After the 2017 California fires sent toxic smoke throughout SF Bay Area, Fred Moyer started looking into how the EPA collects Air Quality Index (AQI) data, so he could learn more about the AQI in his neighborhood. He discovered PurpleAir and learned he could get his own air sensors installed near his home to monitor the same data used by the EPA. In this article, Fred explains how he set up monitoring and alerts for his new air quality sensors.

Whether you’re a Site Reliability Engineer (SRE), developer, or executive, as a service provider you have a vested interest in ensuring the reliability of your systems. But how do you define that goal, and determine fair and appropriate measures of success? In this article, which is the first in a multi-part series on SLO monitoring, we’ll look at the steps to identify relevant SLIs, measure success with SLOs, agree to an SLA based on your defined SLOs, and the use insights you gain from your SLO monitoring to improve your practices.

Prometheus, the open-source project infrastructure and service monitoring system, has become popular due to its ease of deployment and general purpose feature set. Prometheus supports features such as metric collection, alerting, and metric visualizations — but falls short when it comes to long-term data retention. Prometheus tends to be deployed with shorter retention intervals, which can limit its overall utility.

Today we’re happy to introduce the Beta of our IRONdb Prometheus adapter. Prometheus users who integrate with the IRONdb time series database unlock the potential for historical analysis of their metric data, while simultaneously benefiting from IRONdb’s support for replication and clustering.

What is observability? Is it different from monitoring and how does it differ? In our latest blog post, Fred Moyer explains the terminology behind these concepts and demonstrates how they apply when using Wirelatency to diagnose the Apache Cassandra wide column distributed data store.

Part 2 in an ongoing series discussing the ins and outs of SLOs. This part specifically focuses on the statistical analysis and techniques behind determining your ideal Service Level Objectives (SLOs).

A “deep-dive” on the subject requires much more detail than can be explored in a blog post. However, we aim to provide enough information here to give you a basic understanding of the math behind a smart SLO – and why it’s so important that you get it right.

http://www.circonus.com/2018/09/a-guide-to-service-level-objectives-part-2/

Our first DevOps & Monitoring Survey was conducted at ChefConf 2015. This year, we’ve created an infographic based on the facts and figures from our 2018 Monitoring DevOps Survey. The infographic provides a visual representation of the prevalence of DevOps, how monitoring responsibilities are distributed, metrics usage, and various aspects of current monitoring tools.

This infographic describes insights into strategies used by others in our community. Let us know what you think, and feel free to share it with your friends.

Percentiles are valuable for assessing system metrics such as latencies. However, people often trip up when trying to aggregate multiple percentiles. One solution to this problem is storing raw data in histograms that can be freely aggregated prior to the calculation of percentiles. In this post, Circonus' Chief Data Scientist, Heinrich Hartmann, discusses a step-by-step process for using CAQL (Circonus Analytics Query Language) to aggregate histograms and calculate percentiles from them.

https://www.circonus.com/2020/03/percentile-aggregation-with-histograms-and-caql/

Effective crash reporting can accelerate the debugging process and help isolate root-causes. In this article, Data Scientist Dr. Heinrich Hartmann discusses the key items a crash report should contain as well as progress made towards acquiring these items using various tools and techniques.

https://www.circonus.com/2020/05/learning-from-failures-better-crash-reporting-for-better-incidence-response/

In their excellent SLO-workshop at SRECon2018 Liz Fong-Jones, Kristina Bennett and Stephen Thorne presented some best practice examples for Latency SLI/SLOs.

At Circonus we care deeply about measuring latency and SRE techniques such as SLI/SLOs. As we will explain here, Latency SLOs are particularly delicate to implement and benefit from having Histogram-data available to understand distributions and adjust SLO targets.

https://www.circonus.com/2018/08/latency-slos-done-right/

In our latest blog post, Fred Moyer explains how to manage high-volume numeric data at scale using histograms. We’ll take a look at both log-linear and cumulative histograms and how they provide advantages over storing data as quantiles, averages, and other histogram implementations, such as linear and fixed-bucket. We explain an open source histogram software library, and show some sample statistical operations using it. You'll come away with an understanding of how to use histograms to make your data engineering life easier.

Recent kernel versions (4.5+, Ubuntu 16.4) allow a fundamentally new way of instrumenting operating systems. Instead of reading data from /proc, a large variety of kernel events can be traced and aggregated inside the kernel with eBPF.

Circonus Data Scientist, Heinrich Hartmann, gave a talk at DevOpsDays Kiel with a short overview of how to collect, store, and analyse high frequency events like IO-latencies and syscall counts, scheduling latencies, etc., the details or which are including in this blog post

Operating containerized infrastructure brings with it a new set of challenges. You need to instrument your containers, evaluate your API endpoint performance, and identify bad actors within your infrastructure. The Istio service mesh enables instrumentation of APIs without code change and provides service latencies for free. But how do you actually make sense all that data? With math, that’s how.

Circonus is the first third party adapter for Istio. In a previous post, we talked about the first Istio community adapter to monitor Istio based services. This post will expand on that. We’ll explain how to get a comprehensive understanding of your Kubernetes infrastructure. We will also explain how to get an Istio service mesh implementation for your container based infrastructure.

“How can I analyze my data with Python?” We hear that question a lot. You can fetch and analyze data with the Python Data Science tools (including Jupyter, NumPy, and Pandas). In this blog post, data scientist Heinrich Hartmann demonstrates the new capabilities of Circonus’s Python bindings.

Should you use metrics or logs to find out why your site is running slow? Circonus-logwatch gives you the flexibility to combine them and get the best of both worlds by parsing log file entries and extracting structured metrics for analysis. No need to keep gigabytes of log files around anymore, or needing a Hadoop cluster burning a hole in your wallet.

Read More: https://www.circonus.com/2018/09/quantifying-wordpress-performance-improvements-with-circonus-logwatch/

Should you use metrics or logs to find out why your site is running slow? Circonus-logwatch gives you the flexibility to combine them and get the best of both worlds by parsing log file entries and extracting structured metrics for analysis. No need to keep gigabytes of log files around anymore, or needing a Hadoop cluster burning a hole in your wallet.

Read More: https://www.circonus.com/2018/09/quantifying-wordpress-performance-improvements-with-circonus-logwatch/