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To integrate StatsD and Amazon EFS with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating StatsD and Amazon EFS 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 Elastic File System (EFS) provides a scalable but easy-to-use elastic NFS (network file system) for AWS Cloud services and on-premises resources. It supports on-demand requirements to the petabyte scale. It is fully managed and can shrink or expand as you add and remove files. This high level of change removes the need to manage capacity to accommodate growth.
Amazon EFS provides parallel shared access to thousands of Amazon EC2 instances, allowing your applications to achieve high aggregate throughput levels and IOPS (Input/output operations per second) with consistent low latencies.
You can use Amazon EFS for almost any purpose, from small home storage to business-critical applications, including big data analytics, database backups, application development, testing, web serving, content management, media and entertainment workflows, and container storage.
You can monitor your Amazon EFS file systems using Amazon CloudWatch. Metrics include the number of client connections, the amount of data read or written, file system storage, and more. You can use your Amazon EFS console to display a series of graphs based on the raw data from Amazon CloudWatch. For more extensive monitoring, you would find MetricFire an excellent companion thanks to the more advanced features.
With Metricfire, you can turbocharge your Amazon Elastic File System monitoring services. By integrating Amazon CloudWatch with the Metricfire platform, you can display your metrics on aesthetically pleasing dashboards. MetricFire's advanced filtering lets you choose only the data views you want to see, scraping the unwanted data. You can also establish clear rules to toss out data you no longer have, plus get custom alerts via email or Slack when your EFS files system falls out of alignment.
To integrate StatsD and Amazon EFS with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate StatsD and Amazon EFS and get StatsD and Amazon EFS 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
