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

ArangoDB vs SQLite

OverviewDecisionsComparisonAlternatives

Overview

SQLite
SQLite
Stacks19.9K
Followers15.2K
Votes535
ArangoDB
ArangoDB
Stacks273
Followers442
Votes192

ArangoDB vs SQLite: What are the differences?

<ArangoDB is a distributed NoSQL database system while SQLite is a self-contained, serverless, zero-configuration SQL database engine. These two databases have differences in terms of functionality and use cases.>

  1. Data Model: ArangoDB is a multi-model database that supports key-value, document, and graph data models, allowing flexible data modeling within a single database. On the other hand, SQLite follows a traditional relational data model based on tables and schema definitions, which is ideal for structured data storage.

  2. Scalability: ArangoDB is designed to scale horizontally across multiple servers, making it suitable for large-scale applications with growing data needs. In contrast, SQLite is typically used for smaller projects and lacks built-in support for distributed architectures.

  3. Concurrency: ArangoDB provides multi-threaded, concurrent access to its data, enabling high performance in read and write operations from multiple clients simultaneously. SQLite, on the other hand, is limited in concurrency as it follows a file-based architecture and can encounter write locks when multiple connections try to write to the database concurrently.

  4. Query Language: ArangoDB offers AQL (ArangoDB Query Language), a powerful and expressive query language tailored for complex data retrieval, manipulation, and traversal in a multi-model database environment. SQLite uses standard SQL (Structured Query Language) for querying and data manipulation operations, which is well-known and widely supported within the database community.

  5. Deployment: ArangoDB is typically deployed in distributed environments, allowing for high availability, fault tolerance, and horizontal scalability through cluster setups. In comparison, SQLite is often used as an embedded database within applications or for local storage, where simplicity and self-contained operation are more critical than distributed capabilities.

  6. Use Cases: ArangoDB is well-suited for applications requiring flexible data modeling, graph traversals, and multi-model capabilities, such as social networks, recommendation systems, and IoT platforms. SQLite is commonly used in mobile apps, desktop software, and small-scale web applications that need a lightweight, file-based database without the overhead of client-server architectures.

In Summary, ArangoDB and SQLite differ significantly in their data models, scalability, concurrency, query languages, deployment options, and use cases.

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Advice on SQLite, ArangoDB

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?

670k views670k
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
ArangoDB
ArangoDB

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.

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.

-
multi-model nosql db; acid; transactions; javascript; database; nosql; sharding; replication; query language; joins; aql; documents; graphs; key-values; graphdb
Statistics
Stacks
19.9K
Stacks
273
Followers
15.2K
Followers
442
Votes
535
Votes
192
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
  • 37
    Grahps and documents in one DB
  • 26
    Intuitive and rich query language
  • 25
    Open source
  • 25
    Good documentation
  • 21
    Joins for collections
Cons
  • 3
    Web ui has still room for improvement
  • 2
    No support for blueprints standard, using custom AQL

What are some alternatives to SQLite, ArangoDB?

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.

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.

CouchDB

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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