Mirantis Kubernetes Engine vs. New Relic

[Mirantis Cloud Native Suite (Docker Enterprise)] is the most advanced tool till now, which works as a VMs
and separates any single application from the dependencies. Also, this tool is
helping me in the agile development of the processes. It is strongly recommended to
almost all major organizations.

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New Relic its an excellent tool for monitoring services used on the SAAS universe, like web servers, relational and nosql dbms, reverse proxies, text databases, etc. Its also a powerful tool to monitor resource usage on said servers. However, its not well fitted to monitor custom services - if you need to generate alerts based on logs or database information, for example

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• Containers - Docker is the go-to when using Containers, which are super useful if you need an environment that works both for Windows and Linux
• Efficiency - Docker is very lightweight and doesn't demand too much from your CPU or server
• CI/CD - Docker is excellent for plumbing into your build pipeline. It integrates nicely, is reliable, and has an easy set up.

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• It gives us insights of applications, infrastructure, clouds very well.
• The alerting system in New Relic helps us to easily identify the root cause which reduces the MTTR.
• It has new features related to AI which helps to troubleshoot any problem in a very short time it gives future predictions also.

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• Containers are often opaque - if a container doesn't work out of the box, it's messy to fix.
• Logging is complexified by the multiple containers and logs are often not piped to places you expect them to be.
• Networking is complexified due to internal port mapping between containers, etc.

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• And while powerful, building tailored dashboards with organ-specific metrics (such as energy load variance across regions) can be difficult to navigate. The UI isn't as drag-and-drop easy, and query-based widgets typically involve some trial and error for non-devs.
• Alerts may be hypersensitive or over general. I We often get a spam of non-critical alerts while doing load testing, all overhauling to me alone and making it difficult to identify actual issues especially in energy systems where spikes are very common.
• With our expanding fleet of Iot devices, the per-host pricing model is becoming expensive, quickly. More detailed billing based on microservices, or that works at sensor level, would make it more adaptable for energy platforms.

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The only issue that we have had with New Relic is that the price might be a little expensive for smaller companies. The amount of data you store in New Relic impacts the cost, and can get away from you if you don't work closely with the vendor. Overall though the application is top notch.

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Docker's CLI has a lot of options, and they aren't all intuitive. And there are so many tools in the space (Docker Compose, Docker Swarm, etc) that have their own configuration as well. So while there is a lot to learn, most concepts transfer easily and can be learned once and applied across everything.

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I have given this much rating as I am used New Relic in different sectors and for different use cases like its K8s monitoring, infra monitoring, full stack monitoring as compare to other tools New Relic gives data in a formatted and connected way, and also it is giving us value for money. It also launches new features day by day which helps users to track the issue very quickly. It also supports OTel integrations which is the latest trend of observability tools. thats why I had given this much rating to New Relic.

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Never observed an outage

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there are times where browser cache will cause issues that require you to clear your browser before continuing.

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The community support for Docker is fantastic. There is almost always an answer for any issue I might encounter day-to-day, either on Stack Overflow, a helpful blog post, or the community Slack workspace. I've never come across a problem that I was unable to solve via some searching around in the community.

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The support team has been really helpful and resolved most of the issues on time. However, for a couple of issues, several follow-ups were needed to elicit a reasonable response. The issue was deeply technical and could have been investigated only by their Architects, and bringing them into the ticket took longer than needed

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It's better to start by implementing New Relic in one project and test everything. Try to follow best recommended practices and read all the official documentation. Everything seems well tested. Then, start by installing agents to the rest of your projects and keep a close look to all logs and metrics New Relic gives you.

We've used XAMPP, PHPmyAdmin and similar local environments (our app is on PHP). Because of how easy you can change the configuration of libraries on PHP and versions (which is SO painful on XAMPP or other friendly LAMP local servers) we are using Docker right now. Also, being sure that the environment is exactly the same makes things easier for developing.

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Data Dog has solutions that look more attractive, but not at their price point. We have also tried to build a solution straight from the Cloud, where our business is built, but some things are too hard to replicate. This shows that New Relic is useful and helps our efficiency.

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Agent deployment is easily integrated into our workflow. Adding the agent to new servers is quick and painless

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• Docker has made it possible for us to deploy code faster, increasing the productivity of our development teams.
• Docker has made it possible for us to decentralize our build and release system. This means that teams can deploy on their own schedule and our dev ops team can concentrate on building better tools rather than deploying for the teams
• Docker has allowed us to virtualize our entire development process and made it much simpler to build out new data centers. This, in turn, is significantly increasing our ROI by providing a path forward for internationalization.

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• We were able to quickly identify our most time consuming APIs. In some cases we were able to bring down times for some apis from 4s to 200ms.
• We were able to identify our slowest database queries and optimize them for quicker response times.

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