mobx vs. recoil
Side-by-side comparison · 9 metrics · 14 criteria
- Weekly Downloads
- 3.2M
- Stars
- 28.2K
- Gzip Size
- 15.4 kB
- License
- MIT
- Last Updated
- 10mo ago
- Open Issues
- 66
- Forks
- 1.8K
- Unpacked Size
- 4.7 MB
- Dependencies
- 1
- Weekly Downloads
- 407.9K
- Stars
- 19.4K
- Gzip Size
- 29.5 kB
- License
- MIT
- Last Updated
- 2y ago
- Open Issues
- 320
- Forks
- 1.2K
- Unpacked Size
- 2.2 MB
- Dependencies
- 3
mobx vs recoil downloads — last 12 months
Criteria — mobx vs recoil
- Learning Curve
- mobx ✓Generally accessible, especially for those comfortable with OOP or mutable state.recoilIntuitive for developers deeply familiar with React hooks and functional paradigms.
- State Mutation
- mobx ✓Supports direct mutation of observable state, simplifying common update patterns.recoilEmphasizes immutable updates through explicit atom and selector definitions.
- Core Philosophy
- mobx ✓Minimize boilerplate through transparently applied changes and observable state.recoilProvide a granular, concurrent-first state management solution tailored for React.
- Codebase Control
- mobx ✓Allows direct mutation of observable state, simplifying immediate updates.recoilEncourages explicit state updates, providing clearer control over state transitions.
- Primary Audience
- mobxDevelopers seeking simplicity, scalability, and automatic reactivity across various JavaScript environments.recoil ✓React developers prioritizing deep integration with hooks and concurrent features.
- Reactivity Model
- mobx ✓Observable-based, automatically tracks dependencies and triggers reactions.recoilAtomic state (atoms) and computed state (selectors) forming an explicit dependency graph.
- Bundle Size (gzip)
- mobx ✓Minimal at 15.4 kB, contributing to faster application load times.recoilSubstantial at 29.5 kB, impacting initial load performance slightly more.
- Ecosystem Maturity
- mobx ✓Extensive mature ecosystem with broad community support and tooling.recoilGrowing ecosystem, benefiting from direct backing and alignment with React's future.
- Rendering Strategy
- mobxRelies on observable tracking to trigger minimal re-renders of subscribed components.recoilUses a graph-based system to determine precise re-render paths for components subscribed to state.
- Dependency Management
- mobxImplicit dependency tracking through observable-observer pattern.recoil ✓Explicit dependency graph management via selectors referencing atoms.
- Bundle Size Efficiency
- mobx ✓Highly optimized, resulting in a significantly smaller gzipped size.recoilLarger footprint due to React-specific optimizations and graph management overhead.
- TypeScript Integration
- mobxRobust and mature TypeScript support with extensive type inference.recoilStrong TypeScript support, particularly with explicit typing of atoms and selectors.
- Asynchronous Operations
- mobxHandles async logic via standard JS patterns, reactions managed by bindings.recoil ✓Offers integrated support for asynchronous selectors within the state graph.
- Environment Flexibility
- mobx ✓Works seamlessly outside of React, applicable in vanilla JS or other frameworks.recoilPrimarily designed and optimized for React applications and React Native.
| Criteria | mobx | recoil |
|---|---|---|
| Learning Curve | ✓ Generally accessible, especially for those comfortable with OOP or mutable state. | Intuitive for developers deeply familiar with React hooks and functional paradigms. |
| State Mutation | ✓ Supports direct mutation of observable state, simplifying common update patterns. | Emphasizes immutable updates through explicit atom and selector definitions. |
| Core Philosophy | ✓ Minimize boilerplate through transparently applied changes and observable state. | Provide a granular, concurrent-first state management solution tailored for React. |
| Codebase Control | ✓ Allows direct mutation of observable state, simplifying immediate updates. | Encourages explicit state updates, providing clearer control over state transitions. |
| Primary Audience | Developers seeking simplicity, scalability, and automatic reactivity across various JavaScript environments. | ✓ React developers prioritizing deep integration with hooks and concurrent features. |
| Reactivity Model | ✓ Observable-based, automatically tracks dependencies and triggers reactions. | Atomic state (atoms) and computed state (selectors) forming an explicit dependency graph. |
| Bundle Size (gzip) | ✓ Minimal at 15.4 kB, contributing to faster application load times. | Substantial at 29.5 kB, impacting initial load performance slightly more. |
| Ecosystem Maturity | ✓ Extensive mature ecosystem with broad community support and tooling. | Growing ecosystem, benefiting from direct backing and alignment with React's future. |
| Rendering Strategy | Relies on observable tracking to trigger minimal re-renders of subscribed components. | Uses a graph-based system to determine precise re-render paths for components subscribed to state. |
| Dependency Management | Implicit dependency tracking through observable-observer pattern. | ✓ Explicit dependency graph management via selectors referencing atoms. |
| Bundle Size Efficiency | ✓ Highly optimized, resulting in a significantly smaller gzipped size. | Larger footprint due to React-specific optimizations and graph management overhead. |
| TypeScript Integration | Robust and mature TypeScript support with extensive type inference. | Strong TypeScript support, particularly with explicit typing of atoms and selectors. |
| Asynchronous Operations | Handles async logic via standard JS patterns, reactions managed by bindings. | ✓ Offers integrated support for asynchronous selectors within the state graph. |
| Environment Flexibility | ✓ Works seamlessly outside of React, applicable in vanilla JS or other frameworks. | Primarily designed and optimized for React applications and React Native. |
MobX is built around the principle of observable state and transparently applying changes to the UI, making it exceptionally powerful for complex applications where state can evolve rapidly. Its core philosophy is minimizing boilerplate by deriving state and automating updates, which appeals to developers who prefer a more declarative approach to reactivity and want to avoid manual state synchronization.
Recoil, on the other hand, focuses on providing a more granular and concurrent-first state management solution specifically for React applications. It uses atomic state units called "atoms" and "selectors" to manage application state, offering a model that feels more tightly integrated with React's component lifecycle and hooks API. This makes it particularly suitable for teams deeply invested in the React ecosystem.
A key architectural difference lies in their core reactivity models. MobX utilizes a system of observables, observers, and reactions. You mark your state as observable, and MobX automatically tracks dependencies and updates components or other reactions when that observable state changes. Recoil's architecture is centered around atoms (writeable state) and selectors (computed state), which are explicitly defined and subscribed to, providing a more explicit graph-based dependency management.
Another technical distinction is how they manage asynchronous operations and side effects. MobX often integrates with asynchronous patterns through standard JavaScript promises and async/await, with libraries like `mobx-react-lite` or `mobx-react` handling the reaction to these changes in React. Recoil offers built-in support for asynchronous selectors, allowing for more integrated handling of data fetching and asynchronous logic directly within its state graph, which can simplify the mental model for data loading states.
In terms of developer experience, MobX generally offers a gentler learning curve for those familiar with object-oriented programming or mutable data structures, as it allows for direct mutation of observable state. Its TypeScript support is robust. Recoil, designed with React hooks in mind, might feel more intuitive for developers already proficient with functional programming paradigms and the React hooks API; its type inference for atoms and selectors is also strong, though the explicit graph definition can require a different way of thinking.
Regarding performance and bundle size, MobX has a clear advantage. With a gzipped bundle size of 15.4 kB, it is significantly smaller than Recoil's 29.5 kB. MobX's efficient reactivity system often leads to fewer unnecessary re-renders, particularly in applications with highly dynamic state. Recoil's larger bundle size is partly due to its React-specific optimizations and the overhead of managing its dependency graph.
For practical recommendations, consider MobX for large-scale enterprise applications with complex, interconnected state where rapid development and minimal boilerplate are paramount. If your team is comfortable with its observer pattern and wants a highly performant, flexible solution that isn't strictly tied to React, MobX is an excellent choice. Recoil is ideal for React-native development or single-page applications where a tightly integrated, concurrent-ready state solution is desired, especially for teams already leveraging React hooks extensively. It simplifies asynchronous state management within the React paradigm.
While both offer excellent TypeScript support, MobX's long history and broad adoption mean a vast ecosystem of community tools and plugins. Recoil is newer but benefits from being developed by Facebook (Meta), ensuring strong alignment with React's future direction. MobX's migration path from vanilla JavaScript or older state management solutions can be more gradual, whereas adopting Recoil implies a deeper commitment to the React-specific, hook-centric approach.
Edge cases might involve highly specialized scenarios. For instance, MobX's fine-grained control over reactivity and its ability to work outside of React makes it suitable for backing non-UI state or integrating into vanilla JavaScript projects. Recoil's strength lies in scenarios demanding fine-grained control over updates within a concurrent React environment, where its graph model can precisely manage which parts of the app re-render in response to specific state changes, leading to optimizations in scenarios with frequent, isolated state updates.
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