+
To integrate collectd and Pingdom with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating collectd and Pingdom and how that can support your monitoring system.
Collectd is system statistics collection daemon that periodically retrieves system and application performance metrics and then stores them using various storage solutions such as RRD files. Collectd is a free open-source project. It can gather metrics from multiple sources, including operating systems, applications, log files, and external devices. You can use the statistics compiled by collectd to monitor systems, detect performance bottlenecks, and predict future system loads.
Because it's written in C, collectd is built for performance and portability, allowing you to run it on systems with no scripting language or an embedded system. It comes with optimizations and features to handle hundreds of thousands of metrics. Collectd also lets you configure what metrics you want to capture and what frequency you want to capture them, allowing you to scale back your metrics collection collect only the metrics that make sense for your business needs. Its modular architecture allows the daemon to use only the resources required to do the job.
Collectd only performs the collection and transmission of the metrics. It does not analyze or present the metrics on its own. But by integrating collectd with the Metricfire platform, you can enjoy metrics displayed on aesthetically pleasing dashboards. Our integration lets you view metrics on essential systems and application log messages on your Hosted Graphite graphs and dashboards.
You can download a collectd configuration file pre-filled with your Hosted Graphite account details on our add-ons page. This configuration comes with several collectd plugins enabled, including CPU, DF (file system usage), disk, entropy, interface, load, memory, processes, rrdtool, swap, users and write_graphite.
Pingdom is a monitoring system that uses both synthetic and real user monitoring to deliver enhanced app and website troubleshooting.
Pingdom's synthetic monitoring allows you to simulate visitor interaction with your website or app to spot app errors or website failures before your users do. You can monitor site uptime from over a hundred locations worldwide or troubleshoot your website speed and identify webpage issues. You can also test user transactions such as shopping carts, logins, registrations, and even URL hijacking.
Pingdom's real-time user monitoring allows you to monitor how your users interact with your website or app. Pingdom's real user monitoring (RUM) is fully scalable and intuitive. Learn how your users experience your site based on device, location, and browser. Ensure you meet your SLAs or KPIs by setting your own goals and monitoring your performance. Record and compare user metrics over time to see how your website performs over weeks, months, or years.
Digital marketers can use Pingdom's synthetic monitoring to test new campaigns and ensure their users have the best experience. They can use Pingdom's real-time user monitoring to review how different user experience aspects, such as browser type or device, affect sales.
Web developers can test for website or app performance and spot defects while updates are in development. Web host providers can monitor performance in real-time to ensure client uptime. IT/Web Ops can use synthetic monitoring to quickly identify bottlenecks and spot backend issues with Pingdom's real-time user monitoring.
With MetricFire, you can amplify Pingdom's synthetic and real-time user monitoring. By integrating Pingdom with the MetricFire platform, you can display your metrics on aesthetically pleasing dashboards. With MetricFire, you can quickly understand complex websites at a glance or compare your Pingdom data with other monitoring systems.
To integrate collectd and Pingdom with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate collectd and Pingdom and get collectd and Pingdom 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
