I apologize for the misunderstanding earlier. Let’s focus on diagnosing why your custom state management solution is running slower than NGXS, despite appearing to have a simpler implementation. We’ll explore potential bottlenecks in your code, compare them with NGXS’s optimizations, and provide actionable recommendations to enhance performance.
- Potential Performance Bottlenecks in Your Custom Store
a. Usage of Immer’s produce Function
• Issue: While Immer simplifies immutable state updates, it introduces overhead by creating proxies and performing structural sharing. This can become a performance bottleneck, especially with frequent or large state updates.
• Impact: Each call to set involves produce, which can slow down state mutations, particularly in applications with complex or large state trees.
b. Local Storage Operations on Every State Update
• Issue: The set method calls saveToLocalStorage after every state mutation. LocalStorage operations are synchronous and can block the main thread, leading to noticeable performance degradation, especially if the state is sizable or updates are frequent.
• Impact: Frequent reads and writes to localStorage can significantly slow down your application, making UI interactions feel laggy.
c. Updating All Signals on Every State Change
• Issue: In your set method, after producing the nextState, you iterate through all keys to update signals only if their values have changed:
for (const key in nextState) { if (Object.prototype.hasOwnProperty.call(nextState, key) && nextState[key] !== currentState[key]) { (this._state[key] as any).set(nextState[key]); } }
• Comparison Overhead: The !== comparison can be expensive for complex objects or large state slices.
• Signal Updates: Even with conditional updates, triggering multiple Signal.set calls can introduce performance overhead.
• Impact: This approach can lead to unnecessary computations and signal updates, especially when multiple parts of the state are interdependent or frequently changing.
d. Double Invocation in getALot Method
• Issue: The getALot method uses a double invocation:
public getALot() { return this.get(state => { return state.arr })()() }
• The first () invokes the Signal returned by get.
• The second () invokes the result of the first invocation.
This can introduce unnecessary function calls and potential re-computations.
• Impact: While not a major bottleneck, redundant function calls can accumulate, especially if getALot is called frequently.
- How NGXS Optimizes Performance
NGXS is a mature state management library with several built-in optimizations:
• Selective State Updates: NGXS uses decorators and selectors to ensure only relevant parts of the state trigger change detection. This minimizes unnecessary computations.
• Efficient Change Detection: Leveraging RxJS Observables and Angular’s change detection strategy, NGXS efficiently manages state changes without blocking the main thread.
• Asynchronous Actions: NGXS handles asynchronous operations gracefully, ensuring that state mutations don’t interfere with UI responsiveness.
• Middleware and Plugins: Optimizations like memoization and lazy loading of state slices help in reducing performance overhead.
• Optimized Serialization: NGXS provides mechanisms to optimize state serialization and deserialization, avoiding frequent and heavy operations on every state change.
- Recommendations to Improve Your Custom Store’s Performance
To align your custom store’s performance with or surpass NGXS’s, consider the following optimizations:
a. Minimize or Optimize Immer Usage
• Selective Immer Usage: Instead of using produce for every state update, consider mutating only the necessary parts of the state manually, especially for simple or infrequent updates.
• Batch Updates: Group multiple state changes into a single produce call to reduce the overhead of multiple proxy creations.
• Alternative Libraries: Explore lighter alternatives to Immer or implement custom immutable update utilities tailored to your application’s needs.
b. Optimize Local Storage Interactions
• Debounce State Persistence:
• Implementation: Instead of saving to localStorage on every set, debounce the saveToLocalStorage calls. For example, wait for a short period (e.g., 300ms) after the last state change before persisting.
• Benefit: Reduces the number of synchronous localStorage writes, minimizing main thread blocking.
private saveToLocalStorageDebounced = debounce(() => { this.saveToLocalStorage(); }, 300);
protected set(updater: (draft: T) => void): void { const currentState = this.getCurrentState(); const nextState = produce(currentState, updater);
for (const key in nextState) {
if (Object.prototype.hasOwnProperty.call(nextState, key) && nextState[key] !== currentState[key]) {
(this._state[key] as any).set(nextState[key]);
}
}
this.saveToLocalStorageDebounced();
}
(Ensure you import a debounce utility, such as from lodash.)
• Asynchronous Storage:
• Implementation: Move localStorage operations to a Web Worker or use IndexedDB with asynchronous APIs to prevent blocking the main thread.
• Benefit: Keeps the UI responsive during state persistence.
c. Refine Signal Update Strategy
• Selective Signal Updates:
• Implementation: Instead of iterating through all state keys, track which specific parts of the state have changed and update only those signals.
• Benefit: Reduces the number of unnecessary Signal.set calls.
• Immutable vs. Mutable Signals:
• Alternative Approach: Consider structuring your signals to represent more granular slices of state, reducing the need to iterate and compare entire state objects.
d. Simplify or Correct getALot Method
• Remove Redundant Invocations:
• Implementation: Adjust the getALot method to avoid double function calls. For example:
public getALot() { return this.get(state => state.arr)(); }
• Benefit: Reduces unnecessary function calls, enhancing performance.
e. Profile and Identify Specific Bottlenecks
• Use Performance Profiling Tools:
• Implementation: Utilize browser developer tools to profile your application. Identify which parts of the set and get methods consume the most time.
• Benefit: Provides concrete data on where optimizations will have the most impact.
• Measure Impact of Optimizations:
• Implementation: After each optimization, benchmark the performance to ensure that changes are effective.
• Benefit: Ensures that optimizations lead to tangible performance improvements.
f. Consider Memoization for Selectors
• Implementation: Implement memoization for your selector functions to prevent unnecessary recalculations when state slices haven’t changed.
import { memo } from 'lodash';
protected get(selector: (state: StateSignals) => R): Signal { const memoizedSelector = memo(selector); return computed(() => memoizedSelector(this._state)); }
• Benefit: Reduces computation overhead for selectors, especially if they perform complex operations.
g. Optimize State Structure
• Flatten State Trees:
• Implementation: Structure your state to be as flat as possible, minimizing nested objects. This can make state comparisons and updates more efficient.
• Benefit: Enhances performance of both state mutations and signal updates.
• Normalize Data:
• Implementation: Similar to normalization in databases, store data in a normalized form to reduce redundancy and simplify updates.
• Benefit: Streamlines state management and reduces the complexity of updates.
- Example Optimized BaseStore Implementation
Here’s an optimized version of your BaseStore incorporating some of the recommendations:
import { computed, Signal, signal } from '@angular/core'; import { produce } from 'immer'; import { debounce } from 'lodash'; // Ensure lodash is installed
type StateSignals = { [K in keyof T]: Signal<T[K]> };
const STORAGE_PREFIX = 'nf_store';
export abstract class BaseStore {
private _state: StateSignals<T> = {} as StateSignals<T>;
public get state(): StateSignals<T> {
return this._state;
}
private _storageKey: string | undefined;
private get storageKeyPrefix(): string | undefined {
return this._storageKey ? `${STORAGE_PREFIX}_${this._storageKey}` : undefined;
}
// Debounced save to localStorage to prevent frequent writes
private saveToLocalStorageDebounced = debounce(() => {
this.saveToLocalStorage();
}, 300);
constructor(initialState: T, storageKey?: string) {
this._storageKey = storageKey;
// Load state from local storage if storage key is provided
if (this.storageKeyPrefix) {
const persistedState = this.loadFromLocalStorage();
if (persistedState) {
initialState = { ...initialState, ...persistedState };
}
}
// Create a signal for each property in the initial state
for (const key in initialState) {
if (Object.prototype.hasOwnProperty.call(initialState, key)) {
this._state[key] = signal(initialState[key]);
}
}
}
// Method to get the current state as an object
private getCurrentState(): T {
const currentState: Partial<T> = {};
for (const key in this._state) {
if (Object.prototype.hasOwnProperty.call(this._state, key)) {
currentState[key] = this._state[key]();
}
}
return currentState as T;
}
// Method to create a selector with memoization
protected get<R>(selector: (state: StateSignals<T>) => R): Signal<R> {
const memoizedSelector = memo(selector);
return computed(() => memoizedSelector(this._state));
}
// Method to update the state
protected set(updater: (draft: T) => void): void {
const currentState = this.getCurrentState();
const nextState = produce(currentState, updater);
// Track changed keys to update signals selectively
const changedKeys: (keyof T)[] = [];
for (const key in nextState) {
if (Object.prototype.hasOwnProperty.call(nextState, key)) {
if (nextState[key] !== currentState[key]) {
changedKeys.push(key as keyof T);
}
}
}
// Update only changed signals
changedKeys.forEach(key => {
this._state[key].set(nextState[key]);
});
// Debounced save to prevent frequent writes
this.saveToLocalStorageDebounced();
}
// Method to load cached state from local storage
private loadFromLocalStorage(): Partial<T> | null {
const cachedData = this.storageKeyPrefix ? localStorage.getItem(this.storageKeyPrefix) : null;
return cachedData ? JSON.parse(cachedData) : null;
}
// Method to save state to local storage
private saveToLocalStorage(): void {
if (this.storageKeyPrefix) {
const currentState = this.getCurrentState();
try {
localStorage.setItem(this.storageKeyPrefix, JSON.stringify(currentState));
} catch (e) {
console.error('Failed to save state to localStorage:', e);
}
}
}
// Method to clear cached state from local storage
public clearCache(): void {
if (this.storageKeyPrefix) {
localStorage.removeItem(this.storageKeyPrefix);
}
}
}
Key Optimizations Implemented:
1. Debounced Local Storage Saves: Reduces the frequency of localStorage.setItem calls, preventing main thread blocking during rapid state updates.
2. Selective Signal Updates: Only updates signals that have actually changed, minimizing unnecessary Signal.set invocations.
3. Memoized Selectors: Prevents redundant computations in selectors by caching results based on input dependencies.
4. Error Handling: Adds try-catch around localStorage operations to gracefully handle potential errors.
5. Optimized getALot Method: Ensure that selector invocations are streamlined without redundant calls.
- Additional Considerations
a. Avoid Heavy Computations in Selectors
• Implementation: Ensure that your selector functions are lightweight and avoid performing heavy computations. If necessary, offload complex calculations to Web Workers or perform them asynchronously.
b. Leverage Angular’s Change Detection Strategy
• Implementation: Utilize OnPush change detection strategy in your components to minimize unnecessary re-renders when state changes occur.
@Component({ // ... changeDetection: ChangeDetectionStrategy.OnPush }) export class YourComponent { /* ... */ }
• Benefit: Reduces the performance impact of state changes by limiting change detection cycles to relevant components only.
c. Monitor and Profile Regularly
• Implementation: Use Angular’s built-in profiling tools and browser developer tools to continuously monitor your application’s performance. Identify and address new bottlenecks as your application evolves.
d. Consider Alternative State Management Libraries
• Evaluation: While optimizing your custom store is feasible, consider evaluating other state management solutions like Akita, NgRx, or even NGXS itself. These libraries come with a suite of performance optimizations and community support that can save development time and ensure scalability.
- Summary
Your custom state management solution is a commendable approach leveraging modern Angular features like Signals and Immer. However, certain implementation details—such as frequent localStorage operations, the overhead of Immer, and the strategy for updating signals—can introduce performance bottlenecks, especially when compared to optimized libraries like NGXS.
By implementing the recommended optimizations—debouncing state persistence, refining signal updates, minimizing Immer’s overhead, and profiling your application—you can significantly enhance your store’s performance. Additionally, continuously monitoring and iterating on your implementation will ensure that it scales efficiently as your application grows.
If, after these optimizations, you still encounter performance issues, it might be worthwhile to reconsider using a well-optimized state management library that aligns with your application’s requirements and provides the necessary performance guarantees.