Elasticsearch is really well suited for searching text (Natural Language Processing) and you can fine tune the searches and scoring very well. I like the ability to find Significant Terms in the Index, where you can find aggregations that are really relevant to a specific search. It also allows for queries to lead to new queries via aggregations which is great for navigating your data. It is less suited to doing more complex aggregations where slices of data are required to be processing using guassian normalizations. And doing searches which join different documents is very very hard, and requires serious thought on how to denormalize data.
SumoLogic is a fantastic log aggregator and analysis tool, a fine alternative to Splunk. Searching is powerful and mostly intuitive and results come fast. If you have application logs in clusters or Kubernetes pods that lose their logs every time they're restarted, Sumo is the solution for you
Log Aggregation and uploading. The architecture for Sumo Logic makes a great deal of sense and works very well.
Automated analysis. It still impresses me how well a newly uploaded log can be broken into intelligent parts, then searched and sorted using their tools.
Dashboards. It might not be what YOU will need as an IT admin, but you can give access to these dashboards easily to business users who love that kind of stuff. Most other types of (monitoring / alerting) tools, for no apparent reason, lack this feature.
Reporting, monitoring, and graphing. Given, you need to have useful log generation for an application or service as a prerequisite for sumo logic to be able to gain use, once it has it is an amazingly powerful tool.
Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.
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.
Sumo Logic is very powerful but definitely requires some configuration work to get the most out of it. You can get a certification related to this, but it is definitely not something you can just throw together.
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 would give this rating because I attended a free Sumo Logic training at a WeWork in Chicago. I found the training very useful, and I learned a lot of features that I was not aware of before I went to the training. I like the idea that SumoLogic provides free training seminars. I am certified in level1, and I plan on certifying to level2.
I was satisfied with the implementation, as at the time, it was the best way to implement the product with the available feature sets in Sumo Logic. User creation and management became more of an issue during continued use, instead of it being an issue related to deploying the product in our environment.
Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices. In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch. We've tried Solr in the past be we encountered issues which were deal-breaking for us. MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
We had used Splunk previously. Sumo Logic defeats them when it comes to cost, including the costs that would normally come with supporting/managing/patching/upgrading your own infrastructure and storage. Those were wins, but especially the real-time CDN integrations due to Sumo Logic's collaborations with other vendors. We had spoken to Logentries and discovered that many of the cons we found with Sumo Logic seemed to have been resolved in their product. Their pitfall was that, at the time, Logentries did not have the ability to get real-time log ingestion from our CDN. They said they had a solution, which was scripted, but we had not evaluated/tested. Logentries also did not have a User / RBAC REST API, and are nowhere near the level of compliance that Sumo Logic had (https://www.sumologic.com/press/2015-02-19/sumo-logic-successfully-completes-pci-data-security-stand...). In the end, I believe Logentries and Sumo Logic would be two good vendors to get involved in a bake-off
I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.
One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.
On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.
