Elasticsearch vs. Virtana

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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Overall I would say that we have been very happy with Zenoss. It has been a great server monitoring tool. There are certain aspects that we would like to expand into, such as Capacity Planning, Network Performance Monitoring, and log analysis. We have coupled Zenoss logs with Splunk for external log management, but would like to start using some of the built-in analysis tools.

• 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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• Monitoring Windows, Unix/Linux, Infra apps, storage, virtualization, converge infra etc..
• Integrate with other monitoring tools and receive events for aggregation and correlation
• Integration with ITSM( SNOW) for auto ticket creation

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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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• It can be tricky to set-up depending on what you want to do.
• They have their own proprietary way of visualizing metrics, which is clever but does have a slight learning curve.

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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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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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Anytime we've had issues they have been very responsive and professional.

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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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We strongly prefer Metricly for AWS Cost Analysis -- whereas other tools are easier to use on a traditional monitoring basis. To be clear, Merticly's monitoring tools are GREAT, but they require tuning and manual setup that we didn't have the time for on a small Platform Operations team. We have worked closely with Metricly to expand on their cost analysis capabilities, and plan to use them going forward.

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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're a reseller/Integrator, so this question has a somewhat different meaning to our business. For us, Zenoss Cloud allows us to provide a single Cloud-based monitoring platform that can address virtually all our clients' use cases, dramatically simplifying our training and staffing requirements. Instead of training Engineers on several platforms, including the installation of physical hardware/software, we can focus on a single platform.
• The faster time-to-deployment and always-on cloud platform is a great fit for DevOps environments and newer software-defined data center platforms. The ability of Zenoss to support these environments solves what has been a major blind spot, slowing adoption of platforms that have been difficult to effectively manage with legacy monitoring platforms.
• For clients, the ability to consolidate from multiple prem-based tools to a single cloud-based platform is huge. Eliminating multiple licenses and ongoing hardware & administration costs can show a 1-2 year ROI.