MongoDB vs VelocityDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

VelocityDB

Stacks1

Followers19

Votes0

MongoDB vs VelocityDB: What are the differences?

Introduction: When comparing MongoDB and VelocityDB, there are several key differences to consider in terms of their functionality and capabilities.

Data Model: MongoDB is a document-oriented database that stores data in JSON-like documents, making it easy to store and retrieve complex data structures. On the other hand, VelocityDB is an object-oriented database management system that stores data as objects, providing more flexibility in data modeling and relationships.
Indexes and Querying: MongoDB utilizes indexes to optimize query performance, allowing for faster retrieval of specific data. VelocityDB, on the other hand, leverages object browsing and LINQ queries for data retrieval, providing a different approach to querying data.
Scalability: MongoDB is known for its horizontal scalability, allowing for easy distribution of data across multiple nodes or servers to handle large amounts of data. VelocityDB, while also scalable, may require more manual configuration for horizontal scaling due to its object-oriented nature.
ACID Transactions: MongoDB supports ACID transactions at the document level, ensuring data integrity and consistency. In contrast, VelocityDB offers ACID transactions at the object level, providing granularity in transaction management.
Community and Support: MongoDB has a large and active open-source community, providing extensive documentation, tutorials, and support resources. VelocityDB, being a commercial database system, offers dedicated customer support and assistance for users.
Compatibility: When it comes to compatibility, MongoDB is widely supported across various operating systems and programming languages, making it versatile for different development environments. VelocityDB may have more limited compatibility depending on the specific requirements of the application.

In Summary, MongoDB and VelocityDB differ in terms of their data model, querying capabilities, scalability, transaction management, community support, and compatibility. Each database system offers unique features and functionalities that cater to different use cases and preferences.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on MongoDB, VelocityDB

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	VelocityDB
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.	It is a C# .NET NoSQL Object Database that can be Embedded/Distributed, extended as Graph Database is VelocityGraph. It supports both embedded and distributed deployments.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Acid Transactional; Android; Any CPU (32bit/64bit); Array support; Auto Increment on a field; Backup & Restore; Choice of data structure to use; Compression of data; Data Fragmentation
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 1
Followers 82.0K	Followers 19
Votes 4.1K	Votes 0
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	No community feedback yet
Integrations
No integrations available	MySQL PostgreSQL Velocity.js .NET GraphQL C#

What are some alternatives to MongoDB, VelocityDB?

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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.

Related Comparisons

MongoDB vs VelocityDB: What are the differences?

Introduction: When comparing MongoDB and VelocityDB, there are several key differences to consider in terms of their functionality and capabilities.

Data Model: MongoDB is a document-oriented database that stores data in JSON-like documents, making it easy to store and retrieve complex data structures. On the other hand, VelocityDB is an object-oriented database management system that stores data as objects, providing more flexibility in data modeling and relationships.
Indexes and Querying: MongoDB utilizes indexes to optimize query performance, allowing for faster retrieval of specific data. VelocityDB, on the other hand, leverages object browsing and LINQ queries for data retrieval, providing a different approach to querying data.
Scalability: MongoDB is known for its horizontal scalability, allowing for easy distribution of data across multiple nodes or servers to handle large amounts of data. VelocityDB, while also scalable, may require more manual configuration for horizontal scaling due to its object-oriented nature.
ACID Transactions: MongoDB supports ACID transactions at the document level, ensuring data integrity and consistency. In contrast, VelocityDB offers ACID transactions at the object level, providing granularity in transaction management.
Community and Support: MongoDB has a large and active open-source community, providing extensive documentation, tutorials, and support resources. VelocityDB, being a commercial database system, offers dedicated customer support and assistance for users.
Compatibility: When it comes to compatibility, MongoDB is widely supported across various operating systems and programming languages, making it versatile for different development environments. VelocityDB may have more limited compatibility depending on the specific requirements of the application.

MongoDB vs VelocityDB

Overview