monitoring

Kubernetes: HorizontalPodAutoscaler evaluation based on Prometheus metric

HorizontalPodAutoscaler (HPA) allow you to dynamically scale the replica count of your Deployment based on basic CPU/memory resource metrics from the metrics-server.  If you want scaling based on more advanced scenarios and you are already using the Prometheus stack, the prometheus-adapter provides this enhancement. The prometheus-adapter takes basic Prometheus metrics, and then synthesizes custom API Kubernetes: HorizontalPodAutoscaler evaluation based on Prometheus metric

Python: New Relic instrumentation for Flask app deployed with Gunicorn

Gunicorn is a WSGI HTTP server commonly used to run Flask applications in production.  If you are running these types of workloads in production, you should consider an observability platform such a New Relic to ensure availability, service levels, and visibility into transactions and logging. In a previous article, we created a Docker image of Python: New Relic instrumentation for Flask app deployed with Gunicorn

CloudFoundry: Monitoring the spring-music webapp, Part 5

Cloud Foundry is an opinionated Platform-as-a-Service that allows you to manage applications at scale. This article is part of a series that explores different facets of a Cloud Foundry deployment using the spring-music project as an example. This article is Part 5 of  a series on Cloud Foundry concepts: Deploying the spring-music webapp, Part 1 Persisting spring-music data CloudFoundry: Monitoring the spring-music webapp, Part 5

ELK: Installing MetricBeat for collecting system and application metrics

ElasticSearch’s Metricbeat is a lightweight shipper of both system and application metrics that runs as an agent on a client host.  That means that along with standard cpu/mem/disk/network metrics, you can also monitor Apache, Docker, Nginx, Redis, etc. as well as create your own collector in the Go language. In this article we will describe installing ELK: Installing MetricBeat for collecting system and application metrics

Monitoring: Java JMX exploration from the console using jmxterm

Java JMX (Java Management Extensions) is a standardized way of monitoring Java based applications.  The managed resources (MBeans) are defined and exposed by the JVM, application server, and application – and offer a view into these layers that can provide invaluable monitoring data. But in order to report back the JMX data you must know Monitoring: Java JMX exploration from the console using jmxterm