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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. MongoDB vs NeDB

MongoDB vs NeDB

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
NeDB
NeDB
Stacks37
Followers85
Votes0
GitHub Stars13.6K
Forks1.0K

MongoDB vs NeDB: What are the differences?

Introduction

MongoDB and NeDB are both document-oriented NoSQL databases, designed to handle large volumes of data with high performance and scalability. While they share some similarities, there are key differences between them that differentiate their features and functionalities.

  1. Data Storage and Querying: MongoDB is a fully-featured database that supports complex querying, indexing, and aggregation. Its query language allows for powerful filtering, sorting, and data manipulation. On the other hand, NeDB is a lightweight database that provides a simpler querying mechanism with fewer features. It supports basic querying operations but lacks advanced querying capabilities like indexing and aggregation.

  2. Scalability and Cluster Support: MongoDB is designed to handle large-scale applications with horizontal scalability. It supports sharding, which allows distributing data across multiple servers, and replica sets for high availability. NeDB, on the other hand, is primarily designed for small-scale applications and does not provide native support for clustering or scalability beyond a single instance.

  3. Disk Space and Memory Consumption: MongoDB consumes more disk space and memory compared to NeDB. MongoDB stores data in a binary JSON (BSON) format, which requires additional space compared to NeDB's plain JSON data storage. It also requires more memory for indexing and caching due to its more extensive features.

  4. Durability and Write Performance: MongoDB provides configurable write durability options, allowing users to choose between different consistency levels. It offers synchronous writes, ensuring data durability at the cost of lower write performance. NeDB, on the other hand, focuses on write performance and provides asynchronous writes by default, sacrificing some durability. This makes NeDB suitable for use cases where performance is critical, but data loss in case of failure is acceptable.

  5. Deployment and Integration: MongoDB is a client-server database that requires a dedicated MongoDB server to be set up and maintained. It supports multiple programming languages and has extensive integrations with various frameworks and tools. NeDB, on the other hand, is an embedded database, which means it operates within the application itself without the need for a separate server. It provides a lightweight and simplified integration experience suitable for single-application deployments.

  6. License and Community Support: MongoDB is released under the Server Side Public License (SSPL), which is not recognized as an open-source license by many organizations. This has led to some concerns and limitations in its adoption. NeDB, on the other hand, is released under the MIT license, which is widely accepted and allows for more unrestricted usage and contribution. NeDB also has a smaller but active community that provides support and contributes to its development.

In summary, MongoDB and NeDB differ in terms of their feature set, scalability, performance characteristics, deployment options, licensing, and community support. MongoDB provides a robust and feature-rich solution for large-scale applications, while NeDB offers a simpler and lightweight approach suitable for smaller applications with performance as a primary concern.

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Advice on MongoDB, NeDB

George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

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
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
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
MongoDB
NeDB
NeDB

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.

Embedded persistent or in memory database for Node.js, nw.js, Electron and browsers, 100% JavaScript, no binary dependency. API is a subset of MongoDB's and it's plenty fast.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
In-memory datastore; Persistent datastore;Equivalent of a MongoDB collection; JavaScript database
Statistics
GitHub Stars
27.7K
GitHub Stars
13.6K
GitHub Forks
5.7K
GitHub Forks
1.0K
Stacks
96.6K
Stacks
37
Followers
82.0K
Followers
85
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
Electron
Electron
Node.js
Node.js
JavaScript
JavaScript

What are some alternatives to MongoDB, NeDB?

MySQL

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

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

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

SQLite

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

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

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

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

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

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

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.

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