The power of sync engines: choosing the right one for your next project

Web applications are getting more complex, and users expect real-time collaboration, instant feedback, and offline support. Traditional client-server architectures often struggle to meet those demands efficiently, which turns into data inconsistencies, slow loading times, and a poor experience whenever the network is suboptimal. Sync engines are the standard fix for that gap.

What is a sync engine?

A sync engine synchronizes data between multiple devices or services. It sits between the frontend and the backend as a persistent buffer, so client-side applications can operate on a local, often optimistic, copy of the data while the remote, authoritative source stays consistent with it. The engine itself handles network latency, offline scenarios, conflict resolution, and efficient data transfer.

Why are sync engines so powerful?

Sync engines improve performance, responsiveness, and the user experience of modern applications in a few concrete ways:

• Optimistic UI and perceived performance. The client immediately updates its local state when the user acts, so changes are reflected instantly, before the data is fully synchronized with the backend. Users perceive fast performance, and the application feels responsive.
• Offline capabilities. Users can keep interacting with the application and still make changes and access data even without an internet connection. Once connectivity is restored, the sync engine synchronizes the local changes with the remote server.
• Real-time collaboration. For collaborative applications, sync engines push real-time updates to all connected clients, so every user works with the most current version of the data. This is usually implemented with operational transformations (OT) or conflict-free replicated data types (CRDTs).
• Reduced backend load and complexity. Much of the data management and synchronization logic moves to the client side and the sync engine. The backend simplifies into a data store and a conduit for synchronization, instead of tracking real-time state for every connected client.
• Developer experience. Sync engines hide most of the complexity of data synchronization, network handling, and offline support. Developers can focus on application features instead of wrestling with low-level data consistency issues.

What are the drawbacks of sync engines?

They are not a silver bullet, though. Sync engines bring their own set of challenges and trade-offs:

• Frontend complexity. Sync engines simplify the backend but introduce real complexity in the frontend. Developers need to understand how the local store works, how data flows, and how to handle potential conflicts.
• Setup and integration overhead. Integrating a sync engine into an existing application, or setting one up from scratch, takes considerable initial work. This includes configuring the client-side library, setting up the backend components, and defining synchronization rules.
• Conflict resolution. In collaborative or offline-first settings, conflicts arise when multiple users modify the same data concurrently. Designing a conflict resolution strategy that avoids data loss takes careful thought, and getting it wrong is easy.
• Data model constraints. Some sync engines impose specific constraints on the data model or require certain data structures. You may need to adapt an existing data model, or design a new one, around the sync engine\‘s requirements.
• Debugging and observability. A distributed system with local and remote states is harder to debug than a traditional architecture. You need tooling that reveals data flow, synchronization events, and discrepancies.
• Not always necessary. For simple applications with infrequent data updates or no offline requirements, the overhead and complexity of a sync engine can outweigh the benefits. It\‘s worth checking whether the problem it solves really exists for your project.

ElectricSQL architecture and features

ElectricSQL is a Postgres sync engine for local-first and reactive applications. It synchronizes subsets of your PostgreSQL data into local applications, services, and environments.

Key components

ElectricSQL’s architecture breaks down into three main components:

1. PostgreSQL extension. This component handles the server-side integration with PostgreSQL: it tracks changes and manages data synchronization from the database.
2. Sync service (e.g., Elixir-based). This service is the central hub for synchronization. It moves data between the PostgreSQL database and connected clients to cover partial replication, fan-out, and data delivery.
3. Client-side library (e.g., TypeScript). This library is the interface between client applications, the local data store, and the sync service. It manages optimistic updates, local persistence, and synchronization with the backend.

Core features

• Postgres sync engine. ElectricSQL is built specifically for PostgreSQL, so developers can use their existing Postgres databases for local-first applications.
• Partial replication. Only the relevant subsets of data sync to local clients, which keeps performance and resource usage in check.
• Optimistic UI. Immediate UI updates on the client side, so the interface responds without waiting on the server.
• Offline capabilities. Applications built with ElectricSQL work offline, and data synchronizes once connectivity is restored.
• Conflict resolution (CRDTs). ElectricSQL uses Conflict-Free Replicated Data Types (CRDTs) to merge changes and resolve conflicts automatically, which keeps data consistent in collaborative and offline scenarios. It matters for most local-first applications.
• Real-time updates. Data synchronizes in real time, which suits collaborative applications.
• Open source. ElectricSQL is an open-source project, so the code is transparent, accepts community contributions, and can be self-hosted.
• Framework integrations. It plugs into frontend frameworks like React and data libraries like TanStack DB.
• Electric Cloud. A managed cloud service for production deployments.

How it works (high-level)

1. The client application talks to a local data store provided by the ElectricSQL client-side library.
2. Changes made locally are applied optimistically and persisted in the browser’s storage.
3. The sync service runs on the backend and pulls changes from PostgreSQL and pushes them to connected clients, and the other way around.
4. CRDTs handle concurrent modifications and keep data consistent across all replicas.

ElectricSQL is meant to simplify local-first development by hiding the details of data synchronization and conflict resolution, especially for PostgreSQL users.

Convex architecture and features

Convex is an open-source, reactive database where queries are TypeScript code running directly inside the database. It bundles a database, server functions, and client libraries into one backend solution.

Key components

Convex’s architecture is built around three core components:

1. Reactive database. Convex is built around a reactive database that automatically provisions and manages itself. It’s described as a “document-relational” database: it supports JSON-like nested objects plus relational structures with tables and references.
2. Server functions. These are TypeScript functions that run directly inside the database. They are the backend application logic and the database query code that cover read (query) and write (mutation) operations. That approach reduces boilerplate and keeps type safety intact.
3. Client libraries. These let frontend applications interact with the database and server functions. They handle automatic reactivity, subscriptions, WebSocket connections, and state synchronization.

Core features

• TypeScript-native development. Queries and mutations are written in TypeScript, so end-to-end type safety comes with the language.
• Transactional guarantees. Read-only queries and transactional mutations keep the data consistent.
• Automatic reactivity. The UI updates itself when data changes, which is most of the work behind real-time features.
• Built-in backend services. File storage, authentication, and scheduling are built in, so you need fewer separate backend components.
• Optimistic concurrency control. It manages concurrent data modifications.

How it works (high-level)

1. Client applications interact with Convex through its client libraries.
2. Queries and mutations run as TypeScript server functions directly within the Convex database environment.
3. When data changes, the reactive database automatically updates the dependent queries and client subscriptions.
4. Transactional guarantees and optimistic concurrency control keep data consistent and intact across the system.

Convex ties the database, backend logic, and frontend synchronization together into one reactive stack. It suits TypeScript developers who want a managed backend instead of assembling the pieces themselves.

Convex’s database evolution: from MySQL to PostgreSQL

Convex, a backend-as-a-service (BaaS) platform, recently moved its underlying database from Aurora MySQL to PlanetScale PostgreSQL. The move came down to scalability, cost-efficiency, and multi-tenancy support.

Why the switch to PostgreSQL?

Convex’s primary reason for moving from Aurora MySQL to PlanetScale PostgreSQL was the old setup’s multi-tenancy limits and the cost of a free tier:

• Aurora MySQL’s multi-tenancy limits. Aurora MySQL instances are tied to a single MySQL instance, so running multiple independent databases per instance is inefficient. Each new Convex project (or tenant) needed its own backend instance, which drove costs up and made a cheap free tier impossible.
• PlanetScale PostgreSQL’s multi-tenancy. PlanetScale, built on Vitess, added PostgreSQL support, which let Convex run multiple databases per instance. That made multi-tenant applications and a viable free tier workable.
• Performance. The switch also brought real gains in write latency. Convex reported that P50 commit latency (median) became as good as their previous P99 (worst-case) commit latency, and real-time sync and write performance improved accordingly.

How Convex uses PostgreSQL with a document-based design

Convex describes itself as a “document-relational” database. That means it combines ideas from document databases (like MongoDB) and relational databases (like PostgreSQL).

• Document aspect. “Document” means developers can store JSON-like nested objects inside the database. That provides flexibility with the semi-structured data common in modern web applications.
• Relational aspect. “Relational” means Convex supports tables with relations. Developers link documents across tables with IDs, similar to foreign keys in a traditional relational database.

The article doesn’t state explicitly that Convex uses PostgreSQL’s JSONB fields, but it is highly likely: PostgreSQL has strong support for JSON data types, and a document-based design would use it. JSONB (JSON Binary) in PostgreSQL provides efficient storage and querying of JSON data, and JSON is treated as a first-class data type rather than a plain text string. This brings:

• Schema flexibility. Developers can store arbitrarily complex JSON objects and arrays without predefined schemas, as you would in a document database.
• Powerful querying. PostgreSQL provides a rich set of functions and operators for querying and indexing JSONB data, so complex queries on the nested document structures are possible.
• Hybrid approach. Combining JSONB for document storage with traditional relational features (tables, relations) gives Convex a data model that handles both structured and semi-structured data in one system.

Convex’s approach to database layers

Convex does not use PostgreSQL as a direct, exposed database. It keeps its own application-layer abstractions and custom optimizations, especially in the Rust-powered internal data layer. This means:

• Storage swap only. Convex swapped out just the storage backend, from Aurora MySQL to PlanetScale PostgreSQL, and kept the higher-level query engine and relational schema layers intact.
• Custom optimizations. The query layer and data layer logic stay under heavy custom optimization. The implementation goes beyond simple JSONB usage, with internal mechanisms that back the reactive, real-time behavior.

So Convex uses PostgreSQL, including its JSON handling, as the foundation of its reactive, document-relational database. The switch improved multi-tenancy, raised write performance, and cut costs for the free tier, while the developer-centric platform stayed in place.

Zero by Rocicorp architecture and features

Zero by Rocicorp is a newer sync engine, and its defining trait is that queries power it. Instead of syncing entire tables or applying static rules, you write queries directly in client code, and those queries can touch the entire backend database.

Key concepts and architecture

Zero’s approach is query-driven, and the pieces underneath it are:

1. Query-powered sync. Zero syncs data based on queries defined on the client side. Only the data those queries need gets synchronized, which cuts data transfer and overhead.
2. Local caching and instant responses. Zero caches query data on the device and answers future queries from that cache instantly, with a zero-millisecond response time for most interactions. When a query needs data that is not in the cache, Zero falls back to the server.
3. ZQL (Zero Query Language). A custom streaming query engine built by Rocicorp for efficient data synchronization. ZQL uses Incremental View Maintenance (IVM) on both client and server to keep complex queries up to date without rebuilding them.
4. SQLite replica. Zero keeps a SQLite replica of your data on the client, which gives offline capability and fast local access.
5. Horizontally scalable, stateful web service. The backend is built to scale horizontally and stay stateful, so it handles growing numbers of clients and more data.

Core features

• Query-driven synchronization. Developers define what data to sync by writing queries directly in client code. That gives fine-grained control over data replication.
• Instant UI updates. Local caching and optimistic updates give instant responses to user interactions, so applications feel responsive.
• Offline-first capabilities. The local SQLite replica keeps the application usable without a connection, and changes synchronize when connectivity is restored.
• Incremental View Maintenance (IVM). ZQL uses IVM so only the necessary changes get synchronized, which keeps data transfer low and saves bandwidth.
• Postgres support. Zero uses PostgreSQL as its backend database, so it drops into existing Postgres setups. Table schemas on the replica adjust automatically.
• Custom mutators. Writes go through Custom Mutators, which are more flexible than traditional CRUD APIs.
• General-purpose sync. Zero is meant as a general-purpose sync engine, usable across many kinds of web applications.

How it works (high-level)

1. The client application issues queries (ZQL) to retrieve data.
2. Zero checks its local SQLite replica for the requested data first. If the data is there, it returns it instantly.
3. If the data is missing from the local cache or needs an update, Zero talks to the backend service.
4. The backend, a horizontally scalable web service, processes the queries and pushes the relevant data from the PostgreSQL database to the client.
5. IVM sends only the incremental changes, so the views on client and server both stay efficient.
6. Custom Mutators handle data modifications and provide a flexible way to write data back to the server.

Zero by Rocicorp puts queries at the center of its synchronization model. The result is instant responsiveness and efficient data management, and the design stays flexible.

Comparative analysis of ElectricSQL, Convex, and Zero

This section compares ElectricSQL, Convex, and Zero on architecture, features, and use cases. All three aim to solve data synchronization, but with distinct philosophies and implementations.

1. ElectricSQL

Core philosophy: ElectricSQL is a Postgres sync engine focused on local-first applications running directly on top of an existing PostgreSQL database.

Architecture:

• PostgreSQL-centric. Integrates deeply with PostgreSQL through an extension.
• Sync service. A backend service (e.g., Elixir-based) manages synchronization between Postgres and clients.
• Client-side library. Provides a local data store and handles optimistic updates and local persistence.

Features:

• Partial replication. Syncs only relevant subsets of data to clients.
• CRDTs. Conflict-Free Replicated Data Types resolve conflicts automatically, which suits collaborative and offline scenarios.
• Optimistic UI. Immediate UI feedback.
• Open source. Self-hosting and community contributions are both possible.
• Managed cloud option. Electric Cloud handles production deployment.

Ideal use cases:

• Applications already using or planning to use PostgreSQL as their primary database.
• Local-first applications that need solid offline capabilities and automatic conflict resolution.
• Collaborative applications where multiple users might concurrently modify data.
• Developers who want more hands-on control of their database infrastructure.

2. Convex

Core philosophy: Convex is a reactive database with server functions and client libraries bundled into one cohesive platform.

Architecture:

• Reactive database. A managed, “document-relational” database that automatically provisions itself.
• Server functions. Backend logic and database queries are written in TypeScript and run directly inside the database environment.
• Client libraries. They handle automatic reactivity, subscriptions, and state synchronization on the frontend.

Features:

• TypeScript-native development. Queries and mutations are written in TypeScript, so end-to-end type safety comes with the language.
• Transactional guarantees. Read-only queries and transactional mutations keep the data consistent.
• Automatic reactivity. The UI updates itself when data changes, which is most of the work behind real-time features.
• Built-in backend services. File storage, authentication, and scheduling are built in, so you need fewer separate backend components.
• Optimistic concurrency control. It manages concurrent data modifications.

Ideal use cases:

• Developers who want one integrated backend that combines database, serverless functions, and sync.
• Applications that need rapid development and deployment, with real-time features and reactive UIs.
• Teams that use TypeScript on the frontend and the backend.
• Projects that prefer a managed approach for the backend.

3. Zero by Rocicorp

Core philosophy: Zero is a query-powered sync engine. Client-side queries define what data gets synced, which buys instant responsiveness and efficient transfer.

Architecture:

• Query-driven sync. Data synchronization is based on queries (ZQL) defined in client code.
• Local SQLite replica. A SQLite database on the client supports offline use and instant local access.
• ZQL (Zero Query Language). A custom streaming query engine that uses Incremental View Maintenance (IVM) for efficient data updates.
• Horizontally scalable, stateful web service. The backend handles queries and synchronization with the primary database, and scales horizontally.

Features:

• Instant UI updates. Local caching and optimistic updates give near-zero latency for user interactions.
• Fine-grained synchronization. Only the data subsets that active queries need get synced, which saves bandwidth.
• Offline-first. The local SQLite replica provides offline support.
• Incremental View Maintenance. Data updates stay efficient because only incremental changes are sent.
• Postgres support. Integrates with existing PostgreSQL databases.
• Custom mutators. A flexible way to handle data writes and business logic.

Ideal use cases:

• Applications that need very low latency and a smooth user experience even with intermittent connectivity.
• Projects that want precise control over data synchronization at the query level.
• Developers who already run PostgreSQL and want sync capabilities on top of it.
• Complex collaborative applications where efficient real-time updates are critical.

Comparison summary

Choosing the right sync engine for your project

The right sync engine depends on your project`s requirements, your existing stack, and your team`s expertise.

• If you are heavily invested in PostgreSQL and want to keep control of your database infrastructure, ElectricSQL is a solid choice. Deep Postgres integration and CRDT-based conflict resolution fit local-first and collaborative applications built around a relational database.

• If you want a managed backend that covers full-stack development in TypeScript, Convex is worth a close look. The integrated reactive database, server functions, and client libraries fit applications that need automatic reactivity and real-time updates without separate backend components.

• If your priority is very low latency and fine-grained control over synchronization at the query level, Zero by Rocicorp is a serious option. The query-driven approach and incremental updates suit highly interactive applications where instant UI feedback and efficient data transfer matter, especially if you`re comfortable with ZQL.

A few questions worth asking before you decide:

1. What is your existing database technology? (e.g., PostgreSQL, NoSQL, or starting fresh)
2. What level of control do you want over your backend infrastructure? (fully managed vs. self-hosted components)
3. How critical are offline capabilities and real-time collaboration for your application?
4. What is your team`s familiarity with TypeScript, SQL, or custom query languages?
5. What are your performance and responsiveness requirements? (e.g., near-zero latency for UI updates)
6. What is your budget, and how much setup and integration overhead are you willing to take on?

Weigh these factors against what ElectricSQL, Convex, and Zero actually do, and you will end up with a choice that fits your project`s goals and the kind of application you are building.

Why Convex opts for PostgreSQL with document design over pure document databases like MongoDB

Convex offers a document-relational database, but it built the platform on PostgreSQL (specifically, PlanetScale PostgreSQL) rather than a pure document database like MongoDB. The decision rests on architectural and functional differences between the two, plus Convex’s goal of a reactive, transactional backend-as-a-service that stays friendly to developers.

Key differences and advantages of Convex’s approach

Convex’s comparison with MongoDB lists several areas where its approach, built on PostgreSQL, has an advantage:

1. Transactional guarantees:

   • Convex. Full ACID (Atomicity, Consistency, Isolation, Durability) compliance and transactional guarantees for all operations, even across multiple documents. Data integrity and consistency hold without performance compromises.
   • MongoDB. Transactional support exists now, but by default MongoDB is atomic only at the document level. Multi-document transactions require manually configured two-phase commits, which can impact performance and add complexity. Convex argues that MongoDB’s transaction API removes many of the benefits of a NoSQL database.

2. Real-time capabilities and consistency:

   • Convex. Real-time capabilities come by default, with automatic subscriptions for functions. The WebSocket client gives strong consistency guarantees, so reads and writes stay synchronized and consistent.
   • MongoDB. Real-time subscription support is rolling out (e.g., with the Realm Web SDK), but the feature is newer and may not offer the same integrated consistency guarantees as Convex’s native approach.

3. Backend-as-a-service (BaaS) vs. document store:

   • Convex. A BaaS that bundles a database, serverless functions, file storage, cron jobs, and more, so the entire backend can be written directly in TypeScript.
   • MongoDB. Primarily a document store. It handles data persistence well, but building a complete backend means integrating other products and services, which adds development and operational overhead.

4. Type safety and developer experience:

   • Convex. End-to-end type safety from code to client, direct type inference, and autocompletion out of the box. Schemas are defined in pure TypeScript at the database level and at the application level.
   • MongoDB. Largely an untyped document store. ORMs can define schemas at the application level, but they often fall short of the end-to-end type safety in Convex’s integrated TypeScript approach.

5. Query language and abstraction:

   • Convex. No separate query language like SQL or MQL. Developers use a native DB interface inside TypeScript query functions, with filtering and operations done directly in code.
   • MongoDB. Uses its own query language (MQL) for data manipulation.

6. Multi-tenancy and cost-effectiveness:

   • As covered above, Convex’s move to PlanetScale PostgreSQL came mostly from the need for better multi-tenancy support and a more cost-effective free tier. Traditional MySQL, and some pure document databases by extension, is inefficient for multi-tenant architectures where each tenant needs a separate database instance or complex sharding strategies.
   • PostgreSQL, especially with the advances in PlanetScale, offers more flexible and efficient ways to run multiple databases within a single instance. For a BaaS that offers a free tier, that matters a lot.

The role of PostgreSQL’s JSONB

Convex does not publish its internal implementation details, but the document-relational model points strongly at PostgreSQL’s JSONB data type. JSONB lets PostgreSQL store and query JSON data efficiently, a hybrid that pairs the schema flexibility of document databases with PostgreSQL’s transactional and relational strengths. That lets Convex:

• Use relational strengths. PostgreSQL’s transactional capabilities, indexing, and relational features for structured data are often stronger than what pure document databases offer.
• Handle document flexibility. JSONB stores flexible, nested document structures, which fits web applications that deal with evolving schemas.
• Skip MongoDB’s transaction overhead. Building on PostgreSQL gives strong transactional guarantees without the performance cost or the complexity of MongoDB’s multi-document transactions.

Conclusion

So the MongoDB comparison comes down to trade-offs. Building on PostgreSQL gives Convex a mature relational core: strict data consistency across documents, indexing, and complex relationships, all without the overhead or complexity of MongoDB’s multi-document transactions. The document-oriented side keeps schema flexibility and a developer experience that resembles a document store. For applications that need strict consistency and evolving schemas at the same time, that combination is precisely why Convex chose this design.

Sources

ElectricSQL - PostgreSQL sync engine for local-first applications
Convex - Reactive database and backend-as-a-service platform
Zero by Rocicorp - Query-powered sync engine with instant UI updates