Alternatives to SQLite logo

Alternatives to SQLite

MySQL, PostgreSQL, MongoDB, LiteDB, and Firebase are the most popular alternatives and competitors to SQLite.
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What is SQLite and what are its top alternatives?

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.
SQLite is a tool in the Databases category of a tech stack.

Top Alternatives to SQLite

  • 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. ...

  • 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. ...

  • LiteDB

    LiteDB

    Embedded NoSQL database for .NET. An open source MongoDB-like database with zero configuration - mobile ready ...

  • Firebase

    Firebase

    Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...

  • IndexedDB

    IndexedDB

    This API uses indexes to enable high-performance searches of this data. While Web Storage is useful for storing smaller amounts of data, it is less useful for storing larger amounts of structured data. ...

  • Android Room

    Android Room

    It provides an abstraction layer over SQLite to allow fluent database access while harnessing the full power of SQLite. Apps that handle non-trivial amounts of structured data can benefit greatly from persisting that data locally. The most common use case is to cache relevant pieces of data. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

SQLite alternatives & related posts

MySQL logo

MySQL

77.4K
61.5K
3.7K
The world's most popular open source database
77.4K
61.5K
+ 1
3.7K
PROS OF MYSQL
  • 790
    Sql
  • 673
    Free
  • 557
    Easy
  • 525
    Widely used
  • 485
    Open source
  • 180
    High availability
  • 158
    Cross-platform support
  • 103
    Great community
  • 78
    Secure
  • 75
    Full-text indexing and searching
  • 25
    Fast, open, available
  • 14
    SSL support
  • 13
    Reliable
  • 13
    Robust
  • 8
    Enterprise Version
  • 7
    Easy to set up on all platforms
  • 2
    NoSQL access to JSON data type
  • 1
    Replica Support
  • 1
    Easy, light, scalable
  • 1
    Relational database
  • 1
    Sequel Pro (best SQL GUI)
CONS OF MYSQL
  • 14
    Owned by a company with their own agenda
  • 1
    Can't roll back schema changes

related MySQL posts

Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1M views

Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

https://eng.uber.com/mysql-migration/

See more
PostgreSQL logo

PostgreSQL

58.4K
46.2K
3.5K
A powerful, open source object-relational database system
58.4K
46.2K
+ 1
3.5K
PROS OF POSTGRESQL
  • 754
    Relational database
  • 506
    High availability
  • 436
    Enterprise class database
  • 380
    Sql
  • 302
    Sql + nosql
  • 171
    Great community
  • 145
    Easy to setup
  • 129
    Heroku
  • 128
    Secure by default
  • 111
    Postgis
  • 48
    Supports Key-Value
  • 46
    Great JSON support
  • 32
    Cross platform
  • 29
    Extensible
  • 26
    Replication
  • 24
    Triggers
  • 22
    Rollback
  • 21
    Multiversion concurrency control
  • 20
    Open source
  • 17
    Heroku Add-on
  • 14
    Stable, Simple and Good Performance
  • 13
    Powerful
  • 12
    Lets be serious, what other SQL DB would you go for?
  • 9
    Good documentation
  • 7
    Intelligent optimizer
  • 7
    Scalable
  • 6
    Transactional DDL
  • 6
    Modern
  • 6
    Reliable
  • 5
    Free
  • 5
    One stop solution for all things sql no matter the os
  • 4
    Relational database with MVCC
  • 3
    Faster Development
  • 3
    Full-Text Search
  • 3
    Developer friendly
  • 2
    Excellent source code
  • 2
    search
  • 2
    Great DB for Transactional system or Application
  • 1
    Full-text
  • 1
    Free version
  • 1
    Text
  • 1
    Open-source
CONS OF POSTGRESQL
  • 9
    Table/index bloatings

related PostgreSQL posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

See more
Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

See more
MongoDB logo

MongoDB

58.1K
47.7K
4.1K
The database for giant ideas
58.1K
47.7K
+ 1
4.1K
PROS OF MONGODB
  • 822
    Document-oriented storage
  • 589
    No sql
  • 545
    Ease of use
  • 464
    Fast
  • 405
    High performance
  • 254
    Free
  • 214
    Open source
  • 178
    Flexible
  • 141
    Replication & high availability
  • 108
    Easy to maintain
  • 40
    Querying
  • 36
    Easy scalability
  • 35
    Auto-sharding
  • 34
    High availability
  • 30
    Map/reduce
  • 26
    Document database
  • 24
    Easy setup
  • 24
    Full index support
  • 15
    Reliable
  • 14
    Fast in-place updates
  • 13
    Agile programming, flexible, fast
  • 11
    No database migrations
  • 7
    Enterprise
  • 7
    Easy integration with Node.Js
  • 5
    Enterprise Support
  • 4
    Great NoSQL DB
  • 3
    Aggregation Framework
  • 3
    Support for many languages through different drivers
  • 3
    Drivers support is good
  • 2
    Schemaless
  • 2
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 2
    Easy to Scale
  • 1
    Consistent
CONS OF MONGODB
  • 5
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language

related MongoDB posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

See more
Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more
LiteDB logo

LiteDB

27
134
24
A .Net NoSQL Document Store in a single data file
27
134
+ 1
24
PROS OF LITEDB
  • 6
    No Sql
  • 5
    Portable
  • 4
    Easy to use
  • 3
    Document oriented storage
  • 2
    Bring up or extend a database very quickly
  • 2
    Open Source
  • 2
    Capable of storing images or documents
CONS OF LITEDB
  • 2
    Online documentation needs improvement
  • 2
    Needs more real world examples

related LiteDB posts

Firebase logo

Firebase

25.3K
21.2K
1.9K
The Realtime App Platform
25.3K
21.2K
+ 1
1.9K
PROS OF FIREBASE
  • 361
    Realtime backend made easy
  • 263
    Fast and responsive
  • 234
    Easy setup
  • 207
    Real-time
  • 186
    JSON
  • 127
    Free
  • 121
    Backed by google
  • 81
    Angular adaptor
  • 63
    Reliable
  • 36
    Great customer support
  • 26
    Great documentation
  • 23
    Real-time synchronization
  • 20
    Mobile friendly
  • 17
    Rapid prototyping
  • 12
    Great security
  • 11
    Automatic scaling
  • 10
    Freakingly awesome
  • 8
    Chat
  • 8
    Angularfire is an amazing addition!
  • 8
    Super fast development
  • 6
    Awesome next-gen backend
  • 6
    Ios adaptor
  • 5
    Built in user auth/oauth
  • 5
    Firebase hosting
  • 4
    Speed of light
  • 4
    Very easy to use
  • 3
    It's made development super fast
  • 3
    Great
  • 3
    Brilliant for startups
  • 2
    Great all-round functionality
  • 2
    Low battery consumption
  • 2
    I can quickly create static web apps with no backend
  • 2
    The concurrent updates create a great experience
  • 2
    JS Offline and Sync suport
  • 1
    Faster workflow
  • 1
    Large
  • 1
    Serverless
  • 1
    .net
  • 1
    Free SSL
  • 1
    Good Free Limits
  • 1
    Push notification
  • 1
    Easy to use
  • 1
    Easy Reactjs integration
CONS OF FIREBASE
  • 28
    Can become expensive
  • 15
    Scalability is not infinite
  • 14
    No open source, you depend on external company
  • 9
    Not Flexible Enough
  • 5
    Cant filter queries
  • 3
    Very unstable server
  • 2
    Too many errors
  • 2
    No Relational Data

related Firebase posts

Stephen Gheysens
Senior Solutions Engineer at Twilio · | 13 upvotes · 184.6K views

Hi Otensia! I'd definitely recommend using the skills you've already got and building with JavaScript is a smart way to go these days. Most platform services have JavaScript/Node SDKs or NPM packages, many serverless platforms support Node in case you need to write any backend logic, and JavaScript is incredibly popular - meaning it will be easy to hire for, should you ever need to.

My advice would be "don't reinvent the wheel". If you already have a skill set that will work well to solve the problem at hand, and you don't need it for any other projects, don't spend the time jumping into a new language. If you're looking for an excuse to learn something new, it would be better to invest that time in learning a new platform/tool that compliments your knowledge of JavaScript. For this project, I might recommend using Netlify, Vercel, or Google Firebase to quickly and easily deploy your web app. If you need to add user authentication, there are great examples out there for Firebase Authentication, Auth0, or even Magic (a newcomer on the Auth scene, but very user friendly). All of these services work very well with a JavaScript-based application.

See more
Tassanai Singprom

This is my stack in Application & Data

JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

My Utilities Tools

Google Analytics Postman Elasticsearch

My Devops Tools

Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

My Business Tools

Slack

See more
IndexedDB logo

IndexedDB

26
55
0
A low-level API for client-side storage of significant amounts of structured data
26
55
+ 1
0
PROS OF INDEXEDDB
    Be the first to leave a pro
    CONS OF INDEXEDDB
      Be the first to leave a con

      related IndexedDB posts

      Android Room logo

      Android Room

      155
      188
      2
      Save data in a local database
      155
      188
      + 1
      2
      PROS OF ANDROID ROOM
      • 1
        Pushing bulk data to server easily
      • 1
        Easy to understand the transaction of data
      CONS OF ANDROID ROOM
        Be the first to leave a con

        related Android Room posts

        Redis logo

        Redis

        39.2K
        28.9K
        3.9K
        An in-memory database that persists on disk
        39.2K
        28.9K
        + 1
        3.9K
        PROS OF REDIS
        • 877
          Performance
        • 535
          Super fast
        • 511
          Ease of use
        • 441
          In-memory cache
        • 321
          Advanced key-value cache
        • 190
          Open source
        • 179
          Easy to deploy
        • 163
          Stable
        • 153
          Free
        • 120
          Fast
        • 40
          High-Performance
        • 39
          High Availability
        • 34
          Data Structures
        • 32
          Very Scalable
        • 23
          Replication
        • 20
          Great community
        • 19
          Pub/Sub
        • 17
          "NoSQL" key-value data store
        • 14
          Hashes
        • 12
          Sets
        • 10
          Sorted Sets
        • 9
          Lists
        • 8
          BSD licensed
        • 8
          NoSQL
        • 7
          Async replication
        • 7
          Integrates super easy with Sidekiq for Rails background
        • 7
          Bitmaps
        • 6
          Open Source
        • 6
          Keys with a limited time-to-live
        • 5
          Strings
        • 5
          Lua scripting
        • 4
          Awesomeness for Free!
        • 4
          Hyperloglogs
        • 3
          outstanding performance
        • 3
          Runs server side LUA
        • 3
          Networked
        • 3
          LRU eviction of keys
        • 3
          Written in ANSI C
        • 3
          Feature Rich
        • 3
          Transactions
        • 2
          Data structure server
        • 2
          Performance & ease of use
        • 1
          Existing Laravel Integration
        • 1
          Automatic failover
        • 1
          Easy to use
        • 1
          Object [key/value] size each 500 MB
        • 1
          Simple
        • 1
          Channels concept
        • 1
          Scalable
        • 1
          Temporarily kept on disk
        • 1
          Dont save data if no subscribers are found
        • 0
          Jk
        CONS OF REDIS
        • 12
          Cannot query objects directly
        • 1
          No WAL
        • 1
          No secondary indexes for non-numeric data types

        related Redis posts

        Robert Zuber

        We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

        As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

        When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

        See more

        I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

        We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

        Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

        We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

        Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

        See more