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To integrate StatsD and Amazon Lambda with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating StatsD and Amazon Lambda 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.
Amazon Lambda is a cloud-based, fully scalable service that lets you run code without managing or provisioning servers. Amazon Lambda will run your code when you need it and only charges you when you use it. You can run virtually any code for nearly all types of backend applications and services with no administration. Amazon looks after it all for you, including code monitoring and logging, capacity provision and scaling, and operating system and server maintenance.
You can use code that responds automatically to an Amazon DynamoDB table or Amazon's Simple Storage Service (Amazon S3). You can also invoke code using API calls made using AWS SDKs or write code to respond to HTTP requests using Amazon API Gateway. Plus, you can write functions for serverless applications that you can automatically deploy using AWS CodeBuild and AWS CodePipeline. You can create backend services that take advantage of Amazon Web Services inbuilt security, scale, and performance, plus use Amazon Lambda to extend other AWS services with custom logic.
More importantly, Amazon Lambda automatically monitors your code on your behalf and reports metrics through Amazon CloudWatch. Amazon Lambda automatically tracks the number of requests, the invocations per request, and the number of errors triggered by a request.
With MetricFire, you can turbocharge your Amazon Lambda code monitoring services. By integrating Amazon CloudWatch with the MetricFire platform, you can display your Amazon Lambda metrics on aesthetically pleasing dashboards. MetricFire's advanced filtering lets you choose only the data views you want to see and discard the rest. You can also set up simple rules to discard data you no longer keep, plus you can receive alerts via email or Slack when your code is doing something it shouldn't.
To integrate StatsD and Amazon Lambda with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate StatsD and Amazon Lambda and get StatsD and Amazon Lambda 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
