Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
Graylog
Score 7.5 out of 10
N/A
Graylog, headquartered in Houston, offers their eponymous platform for centralized log management that helps users find meaning in data faster so as to take action immediately. Graylog is available via Enterprise and Cloud plans, but also has a Small Business Plan, and an Open (free) plan with limited features.
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.
If you already have a basic understanding of Elasticsearch and/or MongoDB, Graylog will be a great fit when it comes to log aggregation. It will be a decent option even if you don't have any experience but have the time and willingness to roll up your sleeves that learning those tools will require. Graylog supports plugins to extend functionality for things like SNMP traps, telemetry collection, and solar flares. As is the case with most software with plugins, if the core functionality for which you are looking (i.e. not logging) is based on a plugin, Graylog probably isn't for you. The majority of the plugins in the marketplace are developed by third-parties looking to solve their specific use case so bug fixes and new features are not a given.
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.
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 am still unhappy with the pricing model for the enterprise. Graylog competes against the likes of IBM and Splunk, but your still the new kid on the block. To price Graylog enterprise at 50k for 20GB ingest an unrealistic data. It would require multiple facets of Graylog to be stood up and only forward pruned logs to the paid version.
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.
Azure Monitor is not exactly what I mean, but I couldn't find Azure Application Insights. Anyway, for a large organization, Azure makes more sense than using Graylog because a lot of logging will already be inside Azure. And you don't want to have two "central" logging locations. But Azure is chaos and highly "not intuitive." So for small and mid-size organizations, Graylog is still the better option.
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.
