Alternatives to WatermelonDB logo

Alternatives to WatermelonDB

Pouchdb, Realm, MySQL, PostgreSQL, and MongoDB are the most popular alternatives and competitors to WatermelonDB.
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What is WatermelonDB and what are its top alternatives?

WatermelonDB is a new way of dealing with user data in React Native and React web apps. It's optimized for building complex applications in React Native, and the number one goal is real-world performance. In simple words, your app must launch fast.
WatermelonDB is a tool in the Databases category of a tech stack.
WatermelonDB is an open source tool with 6.5K GitHub stars and 304 GitHub forks. Here’s a link to WatermelonDB's open source repository on GitHub

Top Alternatives of WatermelonDB

WatermelonDB alternatives & related posts

Pouchdb logo

Pouchdb

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Open-source JavaScript database inspired by Apache CouchDB that's designed to run well within the browser
Pouchdb logo
Pouchdb
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WatermelonDB logo
WatermelonDB

related Pouchdb posts

Jonathan Pugh
Jonathan Pugh
Software Engineer / Project Manager / Technical Architect · | 24 upvotes · 880.7K views
Framework7
Framework7
JavaScript
JavaScript
TypeScript
TypeScript
Figma
Figma
Visual Studio Code
Visual Studio Code
Webpack
Webpack
Babel
Babel
Ruby
Ruby
HTML5
HTML5
CouchDB
CouchDB
Pouchdb
Pouchdb
Font Awesome
Font Awesome
Apache Cordova
Apache Cordova
CSS 3
CSS 3
PhoneGap
PhoneGap
#Css
#CSS3
#SCSS
#Sass
#Less
#Electron
#HandleBars
#Template7
#Sketch
#GraphQL
#HTML5
#GraphCool

I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:

For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.

Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.

I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.

I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).

I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.

I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.

For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.

For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.

For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.

I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.

So that's my latest mobile stack. What tools do you use? Have you tried these ones?

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Josh Dzielak
Josh Dzielak
Developer Advocate at DeveloperMode · | 5 upvotes · 136.8K views
Firebase
Firebase
Pouchdb
Pouchdb
CouchDB
CouchDB
Cloudant
Cloudant

As a side project, I was building a note taking app that needed to synchronize between the client and the server so that it would work offline. At first I used Firebase to store the data on the server and wrote my own code to cache Firebase data in local storage and synchronize it. This was brittle and not performant. I figured that someone else must have solved this in a better way so I went looking for a better solution.

I needed a tool where I could write the data once and it would write to client and server, and when clients came back on line they would automatically catch the client up. I also needed conflict resolution. I was thrilled to discover Pouchdb and its server-side counterpart CouchDB. Together, they met nearly all of my requirements and were very easy to implement - I was able to remove a ton of custom code and have found the synchronization to be very robust. Pouchdb 7 has improved mobile support too, so I can run the app on iOS or Android browsers.

My Couchdb instance is actually a Cloudant instance running on IBM Bluemix. For my fairly low level of API usage, it's been totally free, and it has a decent GUI for managing users and replications.

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Realm logo

Realm

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Realm makes it easy to build reactive apps, realtime collaborative features, and offline-first experiences.
Realm logo
Realm
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WatermelonDB
MySQL logo

MySQL

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The world's most popular open source database
MySQL logo
MySQL
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Tim Abbott
Tim Abbott
Founder at Zulip · | 23 upvotes · 340.4K views
atZulipZulip
PostgreSQL
PostgreSQL
MySQL
MySQL
Elasticsearch
Elasticsearch

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
Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 1.3M views
atSmartZipSmartZip
Rails
Rails
Rails API
Rails API
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Capistrano
Capistrano
Docker
Docker
Amazon S3
Amazon S3
Amazon RDS
Amazon RDS
MySQL
MySQL
Amazon RDS for Aurora
Amazon RDS for Aurora
Amazon ElastiCache
Amazon ElastiCache
Memcached
Memcached
Amazon CloudFront
Amazon CloudFront
Segment
Segment
Zapier
Zapier
Amazon Redshift
Amazon Redshift
Amazon Quicksight
Amazon Quicksight
Superset
Superset
Elasticsearch
Elasticsearch
Amazon Elasticsearch Service
Amazon Elasticsearch Service
New Relic
New Relic
AWS Lambda
AWS Lambda
Node.js
Node.js
Ruby
Ruby
Amazon DynamoDB
Amazon DynamoDB
Algolia
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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PostgreSQL logo

PostgreSQL

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A powerful, open source object-relational database system
PostgreSQL logo
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Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 25 upvotes · 888.4K views
atFundsCornerFundsCorner
MongoDB
MongoDB
PostgreSQL
PostgreSQL
MongoDB Stitch
MongoDB Stitch
Node.js
Node.js
Amazon SQS
Amazon SQS
Python
Python
SQLAlchemy
SQLAlchemy
AWS Lambda
AWS Lambda
Zappa
Zappa

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
Tim Abbott
Founder at Zulip · | 23 upvotes · 340.4K views
atZulipZulip
PostgreSQL
PostgreSQL
MySQL
MySQL
Elasticsearch
Elasticsearch

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

related MongoDB posts

Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 25 upvotes · 888.4K views
atFundsCornerFundsCorner
MongoDB
MongoDB
PostgreSQL
PostgreSQL
MongoDB Stitch
MongoDB Stitch
Node.js
Node.js
Amazon SQS
Amazon SQS
Python
Python
SQLAlchemy
SQLAlchemy
AWS Lambda
AWS Lambda
Zappa
Zappa

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
Robert Zuber
CTO at CircleCI · | 22 upvotes · 742.2K views
atCircleCICircleCI
MongoDB
MongoDB
PostgreSQL
PostgreSQL
Redis
Redis
GitHub
GitHub
Amazon S3
Amazon S3

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.

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related Microsoft SQL Server posts

AWS Elastic Beanstalk
AWS Elastic Beanstalk
Heroku
Heroku
Ruby
Ruby
Rails
Rails
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
MariaDB
MariaDB
Microsoft SQL Server
Microsoft SQL Server
Amazon RDS
Amazon RDS
AWS Lambda
AWS Lambda
Python
Python
Redis
Redis
Memcached
Memcached
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Amazon ElastiCache
Amazon ElastiCache

We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

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Erin G
Erin G
Linux
Linux
PostgreSQL
PostgreSQL
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

  1. I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
  2. I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.
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related MariaDB posts

AWS Elastic Beanstalk
AWS Elastic Beanstalk
Heroku
Heroku
Ruby
Ruby
Rails
Rails
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
MariaDB
MariaDB
Microsoft SQL Server
Microsoft SQL Server
Amazon RDS
Amazon RDS
AWS Lambda
AWS Lambda
Python
Python
Redis
Redis
Memcached
Memcached
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Amazon ElastiCache
Amazon ElastiCache

We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

See more
Joshua Dean Küpper
Joshua Dean Küpper
CEO at Scrayos UG (haftungsbeschränkt) · | 7 upvotes · 139.4K views
atScrayos UG (haftungsbeschränkt)Scrayos UG (haftungsbeschränkt)
MariaDB
MariaDB
PostgreSQL
PostgreSQL
GitLab
GitLab
Sentry
Sentry

We primarily use MariaDB but use PostgreSQL as a part of GitLab , Sentry and Nextcloud , which (initially) forced us to use it anyways. While this isn't much of a decision – because we didn't have one (ha ha) – we learned to love the perks and advantages of PostgreSQL anyways. PostgreSQL's extension system makes it even more flexible than a lot of the other SQL-based DBs (that only offer stored procedures) and the additional JOIN options, the enhanced role management and the different authentication options came in really handy, when doing manual maintenance on the databases.

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SQLite logo

SQLite

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A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
SQLite logo
SQLite
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WatermelonDB logo
WatermelonDB

related SQLite posts

Daniel Quinn
Daniel Quinn
Senior Developer at Workfinder · | 2 upvotes · 119.8K views
atThe Paperless ProjectThe Paperless Project
SQLite
SQLite
PostgreSQL
PostgreSQL

SQLite is a tricky beast. It's great if you're working single-threaded, but a Terrible Idea if you've got more than one concurrent connection. You use it because it's easy to setup, light, and portable (it's just a file).

In Paperless, we've built a self-hosted web application, so it makes sense to standardise on something small & light, and as we don't have to worry about multiple connections (it's just you using the app), it's a perfect fit.

For users wanting to scale Paperless up to a multi-user environment though, we do provide the hooks to switch to PostgreSQL .

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SQLite
SQLite
PostgreSQL
PostgreSQL

SQLite for development, PostgreSQL SQL for production databases.

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