Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
Nagios Core
Score 8.0 out of 10
N/A
Nagios provides monitoring of all mission-critical infrastructure components. Multiple APIs and community-build add-ons enable integration and monitoring with in-house and third-party applications for optimized scaling.
Commercial tools where expensive and not as capable for our needs. Many had other functions that where not as useful for monitoring, such as automation, scripting, software installation. Many of which we had migrated to purpose-built tools that served our needs better.
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
Nagios monitoring is well suited for any mission critical application that requires per/second (or minute) monitoring. This would probably include even a shuttle launch. As Nagios was built around Linux, most (85%) plugins are Linux based, therefore its more suitable for a Linux environment.
As Nagios (and dependent components) requires complex configurations & compilations, an experienced Linux engineer would be needed to install all relevant components.
Any company that has hundreds (or thousands) of servers & services to monitor would require a stable monitoring solution like Nagios. I have seen Nagios used in extremely mediocre ways, but the core power lies when its fully configured with all remaining open-source components (i.e. MySQL, Grafana, NRDP etc). Nagios in the hands of an experienced Linux engineer can transform the organizations monitoring by taking preventative measures before a disaster strikes.
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
Nagios could use core improvements in HA, though, Nagios itself recommends monitoring itself with just another Nagios installation, which has worked fine for us. Given its stability, and this work-around, a minor need.
Nagios could also use improvements, feature wise, to the web gui. There is a lot in Nagios XI which I felt were almost excluded intentionally from the core project. Given the core functionality, a minor need. We have moved admin facing alerts to appear as though they originate from a different service to make interacting with alerts more practical.
We're currently looking to combine a bunch of our network montioring solutions into a single platform. Running multiple unique solutions for monitoring, data collection, compliance reporting etc has become a lot to manage.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
The Nagios UI is in need of a complete overhaul. Nice graphics and trendy fonts are easy on the eyes, but the menu system is dated, the lack of built in graphing support is confusing, and the learning curve for a new user is too steep.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
I haven't had to use support very often, but when I have, it has been effective in helping to accomplish our goals. Since Nagios has been very popular for a long time, there is also a very large user base from which to learn from and help you get your questions answered.
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
Because we get all we required in Nagios [Core] and for npm, we have to do lots of configuration as it is not as easy as Comair to Nagios [Core]. On npm UI, there is lots of data, so we are not able to track exact data for analysis, which is why we use Nagios [Core].
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
With it being a free tool, there is no cost associated with it, so it's very valuable to an organization to get something that is so great and widely used for free.
You can set up as many alerts as you want without incurring any fees.
