Elasticsearch vs. Prometheus

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.

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This program works from the roots of the problem and creates a professional matrix for each of its users. This will give them more skills and resources to carry out tasks and reduce the difficulties of operating each of the processes of my work, as well as being An ally for the manipulation and operability of all your master data; Prometheus is very easy to recommend since it is a program that fulfills its mission.

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• 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.

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• Looking at metrics such as the aggregate number of HTTP requests served.
• Understanding how our services are performing in aggregate.
• Easy to deploy within a variety of architectures/environments.
• Open source so new features are added regularly and bugs are fixed in a timely manner.
• Free so there are no licensing restrictions.
• Endorsed by the Cloud Native Computing Foundation.

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• Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
• Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
• Schema changes require complete reindexing of an index

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• Customer Service: since this is an open-source tool, customer service is not that great. Generally, you get all answers to your problems in online forums, but in case you got stuck, nobody will assist you in a channelised manner. You will have to find the way out on your own, and it may become frustrating at times.
• More metrics for dashboards shall be added per the application being monitored. Standards metrics will work in most cases but may not in specific applications. Therefore, customised metrics shall be created for some of the industry-standard niche applications.

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We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.

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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.

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It is usable and one can learn if few people in the team are already using it. It can be difficult to understand at the beginning because of non intuitive UI and syntax of the rules. So, I've gone for 7 points as there is some room for improvement in user interface and rules syntax.

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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.

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Never had to reach out to them.

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Do not mix data and master roles. Dedicate at least 3 nodes just for Master

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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.

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Highly customized pricing plans to choose from. Lower pricing for the same features compared to competitors. Easy to reach the support team, which provided detailed documentation and helped set up the Prometheus. Monitoring metrics gets very easy after the integration with Grafana. It also has a sophisticated alert setting mechanism to ensure we don't miss anything critical.

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• 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.

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• We are still working on the ROI
• The ROI mentioned during the purchase has not been achieved, however this could be due to lack of data from our side. 2 years of implementation is too early to calculate and confirm the ROI.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info