Elasticsearch vs. Magnolia

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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Magnolia is a very capable DXP, that provides client with lots of flexibility in composing its own stack. While the core of the platform is a content management system, the open architecture of Magnolia DXP allows it to connect to any platform, allowing client to extend the capabilities. One scenario would be a centralized content hub - where through a single platform, content authors can choose which channel to distribute what content. For example, long form content for consumers viewing on a laptop, short form content for those using a mobile browser. This allow the client to personalized the experience based on channels. Another scenarios would be leveraging on GenAI - using Magnolia's built-in connector to ChatGPT. If that is not the service that one desire, you can always connect to another AI service such as Google Gemini. With GenAI, connected, content author can use AI as co-pilot to help them scale up their content production.

• 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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• Speed of development - time to delivery from zero to MVP was excellent
• Ease of use - the authoring experience is very easy to build and train
• PAAS/SAAS - the managed service platform removed the traditional overhead of running in-house technologies, meaning we could focus on value add, with less time spent keeping the lights on.

• 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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• The documentation provides samples that are often out of context, and difficult to know where the provided example code should be implemented. More tutorials providing the full project or step-by-step instructions on how to implement subject material would help greatly. Baeldung is a resource I would consider the gold standard in how this is done in other spaces.
• The use of JCR and Nodes makes object serialization/deserialization painful. Jackson compatibility or similar would be a welcome enhancement to the developer experience. Maybe leveraging code-gen from light modules to build model classes when possible could help accomplish this.
• Modifying the home layout from light modules is frustrating. It seems that any configuration overrides made merge with the default rather than overwriting, which makes for a difficult combination of guess-and-check while referencing the documentation to see what should be in each row/column when making changes.
• Including "mark all as read" or "delete all" in the notifications app would be a great quality of life improvement. It seems that by default, users have to individually select messages and operate them.

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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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We have invested a lot of time and energy into tailoring a solution that works for the company.

We think the new features in v6.2 will help us get to the next level

We also don't have the resources to rebuild a website platform from scratch even if we wanted to

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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 shown it to a number of users both clients and our own team and despite initial apprehensions, they "get it" very quickly. It's intuitive and friendly and quick to perform daily tasks. We once had a client tell us "Using Magnolia makes me smile" which says it all for us.

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I gave [it] 7/10 only because of the loading time of pages. Otherwise, I think it deserves an 8. Normally this is not an issue per [se] but considering the rating matrix and as I have been asked to honestly write about it. Yes, the page loading times could be improved.

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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You always get an answer based on your SLA. But you always get a solution. That's the successfactor in this case. To often i was frustrated about people in a company without even a clue what there product is about or how to solve a problem. Magnolia's Support Team does a very good job and try to help you in most of the cases

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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I've used several CMSs like AEM and EpiServer, and comparatively, they all excel at different things. Magnolia is the best to develop for/against. Episerver has the best/most fluid UI in terms of content editing, and the overall admin experience AEM is just all around sucks.

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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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• Magnolia has brought about positive impacts. For instance, we need not outsource web design and marketing services because thanks to this software, we can handle most work inhouse
• The software is affordable with no compromises on capabilities and therefore it is gives us value for money.
• The templates makes the whole process easy

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