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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Mongoose vs SQLite

Mongoose vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535
Mongoose
Mongoose
Stacks2.4K
Followers1.4K
Votes56

Mongoose vs SQLite: What are the differences?

  1. 1. Data Storage and Structure: Mongoose is an Object Data Modeling (ODM) tool for MongoDB, a NoSQL database, while SQLite is a relational database management system. Mongoose stores data in collections and documents, similar to JSON format, while SQLite stores data in tables with rows and columns, following a structured schema.

  2. 2. Query Language: Mongoose uses the MongoDB query language, which provides a flexible and powerful way to query data in a NoSQL database. SQLite, on the other hand, uses SQL (Structured Query Language), a standard language for managing relational databases. SQL offers a wide range of query capabilities and supports complex operations like joins, subqueries, and aggregations.

  3. 3. Scalability and Performance: Mongoose and MongoDB are designed to handle large amounts of unstructured data and can scale horizontally by adding more servers to a clustered environment. SQLite, being a file-based database, is optimized for single-machine deployments and may not be suitable for high-performance applications with a huge amount of concurrent reads and writes.

  4. 4. Data Relationships and Transactions: Mongoose allows the definition of relationships between different collections using references or embedding documents. It also supports transactions, which ensure the integrity of multiple database operations. SQLite, being a relational database, fully supports relationships through primary and foreign keys and also provides ACID-compliant transactions for maintaining data consistency.

  5. 5. Deployment and Setup: Mongoose requires MongoDB to be installed and configured, which involves setting up a database server and managing the connection. SQLite, on the other hand, is a self-contained database engine that does not require any separate server installation and can be easily embedded within an application.

  6. 6. Compatibility and Ecosystem: Mongoose is specifically designed for MongoDB, and its features and functionality are tightly integrated with the MongoDB ecosystem. SQLite, on the other hand, is a widely adopted database technology that has support across various programming languages and frameworks, making it more compatible and easily accessible for different development scenarios.

In Summary, Mongoose and SQLite differ in their data storage and structure, query language, scalability, data relationships and transactions, deployment and setup, and compatibility with different ecosystems.

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Advice on SQLite, Mongoose

Dimelo
Dimelo

Nov 5, 2020

Needs adviceonSQLiteSQLiteMySQLMySQLPostgreSQLPostgreSQL

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

671k views671k
Comments
Stephen
Stephen

Senior DevOps Engineer at Vital Beats

Nov 9, 2020

Review

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

316k views316k
Comments
Jasmine
Jasmine

Feb 12, 2021

Decided

Backend:

  • Considering that our main app functionality involves data processing, we chose Python as the programming language because it offers many powerful math libraries for data-related tasks. We will use Flask for the server due to its good integration with Python. We will use a relational database because it has good performance and we are mostly dealing with CSV files that have a fixed structure. We originally chose SQLite, but after realizing the limitations of file-based databases, we decided to switch to PostgreSQL, which has better compatibility with our hosting service, Heroku.
175k views175k
Comments

Detailed Comparison

SQLite
SQLite
Mongoose
Mongoose

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.

Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Mongoose provides a straight-forward, schema-based solution to modeling your application data and includes built-in type casting, validation, query building, business logic hooks and more, out of the box.

Statistics
Stacks
19.9K
Stacks
2.4K
Followers
15.2K
Followers
1.4K
Votes
535
Votes
56
Pros & Cons
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
Pros
  • 17
    Several bad ideas mixed together
  • 17
    Well documented
  • 10
    JSON
  • 8
    Actually terrible documentation
  • 2
    Recommended and used by Valve. See steamworks docs
Cons
  • 3
    Model middleware/hooks are not user friendly
Integrations
No integrations available
Node.js
Node.js
MongoDB
MongoDB

What are some alternatives to SQLite, Mongoose?

MongoDB

MongoDB

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.

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.

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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