). For example, when you monitor a REST endpoint you can use a summary and configure it to provide the 95th percentile of the latency. If that percentile is 120ms that means that 95% of the calls were faster than 120ms, and 5% were slower. Summary metrics are implemented in the Prometheus client libraries, like client_golang or client_java.

When there’s a cardinality explosion, it can cause problems: It’s a surprise, it’s noise, and it can increase your costs or cause performance degradation of your systems. Over the past year, we’ve improved our time series storage systems so that under normal use, high cardinality is no longer an issue. But as the operator of an observability platform, you should have tools you need to help protect that infrastructure.

Back in November 2021, Grafana Labs released version 2.4 of Grafana Loki. One of the new features it included was a Promtail Kafka Consumer that can easily ingest messages out of Kafka and into Loki for storing, querying, and visualization. Kafka has always been an important technology for distributed streaming data architectures, so I wanted to share a working example of how to use it to help you get started.

We’ve talked in previous posts about why we think the concept of exemplars are so valuable: They make it easy to jump from metrics into exactly the right traces, eliminating the needle in the haystack problem. We were enthusiastic enough about the idea that we helped contribute the necessary code changes to bring this functionality to the Prometheus ecosystem.

One of Grafana’s most powerful features is the ability to funnel data from hundreds of different data sources (i.e., services or databases) into a single dashboard without migrating the data from where it lives. You can connect and correlate data from Grafana’s curated observability stack for metrics, logs, and traces, or third-party services, such as Splunk, Elasticsearch, Github, Jira, and many more.

Grafana 8.4 is here! Get 8.4 This release includes a variety of updates focused on making Grafana easier to use, improving performance, and keeping your data secure. For a full list of new features and capabilities, check out our What’s New in Grafana 8.4 documentation. You can get started with Grafana in minutes with Grafana Cloud. We have free and paid Grafana Cloud plans to suit every use case — sign up for free now.

Grafana k6 is our open source tool to help you ship reliable applications by doing performance testing in a modern and developer-friendly way. Performance testing is still unknown to many, but it is not a new topic. In fact, performance testing courses are everywhere — even at colleges and universities. One of our passions is to educate others on the best practices of performance testing, working together with the Grafana k6 community.

At Grafana Labs, we spend a lot of time talking to our customers, and something we’ve heard from people in a wide range of organizations is that they want to be able to better manage sudden spikes in cardinality. Here we will give you a basic overview of what cardinality is and why it’s an important factor in your observability setup, especially when there is a dramatic uptick.

Are your metrics, logs, and traces playing hard to get in your current observability setup? Feel like your on-call messages are left on read? Is the heatmap between your data sources fizzling out on your dashboard?

Do you actually know what your customers are looking for? A way to uncover new business opportunities is to analyze your system, collect what you really need, and visualize it through a comprehensive graph! Log traces are a great place to start because they usually contain useful information on your customers' interests. You just need to transform them.