Offline First Database Comparison

In this project I have implemented the exact same chat application with different database technologies. You can use it to compare metrics and learn about the differences. The chat app is a web based angular application, with functionality similar to Whatsapp Web.

Implemented Databases:

• AWS Ampflify Datastore
• Firebase Firestore
• PouchDB & CouchDB
• RxDB & GraphQL
• WatermelonDB (no backend sync atm)

Metrics

All metrics are measured automatically via code in a browser tests (chrome:headless). The results heavily depend on the developers device. You should compare the values relative to another and not as absolute values. Also you might want to create new metrics that better represent how you would use the respective database.

You can reproduce these values by running sh measure-metrics.sh in the root folder.

Metric \ Project aws firebase pouchdb rxdb watermelondb First angular component render 207ms 270ms 197ms 201ms 210ms Page load time 294ms 178ms 271ms 284ms 293ms First full render 452ms 1475ms 1398ms 1454ms 344ms Insert one message 23ms 380ms 19ms 128ms 9ms Inserting 20 messages one after another 651ms 8012ms 581ms 1884ms 33ms Inserting 20 messages in parallel 187ms 7039ms 126ms 1705ms 1582ms Message insert to message list change 59ms 30ms 386ms 19ms 4ms Message insert to user list change 13ms 192ms 311ms 282ms 6ms Message search query time 485ms 299ms 305ms 90ms 29ms First full render with many messages 408ms 1922ms 1869ms 2287ms 293ms Storage usage 199kb 3478kb 1395kb 2311kb 1824kb Bundle size, plain JavaScript 1545kb 915kb 800kb 1084kb 895kb Bundle size, minified+gzip 358kb 224kb 192kb 278kb 208kb

Metrics Explanation

• Page load time: How long does it take to download and parse the JavaScript bundle.
• First angular component render: How long does it take for the first angular component to be rendered.
• First full render: How long does it take until all relevant data is displayed for the first time.
• Insert one message: How long does it take to insert a single message.
• Inserting 20 messages one after another: How long does it take to insert 20 messages in serial.
• Inserting 20 messages in parallel: How long does it take to insert 20 messages in parallel.
• Message insert to message list change: How long does it take until a new message is rendered to the dom.
• Message insert to user list change: How long does it take until a new messages affects the sort order of the user list.
• Message search query time: How long does it take to search for a given message by regex/like-operator.
• First full render with many messages: Time to first full render when many messages exist.
• Storage usage: Size of the stored IndexedDB database after inserting the full test dataset.
• Bundle size, plain JavaScript: The full JavaScript bundle size, without minification or gzip.
• Bundle size, minified+gzip: The full JavaScript bundle size after minification and gzip compression.

Investigations

Why is WatermelonDB so much faster?

WatermelonDB uses the LokiJS adapter which is an in memory database that regularly persists the data to IndexedDB either on interval, or when the browser tab is closed. Keeping and processing the data in memory has the benefit of being much master, but it also has its downsides:

• Data can be lost when the JavaScript process is killed ungracefully like when the browser crashes or the power of the PC is terminated.
• There is no multi-tab-support. The data is not shared between multiple browser tabs of the same origin.
• There is no concept of conflict handling or transactions. The last write always wins.

Why is Firebase so slow on first render?

On the first page load, Firebase ensures that the local data is equal to the server side state. This means that the client has to be online at application startup which is the reason why Firebase is not completely offline first. To ensure the equalness of client side data, Firebase has to perform several requests to the backend, before the database will respond to queries. This makes the inital page load slow, and it becomes even more slower, the more data exists and has to be validated.

Why is PouchDB & RxDB so slow?

For the PouchDB and RxDB (based on PouchDB storage) I used the old Indexeddb adapter. It is much less optimized then the new adapter, but the new one made problems with returning the correct query results. Theses problems have been fixed on the PouchDB master branch, but I have to wait for the next PouchDB release. I will updated the repo when this change can be done.

Why does AWS Datastore need so less storage space?

AWS Datastore does not save any metadata together with the documents. Instead only the plain documents are stored in IndexedDB. They can do this because they only allow simple queries and have no conflict resolution.

Feature Map

Feature\Project aws firebase pouchdb rxdb watermelondb Offline First No, login required Partially, must be online on first page load Yes Yes Yes Realtime Replication Yes Yes Yes Yes Partially, must be implemented by hand Multi Tab Support Yes Yes No Yes No Observable Queries No Yes No Yes Yes Complex Queries No Yes Yes Yes Partially, limit/skip/sort not working with LokiJS adapter Client Side Encryption No No Yes Yes No Schema Support Yes No No Yes Yes Custom Backend No No No Yes Yes Custom Conflict Handling Yes No Yes Yes No

Starting the projects

All sub-projects use the same port and so can not be started in parallel.

Installation

• You must have installed Node.js
• Clone this project
• In the root folder, run npm install to install the dependencies.
• In the root folder, run npm run build to build all projects.

Firebase Firestore

• Run npm run start:firebase to start the mock server and the production build frontend.

• Or run npm run dev:firebase to start the mock server and the development frontend server.

• Open http://localhost:3000/ to browse the frontend.

AWS Amplify & Datastore

The official AWS mock does not allow a live replication at this point. So you first have to setup an amplify project in the ./projects/aws folder by using this tutorial

• Run npm run start:aws to start the mock server and the production build frontend.

• Or run npm run dev:aws to start the mock server and the development frontend server.

• Open http://localhost:3000/ to browse the frontend.

PouchDB

• Run npm run start:pouchdb to start the mock server and the production build frontend.

• Or run npm run dev:pouchdb to start the mock server and the development frontend server.

• Open http://localhost:3000/ to browse the frontend.

• Run npm run start:rxdb to start the mock server and the production build frontend.

• Or run npm run dev:rxdb to start the mock server and the development frontend server.

• Open http://localhost:3000/ to browse the frontend.

WatermelonDB

• Run npm run start:watermelondb to start the mock server and the production build frontend.

• Or run npm run dev:watermelondb to start the mock server and the development frontend server.

• Open http://localhost:3000/ to browse the frontend.

TODOs

Pull requests are welcomed. Please help implementing more examples:

• Meteor (with the IndexedDB offline first plugin).
• WatermelonDB backend replication.
• AWS Ampflify local backend mock with realtime replication.
• GunDB.