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MarkLogic vs Neo4j: What are the differences?

Introduction

MarkLogic and Neo4j are both popular database management systems but have distinct differences. Understanding these differences is crucial to choose the appropriate system for a specific use case. Below are the key differences between MarkLogic and Neo4j.

  1. Data Model: MarkLogic is a document-oriented database that stores data in the form of JSON or XML documents. It provides rich indexing and search capabilities, enabling fast retrieval of documents based on their content. On the other hand, Neo4j is a graph database that represents data as nodes, relationships, and properties. It excels in modeling complex relationships between entities and supports efficient traversal of the graph structure.

  2. Query Language: MarkLogic uses XQuery and JavaScript as its primary query languages. XQuery is a powerful language for querying structured data, while JavaScript is more suitable for unstructured data processing. Neo4j, on the other hand, utilizes the Cypher query language, specifically designed for graph traversal and pattern matching. Cypher allows for expressive querying of graph structures and makes it easier to work with connected data.

  3. Scalability: MarkLogic is designed to support high scalability and horizontal scaling through its shared-nothing architecture. It can distribute data and workload across multiple nodes, ensuring high availability and fault tolerance. Neo4j also supports horizontal scaling, but it is primarily optimized for smaller to medium-sized datasets. Neo4j's strength lies in its ability to handle highly connected data and complex graph traversals efficiently.

  4. Use Cases: MarkLogic is commonly used in applications that require flexible and rich data modeling, along with robust searching and indexing capabilities. It is suitable for scenarios where unstructured and structured data coexist and need to be seamlessly integrated. Neo4j, on the other hand, excels in use cases involving network analysis, recommendation engines, social networks, and any scenario that heavily relies on graph-like relationships and connected data.

  5. ACID Compliance: MarkLogic provides ACID (Atomicity, Consistency, Isolation, Durability) compliance out of the box. This ensures data integrity and consistency, making it suitable for applications with strict data integrity requirements, such as financial systems. Neo4j, being a graph database, does not provide full ACID compliance by default. Instead, it offers eventual consistency, which is typically sufficient for most graph-based use cases but might not be suitable for applications requiring strong transactional guarantees.

  6. Community and Ecosystem: MarkLogic has been around for many years and has a well-established community and ecosystem. It offers a wealth of third-party integrations, connectors, and development tools. Neo4j also has a growing community and ecosystem, but it might not be as mature as MarkLogic's. However, Neo4j has gained popularity in recent years and has its own set of integrations and tools.

In summary, MarkLogic is a document-oriented database with rich indexing capabilities, using XQuery and JavaScript as query languages. It excels in flexible data modeling and powerful search capabilities. Neo4j, on the other hand, is a graph database primarily focused on efficient graph traversal and pattern matching, with the Cypher query language. It is well-suited for applications requiring network analysis and heavily connected data.

Advice on MarkLogic and Neo4j
Jaime Ramos
Needs advice
on
ArangoDBArangoDBDgraphDgraph
and
Neo4jNeo4j

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

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Replies (3)
Recommends
on
ArangoDBArangoDB

I have not used the others but I agree, ArangoDB should meet your needs. If you have worked with RDBMS and SQL before Arango will be a easy transition. AQL is simple yet powerful and deployment can be as small or large as you need. I love the fact that for my local development I can run it as docker container as part of my project and for production I can have multiple machines in a cluster. The project is also under active development and with the latest round of funding I feel comfortable that it will be around a while.

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David López Felguera
Full Stack Developer at NPAW · | 5 upvotes · 46.3K views
Recommends
on
ArangoDBArangoDB

Hi Jaime. I've worked with Neo4j and ArangoDB for a few years and for me, I prefer to use ArangoDB because its query sintax (AQL) is easier. I've built a network topology with both databases and now ArangoDB is the databases for that network topology. Also, ArangoDB has ArangoML that maybe can help you with your recommendation algorithims.

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Recommends
on
ArangoDBArangoDB

Hi Jaime, I work with Arango for about 3 years quite a lot. Before I do some investigation and choose ArangoDB against Neo4j due to multi-type DB, speed, and also clustering (but we do not use it now). Now we have RMDB and Graph working together. As others said, AQL is quite easy, but u can use some of the drivers like Java Spring, that get you to another level.. If you prefer more copy-paste with little rework, perhaps Neo4j can do the job for you, because there is a bigger community around it.. But I have to solve some issues with the ArangoDB community and its also fast. So I will preffere ArangoDB... Btw, there is a super easy Foxx Microservice tool on Arango that can help you solve basic things faster than write down robust BackEnd.

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Pros of MarkLogic
Pros of Neo4j
  • 5
    RDF Triples
  • 3
    JSON
  • 3
    Marklogic is absolutely stable and very fast
  • 3
    REST API
  • 3
    JavaScript
  • 3
    Enterprise
  • 2
    Semantics
  • 2
    Multi-model DB
  • 1
    Bitemporal
  • 1
    Tiered Storage
  • 70
    Cypher – graph query language
  • 61
    Great graphdb
  • 33
    Open source
  • 31
    Rest api
  • 27
    High-Performance Native API
  • 23
    ACID
  • 21
    Easy setup
  • 17
    Great support
  • 11
    Clustering
  • 9
    Hot Backups
  • 8
    Great Web Admin UI
  • 7
    Powerful, flexible data model
  • 7
    Mature
  • 6
    Embeddable
  • 5
    Easy to Use and Model
  • 4
    Best Graphdb
  • 4
    Highly-available
  • 2
    It's awesome, I wanted to try it
  • 2
    Great onboarding process
  • 2
    Great query language and built in data browser
  • 2
    Used by Crunchbase

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Cons of MarkLogic
Cons of Neo4j
    Be the first to leave a con
    • 9
      Comparably slow
    • 4
      Can't store a vertex as JSON
    • 1
      Doesn't have a managed cloud service at low cost

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    - No public GitHub repository available -

    What is MarkLogic?

    MarkLogic is the only Enterprise NoSQL database, bringing all the features you need into one unified system: a document-centric, schema-agnostic, structure-aware, clustered, transactional, secure, database server with built-in search and a full suite of application services.

    What is Neo4j?

    Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.

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    What companies use MarkLogic?
    What companies use Neo4j?
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    What tools integrate with MarkLogic?
    What tools integrate with Neo4j?

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    What are some alternatives to MarkLogic and Neo4j?
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Oracle
    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
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