With continuous integration becoming standard practice, getting full visibility into your CI pipelines has become a key part of monitoring and troubleshooting. Datadog gives you that visibility with out-of-the-box support for several continuous integration tools, including: GitLab, Jenkins, Travis CI, CircleCI and TeamCity. Monitoring your CI servers can help you identify bottlenecks in your pipelines.
I’ve been designing monitoring tools for almost 10 years now, and in that time a lot has changed. The infrastructure we build software on, for example, has been transformed multiple times—moving first from physical hosts to VMs in the cloud, then from VMs to containers, and now from containers to serverless and cloud service-based infrastructure.
In this post, we will show you how you can use AWS CloudFormation to automatically deploy infrastructure that is preconfigured to send metrics and logs to Datadog.
Apache Tomcat is a server for Java-based web applications, developed by the Apache Software Foundation. The Tomcat project’s source was originally created by Sun Microsystems and donated to the foundation in 1999. Tomcat is one of the more popular server implementations for Java web applications and runs in a Java Virtual Machine (JVM).
In Part 1 of this series, we discussed some key Tomcat and JVM metrics that are exposed through Java Management Extensions (JMX). Now that you are familiar with metrics necessary for monitoring Tomcat, we can look at how to collect and query that data.
In Part 2 of this series, we showed you how to collect key Tomcat performance metrics and logs with open source tools. These tools are useful for quickly viewing health and performance data from Tomcat, but don’t provide much context for how those metrics and logs relate to other applications or systems within your infrastructure.
Apache ActiveMQ is message-oriented middleware (MOM), a category of software that sends messages between applications. Using standards-based, asynchronous communication, ActiveMQ allows loose coupling of the elements in an IT environment, which is often foundational to enterprise messaging and distributed applications.
In Part 1 of this series, we looked at how ActiveMQ works, and the key metrics you can monitor to ensure proper performance of your messaging infrastructure. In this post, we’ll show you some of the tools that you can use to collect ActiveMQ metrics. This includes tools that ship with ActiveMQ, and some other tools that make use of Java Management Extensions (JMX) to monitor ActiveMQ brokers and destinations.
As you operate and scale ActiveMQ, comprehensive monitoring will enable you to rapidly identify any bottlenecks and maintain the flow of data through your applications. Earlier in this series, we introduced some key ActiveMQ metrics to watch, and looked at some tools you can use to monitor ActiveMQ.
To build applications in AWS Lambda, you often need to use third party libraries and packages in your function code. Previously, these packages had to be included in a function’s deployment package. Today, Amazon Web Services released a new feature called Layers to simplify this process for Lambda developers. Layers allow you to deploy common components that you can reuse across functions, such as machine learning models, SDKs, or instrumentation libraries.
