Operations | Monitoring | ITSM | DevOps | Cloud

A few months ago, I wrote an in-depth article describing how labels work in Loki. Here, I’m consolidating that information into a more digestible “cheat sheet.” There are some big differences in how Loki works compared to other logging systems which require a different way of thinking. This is my attempt to convey those differences as well as map out our thought process behind them. As a Loki user or operator, your goal should be to use the fewest labels possible to store your logs.

Metrics help you understand how your business and applications are performing. Longer metric retention enables quarter-over-quarter or year-over-year analysis and reporting, forecasting seasonal trends, retention for compliance, and much more. We recently announced the general availability (GA) of extended metric retention for custom and Prometheus metrics in Cloud Monitoring, increasing retention from 6 weeks to 24 months. Extended retention for custom and Prometheus metrics is enabled by default.

Java is a well-established object-oriented programming language that epitomizes cross-platform software development and helped to popularize the “write once, run anywhere” (WORA) concept. Java runs on billions of devices worldwide and powers a huge range of important software, such as the popular Android operating system and Elasticsearch. In this tutorial, we will go over how to manage Java logs with the ELK Stack and Logz.io.

In our blog post on structuring Elasticsearch data with grok on ingest for faster analytics, we took a look at how to structure unstructured data on ingest (schema on write) to make sure your analytics run at near real time. Speed like that can help take your observability use cases to the next level. In this article, we’re going to build on what we learned by incrementally creating a new grok pattern from scratch!

Open source grafana is one of the most popular OSS UI for metrics and infrastructure monitoring today. Capable of ingesting metrics from the most popular time series databases, it’s an indispensable tool in modern DevOps. This OSS grafana tutorial will go over installation, configuration, queries, and initial metrics shipping. Open source grafana is the equivalent of what Kibana is for logs (for more, see Grafana vs. Kibana).

Thousands of teams use SolarWinds® Papertrail™ to manage different types of logs. And with such a large and diverse group of users, there’s a wide variety of needs and preferences. Fortunately, we added a Display Preferences menu to the footer in the new Papertrail event viewer, allowing us to create and deliver new display options and color themes. If you’ve opened the Display Preferences menu this week, you may have noticed two new color themes: Solarized and Solarized Light.

Being able to easily monitor the health of all your sites and services from multiple global locations is a powerful tool for site reliability. However, no one wants to sit and stare at a status dashboard all day. Naturally, teams want to be alerted when there is an issue. We can do that with alerting in Kibana. And when coupled with Elastic machine learning, alerts can be automatically generated from anomalies that are automatically detected. That’s the power of Elastic Observability.

We examined best practices for logging in a prior series. However, how can you apply those best practices in real life? Let’s dive into how you could use LogDNA in an opinionated manner to utilize best practices to bring value to your DevOps-focused projects. How can we ensure we follow best practices and keep our logs secure and compliant as noted in the previous series? Let’s pretend we’re setting up centralized log management with LogDNA for a new team and project.

Security teams can’t defend what they can’t see. As organizations move more workloads to the cloud, security teams need added visibility into these new workloads or risk having blind spots that lead to compromise. In the first installment of our "Getting Data In" webinar series, "Modernizing your SOC for the Cloud Age Starts with Security Foundations," we demonstrate how to quickly and easily onboard data into Splunk Cloud.

Logs in continuous delivery pipelines are often entirely ignored, right up until something goes wrong. We usually find ourselves wishing we’d put some thought into our logs, once we’re in the midst of trawling through thousands of lines. In order to try to prevent this, we can add DevOps metrics into our logs, which will provide us with greater observability, and give insight into anything going wrong in our pipelines.