MongoDB vs UnQLite

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

UnQLite

Stacks6

Followers51

Votes0

GitHub Stars2.3K

Forks174

MongoDB vs UnQLite: What are the differences?

MongoDB and UnQLite are both popular NoSQL databases used for storing and managing data. While MongoDB is a document-oriented database, UnQLite is a lightweight, embedded NoSQL database. Let's explore some key differences between MongoDB and UnQLite:

Data Model: MongoDB uses a flexible document model based on BSON (Binary JSON), allowing nested structures and arrays within documents. On the other hand, UnQLite follows a key/value store approach with support for persistent JSON documents.
Query Language: MongoDB supports a powerful query language that includes a rich set of operators and aggregation framework. UnQLite, on the other hand, uses a simple query and document retrieval API without the complexities of SQL-like queries.
Performance: Due to its architecture and indexing capabilities, MongoDB is well-suited for handling large-scale applications with high read and write workloads. UnQLite, being a lightweight database, may not offer the same level of performance for complex operations.
Scalability: MongoDB provides horizontal scalability through sharding, allowing the distribution of data across multiple clusters for high availability and performance. UnQLite, being an embedded database, may have limitations in terms of scalability compared to MongoDB.
Use Cases: MongoDB is commonly used for applications that require real-time analytics, content management, and high-volume data processing. UnQLite, on the other hand, is preferred for embedded systems, IoT devices, and applications where a lightweight database is needed.
Community and Ecosystem: MongoDB has a large and active community with extensive documentation, support, and a wide range of tools and integrations available. UnQLite, being a lesser-known database, may have a smaller community and ecosystem of resources.

In summary, MongoDB and UnQLite differ in their data model, query language, performance, scalability, use cases, and community support. Each database has its own strengths and weaknesses, making them suitable for different types of applications and environments.

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Advice on MongoDB, UnQLite

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	UnQLite
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.	UnQLite is a in-process software library which implements a self-contained, serverless, zero-configuration, transactional NoSQL database engine. UnQLite is a document store database similar to MongoDB, Redis, CouchDB etc. as well a standard Key/Value store similar to BerkeleyDB, LevelDB, etc.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars 2.3K
GitHub Forks 5.7K	GitHub Forks 174
Stacks 96.6K	Stacks 6
Followers 82.0K	Followers 51
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	Cons 1 Different compilation for each platform

What are some alternatives to MongoDB, UnQLite?

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.

Related Comparisons

MongoDB vs UnQLite: What are the differences?

Data Model: MongoDB uses a flexible document model based on BSON (Binary JSON), allowing nested structures and arrays within documents. On the other hand, UnQLite follows a key/value store approach with support for persistent JSON documents.
Query Language: MongoDB supports a powerful query language that includes a rich set of operators and aggregation framework. UnQLite, on the other hand, uses a simple query and document retrieval API without the complexities of SQL-like queries.
Performance: Due to its architecture and indexing capabilities, MongoDB is well-suited for handling large-scale applications with high read and write workloads. UnQLite, being a lightweight database, may not offer the same level of performance for complex operations.
Scalability: MongoDB provides horizontal scalability through sharding, allowing the distribution of data across multiple clusters for high availability and performance. UnQLite, being an embedded database, may have limitations in terms of scalability compared to MongoDB.
Use Cases: MongoDB is commonly used for applications that require real-time analytics, content management, and high-volume data processing. UnQLite, on the other hand, is preferred for embedded systems, IoT devices, and applications where a lightweight database is needed.
Community and Ecosystem: MongoDB has a large and active community with extensive documentation, support, and a wide range of tools and integrations available. UnQLite, being a lesser-known database, may have a smaller community and ecosystem of resources.

MongoDB vs UnQLite

Overview