MongoDB vs SQLite

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

SQLite

Stacks19.9K

Followers15.2K

Votes535

MongoDB vs SQLite: What are the differences?

Introduction

This Markdown code provides a comparison between MongoDB and SQLite, highlighting their key differences.

Storage Structure: MongoDB is a document-oriented database that stores data in a flexible, JSON-like structure called BSON (Binary JSON). It allows for nested data and dynamic schema. On the other hand, SQLite is a relational database that stores data in predefined tables with fixed columns and rows.
Scalability: MongoDB is designed to scale horizontally, allowing for the distribution of data across multiple servers or clusters to handle high volumes of traffic and large datasets. In contrast, SQLite is primarily designed for single-machine use and lacks built-in support for distributed computing.
Query Language: MongoDB uses a powerful and flexible query language known as the MongoDB Query Language (MQL), which supports a wide range of operators and expressions for querying and manipulating data. SQLite, being a relational database, uses SQL (Structured Query Language) for querying and managing data.
Transaction Support: MongoDB provides support for multi-document transactions, allowing operations on multiple documents to be grouped together and either succeed or fail as a single unit. SQLite also supports transactions, but it operates at the level of individual SQL statements rather than documents.
Schema Design: MongoDB's flexible schema design allows for dynamic changes to the schema, making it suitable for evolving data structures and use cases where the schema may change frequently. In contrast, SQLite requires a predefined schema and enforces data integrity through the use of constraints such as primary keys and foreign keys.
Concurrency: MongoDB can handle concurrent read and write operations efficiently, especially when distributed across multiple servers or clusters. SQLite, being a file-based database, has limited concurrency capabilities and is best suited for single-user or low-concurrency scenarios.

In summary, MongoDB and SQLite differ in their storage structure, scalability, query language, transaction support, schema design, and concurrency capabilities.

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Advice on MongoDB, SQLite

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	SQLite
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.	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.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 19.9K
Followers 82.0K	Followers 15.2K
Votes 4.1K	Votes 535
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 163 Lightweight 135 Portable 122 Simple 81 Sql 29 Preinstalled on iOS and Android Cons 2 Not for multi-process of multithreaded apps 1 Needs different binaries for each platform

What are some alternatives to MongoDB, SQLite?

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.

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.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

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MongoDB vs SQLite: What are the differences?

Introduction

This Markdown code provides a comparison between MongoDB and SQLite, highlighting their key differences.

Storage Structure: MongoDB is a document-oriented database that stores data in a flexible, JSON-like structure called BSON (Binary JSON). It allows for nested data and dynamic schema. On the other hand, SQLite is a relational database that stores data in predefined tables with fixed columns and rows.
Scalability: MongoDB is designed to scale horizontally, allowing for the distribution of data across multiple servers or clusters to handle high volumes of traffic and large datasets. In contrast, SQLite is primarily designed for single-machine use and lacks built-in support for distributed computing.
Query Language: MongoDB uses a powerful and flexible query language known as the MongoDB Query Language (MQL), which supports a wide range of operators and expressions for querying and manipulating data. SQLite, being a relational database, uses SQL (Structured Query Language) for querying and managing data.
Transaction Support: MongoDB provides support for multi-document transactions, allowing operations on multiple documents to be grouped together and either succeed or fail as a single unit. SQLite also supports transactions, but it operates at the level of individual SQL statements rather than documents.
Schema Design: MongoDB's flexible schema design allows for dynamic changes to the schema, making it suitable for evolving data structures and use cases where the schema may change frequently. In contrast, SQLite requires a predefined schema and enforces data integrity through the use of constraints such as primary keys and foreign keys.
Concurrency: MongoDB can handle concurrent read and write operations efficiently, especially when distributed across multiple servers or clusters. SQLite, being a file-based database, has limited concurrency capabilities and is best suited for single-user or low-concurrency scenarios.

In summary, MongoDB and SQLite differ in their storage structure, scalability, query language, transaction support, schema design, and concurrency capabilities.