Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

RavenDB

Stacks79

Followers82

Votes9

GitHub Stars3.9K

Forks850

MongoDB vs RavenDB: What are the differences?

Introduction:

MongoDB and RavenDB are both popular NoSQL databases that provide flexible document storage and retrieval features. While they share some similarities, there are key differences between the two. This article aims to highlight the main distinctions between MongoDB and RavenDB.

Query Language: MongoDB uses a query language called Query Language Plus (QL+) which allows developers to perform complex queries and aggregations on data. On the other hand, RavenDB uses a query language called Raven Query Language (RQL) which is designed to be more developer-friendly and intuitive. RQL supports LINQ queries and allows for easier document manipulation.
Transaction Support: MongoDB provides support for multi-document transactions, allowing ACID (Atomicity, Consistency, Isolation, Durability) operations across multiple documents and collections. RavenDB, on the other hand, supports full ACID transactions, supporting multi-document and multi-collection transactions with rollback and commit capabilities.
Indexing: MongoDB uses a variety of indexing techniques, including B-trees, Geospatial Indexes, and Text Indexes, to optimize data retrieval performance. RavenDB also supports various indexing options, but it additionally provides the ability to create more complex map-reduce indexes, which can be useful for advanced querying and reporting scenarios.
Scalability: MongoDB is designed to scale horizontally, allowing for easy distribution of data across multiple nodes or clusters. It supports automatic sharding to divide data sets and load balancing for better performance. RavenDB, on the other hand, is primarily focused on vertical scaling, providing robust options for scaling up on a single machine.
Schema Flexibility: MongoDB provides schema flexibility, allowing documents in a collection to have varying structures. It is schema-agnostic, allowing developers to add, modify, or remove fields without any strict structure enforcement. RavenDB, on the other hand, provides the option of having predefined schemas and allows developers to define and enforce a structure, providing more control and consistency.
Replication: MongoDB supports a variety of replication topologies, including master-slave and replica sets, allowing for high availability and data redundancy. RavenDB also supports replication but provides more advanced features such as active-active replication, conflict resolution, and conflict tracking, making it suitable for distributed systems and offline scenarios.

In Summary, MongoDB and RavenDB differ in their query languages, transaction support, indexing options, scalability approaches, schema flexibility, and replication capabilities. Each database has its own strengths and use cases, and choosing the right option depends on the specific requirements and priorities of the project.

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Advice on MongoDB, RavenDB

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	RavenDB
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.	As a document database it remains true to the core principles of these type of storage mechanisms. Somehow it managed to combine the best of relational databases with that of document databases.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Multi-Platform; ACID Transactions
Statistics
GitHub Stars 27.7K	GitHub Stars 3.9K
GitHub Forks 5.7K	GitHub Forks 850
Stacks 96.6K	Stacks 79
Followers 82.0K	Followers 82
Votes 4.1K	Votes 9
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 4 Embedded Library 3 Easy of use 2 NoSql
Integrations
No integrations available	Python Windows Java Ruby Linux

What are some alternatives to MongoDB, RavenDB?

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.