Here at Grafana Labs, when we’re building integrations for Grafana Cloud, we’re often thinking about how to help users get started on their observability journey. We like to focus some of our attention on the different technologies you might come across along the way. That way, we can share our tips on the best ways to interact with them while you’re using Grafana Labs products.
Today, I am excited to introduce the NSQ integration available for Grafana Cloud, our platform that brings together all your metrics, logs, and traces with Grafana for full-stack observability. NSQ is a real-time distributed messaging platform designed to operate at scale, handling billions of messages per day. It’s a simple and lightweight alternative to other message queues such as Kafka, RabbitMQ, or ActiveMQ. This will walk you through how to get the most out of the integration.
Ever since a JPEG created by the digital artist Beeple sold for more than $69 million in 2021, the worldwide obsession with NFTs (non-fungible tokens) that represent digital collectibles, art, and media has been growing. A company at the forefront of the NFT world is the blockchain gaming studio Dapper Labs, which leverages blockchain to build addictive games (such as CryptoKitties), verify authentic digital collectibles, and run fan tokens for sports personalities and music artists.
In the freshly released Grafana 8.4, we’ve enabled the full-range log volume histogram for the Grafana Loki data source by default. Previously, the histogram would only show the values over whatever time range the first 1,000 returned lines fell within. Now those using Explore to query Grafana Loki will see a histogram that reflects the distribution of log lines over their selected time range.
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.
