MarkLogic Server

MarkLogic supports fully ACID transaction and I think this is very rare in a NoSQL system.

The recent version of MarkLogic has Integration with Node.js, REST, JSON which has really made the developers life easier to build integrated systems.

MarkLogic provides superb documentation for us. It really helps to understand which features work how. Example is- the whole dedicated website for it. https://docs.marklogic.com/

From the point of infrastructure - Installation, configuration and deployment is very fast. Compared to RDBMS , it's really easy to scale MarkLogic horizontally by adding nodes.

The licence cost is HIGH.

The amount of space required to store the data seems high hence costly.

The compatibility with legacy system is not yet available. I feel this area needs to be improved very fast.

MarkLogic is highly scalable and with exceptional performance.

Marklogic provides real time services through its RESTful services it can be used as both OLTP and OLAP servers.

MarkLogic is identified as the best fit because they can load all the data in XML format through different silos.

How to do complete data profiling on documents loaded in Marklogic database?

Customers need a tools which can be customized to suit their data profiling needs but currently the tools which MarkLogic provides fall short on this requirement.

Unit testing framework which is using only XQuery as the language is lacking some features.

Search was really advanced. Hard to set up and had limitations about semantical meanings between xml nodes, but provided very good search abilities.

The organization of documents across collections and metadata was particularly useful.

The REST abilities were very advanced and worked with XQuery well.

The management and set up is "too" advanced. It is easy to get started but comes configured wrong out of the box for large stores.

The deployment framework is non-existent. We had to maintain our own framework through Puppet and other means to get it deployed ad-hoc. It's meant to be deployed once, but does not work well with the temporary environment mantra that DevOps aims to achieve.

There is absolutely no way to run tests or automate the testing of REST. We had to roll our own.

The community is lacking for open source. If we needed something we had to write it.

MarkLogic does everything well, but search is the "bread and butter" operation. All data is indexed on-the-fly and the API's offer a multitude of ways to create incredibly powerful search applications. The search engine isn't bolted on- it's at the core of the database. Search suggestion, relevance, advanced grammar, spell correction (did you mean?), paginated search over massive numbers of records, etc. is all at the fingertips of the developer. The database scales to massive size and yet search returns sub-second results for the most complex search parameters.

High availability, disaster recovery, and scaling is handled incredibly well. In the AWS cloud, it is trivial to set up a MarkLogic system to elastically scale with data and request volume- truly elastic, adding nodes and removing them as needed. Databases can replicate to a remote datacenter in real-time to provide instant cut-over for datacenter loss. Clustered servers provide highly available replication of data to instantly recover from node failures.

Security is increasingly important as data takes center stage in an enterprise. MarkLogic's role-based security is baked in to every query. This is battle-hardened content control.

Flexibility is unrivaled. Any data can be stored reliably and securely in the MarkLogic database. Records can be stored as text, XML, JSON, or binary. All text, XML, and JSON is instantly indexed and the various strategies for indexing are easy to configured and well documented. MarkLogic is also a powerful semantic triple store. Unlike any other NoSQL solution, MarkLogic can handle full documents, graphs, key-value pairs, binaries, etc. in a single database, providing powerful and unique ways of combining enterprise data.

MarkLogic still has a long way to go in fostering the developer community. Many developers are gravitating to the simple integrations and do not delve into the deeper capabilities. They have made tremendous strides in recent months and I am sure this will improve over time.

Many of the best features are left on the floor by enterprises who end up implementing MarkLogic as a data store. MarkLogic needs to help customers find ways to better leverage their investment and be more creative in how they use the product.

Licensing costs become a major hurdle for adoption. The pricing model has improved for basic implementations, but the costs seem very prohibitive for some verticals and for some of the most advanced features.

MarkLogic is fast and flexible. The data does not have to be structured (particularly in advance).

MarkLogic is a combination database, search engine, and application server. As a database, it is ACID compliant which is absolutely essential for mission critical production applications.

MarkLogic is dependable, in almost all cases recovers by itself, and is relatively easy to administer.

MarkLogic is not cheap, either for the software itself, the hardware to run it on, or the investment in learning necessary to use it effectively. While MarkLogic has gone to great pains to add multiple interfaces so that a deep understanding of XQuery is theoretically not necessary, I feel XQuery is essential to understanding the product well enough to use it in production applications.

Specifically, it is my understanding that switching database contexts is expensive in terms of performance. There could be be a improvement in the ability to query across databases, or even across clusters, that could drive greater flexibility in design decisions.

The security model definitely could be improved to facilitate sharing users, roles, and permissions across clusters. Building your own security model to allow users access to data on different clusters is very complex and leads to a number of performance issues.

Indexing is a major strength of MarkLogic. The out-of-the-box configuration is set up to handle a combination of text and fielded data. The indexing is also highly configurable. Those configuration options are at the heart of a lot of our high-volume, high-performance applications.

The industrial strength transactions and security are also a strength, particularly when we are dealing with user-created intellectual property.

The engineering support is a strength. They are big enough to have a really strong support and engineering staff, but small enough so that a medium-sized customer has access to it. They are very responsive to questions and problem reports.

The ability to move easily among XML and JSON is a strength.

There is a steep learning curve to learn to use this tool, particularly the xquery and extensive associated API. The more recent releases and features have been responsive to this concern, but some of the core features still take some learning.

The javascript implementation is new and there are still some spots where it needs to be made fully compliant with standards and conventions, such as file extensions

MarkLogic's implementation of the XQuery language is fast and versatile, and the additional language features and numerous libraries that they provide out-of-the-box really make it feel like a "batteries included" language, at least within the context of XML and JSON database management.

Despite its enterprise focus, MarkLogic is a very developer-friendly product. All of its administrative features, including provisioning servers, are available through APIs. Most of the functionality one uses from XQuery come in the form of source-included XQuery libraries. I am strongly inclined, generally, to favor open-source solutions, so the focus on freedom and capability for the developer was important to me in this proprietary platform.

MarkLogic is fast. It's much faster than the other XML-based database engines we looked at, and is certainly faster than XML layers bolted on top of relational database engines. The lockless write strategy is great architecture, and it enables the engine to smoothly scale up simultaneous reads and writes. MarkLogic's speed enabled us to perform significant updates on database structure and content as part of our continuous deployment strategy with minimal impact on stability and availability.

XQuery is a tough language for engineering teams to adopt; it's the world's weirdest pure functional programming language. Once you've crossed over and can see The Matrix, it's clear that the language design is aligned perfectly with the XML querying problem domain, and the fact that it's a Turing-complete programming language rather than just a query domain-specific language (like SQL) enables you to go wherever you need to go with it, which is empowering. But there are areas of obscurity and seeming inconsistency between MarkLogic's lockless write strategy, transaction management and XQuery semantics that all of our developers, at one time or another, ran into, and we only mastered these features after many months with the product and with the help of an expert consultant. It turns out that much of what we learned appears in the copious documentation, but the documentation is SO copious (and, in the area of transaction management, so dense) that none of us had the time to read all of it. This is an area of the product that the documentation should elevate and present, front and center, in a way that is more prescriptive, rather than just descriptive. Give examples showing how developers should solve real problems, and illustrate what the engine is doing.

MarkLogic's pricing, like its feature set, is enterprise-scale. One understands; they're like the Oracle of NoSQL. But the storage restriction on their "free" version basically means that the only way in is full retail, so there are wide categories of companies that would never consider adopting. I wonder if any kind of tiered model might be sustainable for them.

MarkLogic support was quite reasonable in their response time and their general knowledge of the product, but there were certain insights to problems that we really only gained by working with a very knowledgeable consultant that the company recommended for us.