Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

MarkLogic

Stacks43

Followers71

Votes26

MarkLogic vs MongoDB: What are the differences?

Introduction

In this article, we will compare and highlight the key differences between MarkLogic and MongoDB, two popular NoSQL databases.

Data Model: MarkLogic follows a flexible document data model, where data is stored in XML, JSON, or other formats. It provides support for complex data structures and relationships. In contrast, MongoDB uses a document-oriented data model where data is stored in JSON-like documents, making it ideal for handling unstructured or semi-structured data.
Querying Capabilities: MarkLogic offers powerful querying capabilities by combining both search and structured query options. It supports advanced search features like full-text search, faceted search, and geospatial search. On the other hand, MongoDB provides a rich set of query operators and indexes but lacks some advanced search capabilities compared to MarkLogic.
ACID Compliance: MarkLogic is designed to be ACID (Atomicity, Consistency, Isolation, Durability) compliant, ensuring reliable and transactional data processing. It provides built-in support for transactions, which ensures data integrity and consistency. MongoDB, on the other hand, sacrifices some level of ACID compliance for improved scalability and performance by default. It supports atomic operations at the document level but does not offer full ACID support.
Scalability and Performance: MarkLogic is designed to scale vertically and horizontally, allowing organizations to handle large amounts of data and high traffic. Its architecture provides automatic sharding and replication capabilities, ensuring fault tolerance and high availability. While MongoDB also offers horizontal scalability through sharding, it is more suited for read-heavy workloads and may require manual indexing and performance optimization in certain scenarios.
Schema Handling: MarkLogic has a schema-agnostic approach, allowing flexibility in data modeling and schema evolution. It can handle both structured and unstructured data without predefined schemas. On the other hand, MongoDB supports a flexible schema design where fields can vary between documents. It provides the option to enforce schema validation, but lacks the ability to handle complex structured data as effectively as MarkLogic.
Enterprise Features: MarkLogic offers several enterprise features, including built-in security, high availability, disaster recovery, and robust backup and restore capabilities. It also provides tools for data integration, data governance, and data lineage. MongoDB provides basic security features but lacks some of the enterprise-grade capabilities offered by MarkLogic.

In summary, MarkLogic excels in its support for complex data structures, advanced search capabilities, ACID compliance, and enterprise-grade features. MongoDB, on the other hand, offers flexibility in data modeling, scalability, and performance optimization, making it a preferred choice for certain use cases.

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Advice on MongoDB, MarkLogic

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	MarkLogic
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.	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.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Search and Query;ACID Transactions;High Availability and Disaster Recovery;Replication;Government-grade Security;Scalability and Elasticity;On-premise or Cloud Deployment;Hadoop for Storage and Compute;Semantics;Faster Time-to-Results
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 43
Followers 82.0K	Followers 71
Votes 4.1K	Votes 26
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	Pros 5 RDF Triples 3 JavaScript 3 JSON 3 Enterprise 3 REST API

What are some alternatives to MongoDB, MarkLogic?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.