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To integrate GitHub and Logentries with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating GitHub and Logentries and how that can support your monitoring system.
GitHub is a service for hosting code and doing version control for code. The foundations of GitHub is "Git" - which is an open source version control system.
Git allows multiple coders to contribute to one project, while remembering which is the most up-to-date version. Git also remembers who contributed and what they contributed to the project. All modifications and revisions are stored in a central repository, and contributions are made through a pull request.
Pull requests can be made by anybody, but they must be approved by the repository admin. In some projects, pull requests can be contributed without approval. However, most projects have some quality assurance processes.
When doing application performance monitoring for a project built in GitHub, you’ll want to export data about your GitHub setup into your regular monitoring dashboards. You want to monitor when changes happen, and the effect they have on your system.
For example, if you make a change to your code, and suddenly the latency of requests to your server increases - you’ll want to know exactly which pull request caused that change. If you didn’t know that there were any changes in your code, then you wouldn’t be able to correlate the change in your code with the changes in how your system is behaving. This will slow down your debugging process.
With MetricFire, you can get all of your monitoring dashboards automatically annotated with the changes you’ve made to your code in GitHub. If there are any changes in behavior, a small note will be present in the graph to tell you what’s happening in your environment.
This enables you to automatically monitor the correlation between GitHub changes and how your system is functioning.
Logentries is a service that automatically collects and centralizes all of your log data into one secure location in any format. From there, you can search and visualize all your aggregated log data. It uses both agent-based and agentless collection of logs. If an issue occurs, Logentries shows an aggregated tail view so you can review what is happening across your logs in real-time. With Logentries, you can dramatically reduce the time you spend diagnosing and resolving issues.
Logentries is a fully scalable service that dynamically auto-scales your services environment as log volumes expand and change. By aggregating all your logs into one secure location, you can efficiently monitor and track valuable log events in real-time, without the hassle of complicated configurations.
Logentries lets you monitor and track essential server resource usages, such as CPU, memory, and network and disk usage. Plus, it aggregates all server and application logs. By aggregating all your logs in one place, you can quickly review your application performance metrics, usage trends, and application load. This strategy helps you better understand how end-users are using your applications and how their behavior might affect other performance metrics.
You can use your log data to better understand application activity from client-side front-end apps to your back-end components for fine-grained user tracking. And while Logentries offers a straightforward, real-time approach to monitoring and accessing valuable application usage data, there is an even better way to monitor your aggregated log data.
By integrating Logentries with the Metricfire platform, you can turbocharge your log monitoring services to new levels and enjoy metrics displayed on aesthetically pleasing dashboards. You can import Logentries data into Hosted Graphite using the leexportpy tool. This flexible and extensible Python application enables log search results to be easily exported to third-party services, including MetricFire.
To integrate GitHub and Logentries with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate GitHub and Logentries and get GitHub and Logentries 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
