So you want to write a sync system for a web app with offline and realtime support? Good luck. You might find the following resources useful.
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Database in a browser, a spec (Stepan Parunashvili)
What problem are we trying to solve with a sync system?
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The web of tomorrow (Nikita Prokopov)
Lots of ideas. Published in 2015, maybe it was ahead of its time?
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How Figma’s multiplayer technology works (Evan Wallace)
Fantastic summary of Figma's sync system and why they ultimately rejected fully decentralized CRDTs while still using some ideas from the CRDT literature.
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Linear's realtime sync system (Tuomas Artman)
Linear has a sophisticated sync system with offline capabilities. This 2020 talk describes how it works.
More recently, Tuomas described things the team learned in 3 years of building a sync engine in a Twitter thread.
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[Scaling Linear's sync system](Scaling the Linear Sync Engine)
Follow-up to the previous video
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Muse's Sync (Adan Wulf, Addam Wiggins, Mark Mcgranaghan)
In-depth discussion of the why and how behind Muse's realtime sync system
Every app has slightly different needs. So my guess is that you will need to build your own system. But it's worth taking a look at off-the-shelf sync engines, either as buy-over-build or to steal ideas:
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Firebase
Confusingly Google offers two realtime databases under the Firebase brand. Cloud Firestore and Firebase Realtime Database. Lots of good ideas, but they're hard to disentangle from the marketing copy.
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Couchdb / Pouchdb
Couchdb is a database based around replication. The CouchDB book, while a bit dated, is well written and worth reading.
Pouchdb offers an in-browser database with server replication, and did so before it was cool. It's still pretty impressive today, and free software.
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Fluid Framework is a "collection of client libraries for distributing and synchronizing shared state. These libraries allow multiple clients to simultaneously create and operate on shared data structures using coding patterns similar to those used to work with local data." Fluid is developed by Microsoft.
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WatermelonDB - an offline-first local db (React and ReactNative) with capabilities based on IndexedDB/SQLite
WatermelonDB offers a client-side sync implementation and API; you still need to write your own backend.
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AppSync is AWS attempt at offering automated realtime GraphQL updates. It's one of the weirder AWS offerings. As is often the case with AWS marketing-driven producs, it's hard to figure out from the docs if it's a viable alternative for a given problem.
When you're building a sync engine, you're essentially building a database with replication - whether you realize it or not. So it's a good idea to review some of the literature on databases and replication.
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Datomic
Learn as much as you can about Datomic - Datalog vs SQL, inserts/updates as pure data structures, pull syntax, EAV tuples, immutable facts, database as a value, unbundling the database. There's so much to learn. Datomic may not be the right database for your backend (although maybe it is? Check it out) but it's without a doubt one of the best-designed systems out there.
The docs are excellent.
Datascript is a client-side version of Datomic (but there's no built-in sync engine).
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Postgres is the most mature RDBMS out there (but doesn't by itself help you replicate data to the client). There's a lot to learn from from decades of research and production experience. The chapter in the Postgres manual on Isolation levels is excellent.
Some SaaS are trying to offer multiplayer, syncing, caching etc as a service. No clear winner yet. But even if you're not going to use these, it's worth reading the API docs for inspiration.
- replicache "is a JavaScript framework for building high-performance, offline-capable, collaborative web apps"
- Liveblocks "is a set of APIs and tools that helps you create performant and reliable multiplayer experiences in minutes"
- Supabase
The simplest way to replicate data is for client to poll for updates every second or so. But that introduces unnecessary server load and >1 s latencies, so eventually you will want to switch to a push model. On the web that usually means using Websockets or Server-Sent Events to allow the server to push updates to the clients as they arrive.
Notice that realtime updates are conceptually distinct from data replication - you can simplify the system by building a polling based solution first and adding push updates as a separate step. The realtime update can be in the form of a lightweight shoulder tap, a message that wakes up the client, causing it to pull the latest updates from the server. Realtime notificaitons can be seen as optional - when the websocket connection is not available, you can fall back to polling.
Instead of rolling your own websocket server, consider using a hosted service like pusher.com or ably.com. They do all the heavy lifting for you (like keeping thousands of simultaneous TCP connections open) and provide a request/response style interface for your server to send messages to connected clients.
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Differential Synchronization, Neil Fraser, 2009 (link)
There's also a youtube talk by the author on the same topic
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Kleppmann, Designing Data-Intensive Applications, O'Reilly (2017)
If you don't have this book yet, drop everything and read it now. Every chapter is full of insights and summaries of how to use and build databases, much of it applicable to sync engines.
Local-first software is a rebranding of offline-first systems. The name emphasizes the philosophical stance that data lives on your device first and foremost, with implications for privacy, ownership and decentralization.
- Martin Kleppmann, Adam Wiggins, Peter van Hardenberg, and Mark McGranaghan: “Local-first software: You own your data, in spite of the cloud”. ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward! ’19), October 2019 (link)
I recommend learning about CRDTs. Not because a sync system should be built on CRDTs (it probably shouldn't unless you're building a truly decentralized, peer-to-peer system), but because everyone's talking about them and it's useful to understand their limitations (and strengths). Also the literature contains many concepts that are helpful even if you don't need costly decentralization because can rely on a centralized server.
While CRDTs offer advantages like operation without a centralized server, they often suffer from problems with regard to performance, memory usage and implementation complexity.
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Martin Kleppmann, Alastair R. Beresford: "A Conflict-Free Replicated JSON Datatype". IEEE Transactions on Parallel and Distributed Systems 28(10):2733–2746, April 2017 (link)
Very accessible paper on what ended up being published as automerge
Many more papers and other resources are avaialble on crdt.tech.
CS students study this topic in college. If, like me, you skipped this part of your eductation, it's worth learning the basic theory to get a better overview of the problem space.
I recommend Linsey Kuper's lectures at UC Santa Cruz, which she's generously made available on Youtube. The course also has a website.
It's fun and you'll learn about Lamport diagrams and consistency models.
There's also aphyr's braindump of interesting ideas in distributed systems. He's also has a nice page describing consistency models.
If you're storing data in a browser for offline use, it's probably going to end up in IndexedDB. Here's a lot more information about this janky corner of the web platform.