What is SQLite and what are its top alternatives?
Top Alternatives to 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. ...
- 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
Embedded NoSQL database for .NET. An open source MongoDB-like database with zero configuration - mobile ready ...
- 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
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
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 is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...
SQLite alternatives & related posts
- Sql800
- Free678
- Easy561
- Widely used527
- Open source488
- High availability180
- Cross-platform support160
- Great community104
- Secure78
- Full-text indexing and searching75
- Fast, open, available25
- SSL support16
- Reliable15
- Robust14
- Enterprise Version8
- Easy to set up on all platforms7
- NoSQL access to JSON data type2
- Relational database1
- Easy, light, scalable1
- Sequel Pro (best SQL GUI)1
- Replica Support1
- Owned by a company with their own agenda16
- Can't roll back schema changes3
related MySQL posts
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.
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:
- Relational database759
- High availability510
- Enterprise class database439
- Sql382
- Sql + nosql304
- Great community173
- Easy to setup147
- Heroku131
- Secure by default130
- Postgis113
- Supports Key-Value50
- Great JSON support48
- Cross platform34
- Extensible32
- Replication28
- Triggers26
- Rollback23
- Multiversion concurrency control22
- Open source21
- Heroku Add-on18
- Stable, Simple and Good Performance17
- Powerful15
- Lets be serious, what other SQL DB would you go for?13
- Good documentation11
- Reliable8
- Scalable8
- Intelligent optimizer8
- Modern7
- Transactional DDL7
- Free7
- One stop solution for all things sql no matter the os6
- Relational database with MVCC5
- Faster Development5
- Full-Text Search4
- Developer friendly4
- Free version3
- Great DB for Transactional system or Application3
- search3
- Open-source3
- Excellent source code3
- Relational datanbase3
- Full-text2
- Text2
- Table/index bloatings10
related PostgreSQL posts
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!
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.
- Document-oriented storage828
- No sql594
- Ease of use553
- Fast464
- High performance410
- Free257
- Open source218
- Flexible180
- Replication & high availability145
- Easy to maintain112
- Querying42
- Easy scalability39
- Auto-sharding38
- High availability37
- Map/reduce31
- Document database27
- Easy setup25
- Full index support25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Support for many languages through different drivers4
- Drivers support is good3
- Aggregation Framework3
- Schemaless3
- Fast2
- Managed service2
- Easy to Scale2
- Awesome2
- Consistent2
- Good GUI1
- Acid Compliant1
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language1
related MongoDB posts
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!
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.
- No Sql6
- Portable5
- Easy to use4
- Document oriented storage3
- Bring up or extend a database very quickly2
- Open Source2
- Capable of storing images or documents2
- Online documentation needs improvement2
- Needs more real world examples2
related LiteDB posts
- Realtime backend made easy370
- Fast and responsive269
- Easy setup241
- Real-time214
- JSON191
- Free134
- Backed by google127
- Angular adaptor82
- Reliable68
- Great customer support36
- Great documentation32
- Real-time synchronization25
- Mobile friendly21
- Rapid prototyping18
- Great security14
- Automatic scaling12
- Freakingly awesome11
- Chat8
- Super fast development8
- Angularfire is an amazing addition!8
- Built in user auth/oauth6
- Awesome next-gen backend6
- Firebase hosting6
- Ios adaptor6
- Speed of light4
- Very easy to use4
- Brilliant for startups3
- Great3
- It's made development super fast3
- JS Offline and Sync suport2
- Push notification2
- Free hosting2
- Cloud functions2
- Low battery consumption2
- The concurrent updates create a great experience2
- .net2
- I can quickly create static web apps with no backend2
- Great all-round functionality2
- Free authentication solution2
- Simple and easy1
- Google's support1
- Free SSL1
- Faster workflow1
- Easy to use1
- Large1
- Easy Reactjs integration1
- Serverless1
- Good Free Limits1
- CDN & cache out of the box1
- Can become expensive31
- No open source, you depend on external company16
- Scalability is not infinite15
- Not Flexible Enough9
- Cant filter queries7
- Very unstable server3
- No Relational Data3
- Too many errors2
- No offline sync2
related Firebase posts
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.
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
IndexedDB
related IndexedDB posts
We hope to develop to Big PWA Hybrid application for our company (Which should include a good performance). For that, What is the best database out of IndexedDB and SQLite? What would be the best one to choose from?
- Extensive documentation1
- Pushing bulk data to server easily1
- Easy to understand the transaction of data1
related Android Room posts
- Performance884
- Super fast541
- Ease of use512
- In-memory cache443
- Advanced key-value cache323
- Open source193
- Easy to deploy182
- Stable164
- Free155
- Fast121
- High-Performance42
- High Availability40
- Data Structures34
- Very Scalable32
- Replication24
- Pub/Sub22
- Great community22
- "NoSQL" key-value data store19
- Hashes15
- Sets13
- Sorted Sets11
- Lists10
- BSD licensed9
- NoSQL9
- Integrates super easy with Sidekiq for Rails background8
- Async replication8
- Bitmaps8
- Open Source7
- Keys with a limited time-to-live7
- Lua scripting6
- Strings6
- Awesomeness for Free5
- Hyperloglogs5
- Written in ANSI C4
- LRU eviction of keys4
- Networked4
- Outstanding performance4
- Runs server side LUA4
- Transactions4
- Feature Rich4
- Performance & ease of use3
- Data structure server3
- Object [key/value] size each 500 MB2
- Simple2
- Scalable2
- Temporarily kept on disk2
- Dont save data if no subscribers are found2
- Automatic failover2
- Easy to use2
- Existing Laravel Integration2
- Channels concept2
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1
related Redis posts
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.
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.