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To integrate StatsD and circleci with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating StatsD and circleci and how that can support your monitoring system.
StatsD is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers. StatsD sends this information over UDP or TCP from your server or network to your monitoring server. In most cases, your StatsD will send aggregates to one or more pluggable backend services (e.g., Graphite).
StatsD is considered a “collector” in the monitoring world. It’s key job is to find and pull the metrics from your systems, and then push them to a monitoring server. However, it doesn’t store, aggregate, visualize or alert on any of your metrics. StatsD is intended for only one use-case: collecting.
The key benefit of using StatsD is the customizability. Unlike standard Agents, you can tailor StatsD to do exactly what you want. You can map out hundreds of metrics, give the metrics customized names, and have them sent into your monitoring backend.
Typically, StatsD pairs with Graphite as the monitoring backend. The primary reason for this is that Graphite does not come with its own collector.
However, StatsD is also often used to pull metrics in unique or challenging cases, when something like a typical monitoring agent, or Prometheus’ built-in metrics scraping tool isn’t able to collect what you’re looking for. For example, StatsD is often used for monitoring Python web apps with Prometheus.
StatsD can be used to pull metrics from every part of the service that is running, and enables this data to be easily summed to get a clear picture of what your data is doing.
StatsD is also used to combine metrics from multiple sources into one location, such as pulling Graphite metrics into Prometheus.
At MetricFire, you can integrate with StatsD using MetricFire’s StatsD Add-on. You can automatically pull your StatsD metrics into our monitoring platform, and see your StatsD metrics alongside your AWS and Graphite monitoring.
CircleCI is a continuous integration and delivery platform (CI/CD) that you can install in a private cloud or data centers.
After you authorize a software repository, such as GitHub or Bitbucket, as a project on circleci.com, every code change you make triggers automated tests in a clean container or VM. After the tests are complete, CircleCI notifies you via email of successes and failures.
You can configure CircleCI to deploy code to various environments, including AWS CodeDeploy, AWS EC2 Container Service (ECS), AWS S3, Google Kubernetes Engine (GKE), Microsoft Azure, and Heroku. You can deploy other cloud services using SSH or by installing the service's API client with your job configuration.
System administrators can gather metrics for monitoring their CircleCI installation for various environment variables, including installed Nomad clients and Docker metrics. Metrics collected by CircleCI include basic information, such as CPU or memory usage, and more advanced metrics, such as the number of executed builds or the number of internal errors. By recording and analyzing metrics, you can quickly detect incidents and abnormal behavior, retroactively understand infrastructure-wide issues, and scale computing resources dynamically.
With MetricFire, you can turbocharge your CircleCI monitoring to a new level. One useful method of monitoring the changes in your codebase is by adding annotations whenever your tests run. For example, by adding CircleCI annotations to your MetricFire graphs using a webhook, you can keep track of which passed tests have affected your system performance (negatively or positively). This method can help you prioritize what to fix first, as a highly negative impact should have top priority.
To integrate StatsD and circleci with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate StatsD and circleci and get StatsD and circleci interacting with your MetricFire dashboards directly.
MetricFire is a full-scale platform that provides infrastructure, system, and application monitoring using a suite of open-source monitoring tools. We enable you to use Hosted Graphite and aesthetic custom dashboards to visualize your metrics so you can understand what is happening.
MetricFire offers users a complete ecosystem of end-to-end infrastructure monitoring, comprised of popular open-source monitoring software services: Graphite and popular dashboards. Plugins for many other open-source projects are preconfigured, such as StatsD, collectd, and Kubernetes. You get all these within a hosted environment as a single product. Not only does MetricFire fit well into the infrastructure monitoring use-case, such as network monitoring and server monitoring, but we also do application monitoring and business intelligence.
Through this hosted environment, MetricFire boosts the unique features of open-source projects to give you more functionality than the original products. Below are some of the MetricFire features at a glance:
The key thing to remember is that Hosted Graphite by MetricFire is more than just Graphite. Our Hosted Graphite product actually adds data dimensionality and better data storage.
The benefits of MetricFire are:
“We now have over ten times the amount of metrics we started with, and on different accounts. One of the great things about MetricFire is that scaling to support this increase has been hassle-free, requiring no additional work on our side.”
“Building and managing an on-premise installation at this scale would require a lot of engineer time, especially in the first year...we use this engineering time to work on initiatives closer to our core business”
“There’s complete transparency with everything MetricFire do which means we can accurately predict what we’ll be spending and comfortably keep within our budget.”
"Every time I have a question, I get an answer from support after just a couple of hours. Their technical knowledge is excellent.”
In this article, we’ll discuss what can go wrong with our machine-learning model after... Continue Reading
