1. How Improve query and database in case tables having millions of records/Design DB

2. How Improve performance of Web -API

3. How Improve performance of UI /Angular base application

4. How Improve or build architecture incase millions of user concurrent hitting to applications /

or High-Traffic Architecture for Millions of Concurrent Users

1. How Improve query and database in case tables having millions of records/Design DB

Indexing Strategies

Create proper indexes on columns frequently used in WHERE, JOIN, and ORDER BY clauses
Use composite indexes for queries that filter on multiple columns
Avoid over-indexing as it slows down write operations
Consider partial indexes for columns with high cardinality

Query Optimization

Limit returned data using SELECT with specific columns instead of SELECT *
Implement pagination with LIMIT and OFFSET (or keyset pagination for better performance)
Use query caching for frequently executed queries
Avoid N+1 queries by using JOINs or batch loading(
)

Database Structure

Partition large tables by date ranges or other logical divisions
Consider sharding for extreme scale scenarios
Normalize/denormalize appropriately based on read vs. write patterns
Use materialized views for complex, frequently accessed aggregations

Infrastructure Improvements

Upgrade hardware (faster disks, more RAM)
Configure database cache settings appropriately
Implement read replicas to distribute read load
Use connection pooling to reduce connection overhead

Application-Level Techniques

Implement caching (Redis, Memcached) for frequently accessed data
Use lazy loading for non-essential data
Consider asynchronous processing for complex data fetching
Implement data archiving to move older, less-frequently accessed data

Monitoring and Maintenance

Regularly analyze query performance with EXPLAIN plans
Update statistics to help the query optimizer
Schedule maintenance for index rebuilding/defragmentation

N+1 query problem :

What is the N+1 Problem?

The N+1 problem occurs when your application makes:

1 query to fetch the parent records (the "1")
Then N additional queries (one for each parent record) to fetch related child records

Example:

-- Initial query (1)
SELECT * FROM users WHERE active = true;

-- Then for each user (N)
SELECT * FROM orders WHERE user_id = ?;

Why N+1 is Problematic

Performance killer: For 1,000 users, you'd execute 1,001 queries
Network overhead: Each query requires a roundtrip to the database
Resource intensive: Database must parse/execute many simple queries
Solution :
Eager Loading with JOINs
Batch Loading (WHERE IN)
ORM-Specific Solutions (context.Users.Include(u => u.Orders).Where(u => u.Active).ToList();
)
4. Data Loader Pattern (GraphQL/common)
// Using DataLoader to batch requests
const userLoader = new DataLoader(async (userIds) => {
const orders = await Order.findAll({ where: { userId: userIds } });
return userIds.map(id => orders.filter(order => order.userId === id));
});
Advanced Techniques
1. Materialized Views: Pre-compute and store complex relationships
2. Denormalization: Duplicate frequently accessed data to avoid joins
3. GraphQL Dataloader: Batch and cache requests automatically
4. Caching: Cache the results of common relationship queries

2. High-Traffic Architecture for Millions of Concurrent Users

Building an architecture that can handle millions of concurrent users requires a multi-layered approach focusing on scalability, resilience, and performance optimization. Here's a comprehensive architecture design:

Detailed Architecture Components

1. Global Traffic Distribution

Geographically distributed DNS (Amazon Route 53, Cloudflare)
Global load balancing with Anycast routing
Edge caching (Cloudflare, Akamai, Fastly)
DDoS protection at network edge

2. Application Layer

Microservices architecture (decoupled services)
Containerized deployment (Kubernetes, ECS)
Auto-scaling based on CPU/memory/request metrics
Stateless design (all state in external stores)
Circuit breakers for fault tolerance
Regional deployment in at least 3 availability zones

3. Data Layer

Database sharding (horizontal partitioning by user ID/region)
Multi-master replication for write scalability
Read replicas for each region
Polyglot persistence (right database for each use case):
- Relational for transactions (PostgreSQL, Aurora)
- Document for flexible data (MongoDB)
- Key-value for caching (Redis)
- Columnar for analytics (Cassandra)
Database proxies for connection pooling (PgBouncer, ProxySQL)

4. Caching Strategy

Multi-level caching:
- CDN for static assets
- Edge caching for dynamic content
- Application caching (Redis/Memcached)
- Database query caching
Cache invalidation strategies:
- Time-based expiration
- Write-through caching
- Event-driven invalidation

5. Asynchronous Processing

Message queues for decoupling (Kafka, RabbitMQ, SQS)
Event-driven architecture for real-time updates
Background workers for heavy processing
Serverless functions for burst workloads

6. Monitoring and Observability

Distributed tracing (Jaeger, Zipkin)
Metrics collection (Prometheus, CloudWatch)
Centralized logging (ELK stack, Datadog)
Synthetic monitoring from multiple regions
Anomaly detection for proactive scaling

Implementation Roadmap

Phase 1: Foundation

Implement horizontal scaling for application servers
Set up read replicas for database
Deploy Redis caching layer
Configure auto-scaling policies

Phase 2: Resilience

Implement database sharding
Set up multi-region deployment
Configure global load balancing
Implement circuit breakers and retry logic

Phase 3: Optimization

Introduce message queues for async processing
Optimize database queries and indexes
Implement advanced caching strategies
Set up comprehensive monitoring

Phase 4: Continuous Improvement

Implement canary deployments
Set up A/B testing infrastructure
Optimize CDN configurations
Refine auto-scaling algorithms

Technology Stack Example

Frontend:

CDN: Cloudflare/CloudFront
Web Framework: Next.js/Nuxt.js (SSR/SSG)
Real-time: WebSockets/Socket.io/signalr flatter

Backend:

Containers: Docker + Kubernetes
Languages: Go/Node.js/Java (Spring Boot)/,net core c#
API Gateway: Kong/Apigee

Data Layer:

Primary DB: PostgreSQL with Citus for sharding
Cache: Redis Cluster
Analytics: ClickHouse
Search: Elasticsearch

Operations:

CI/CD: GitHub Actions/Jenkins
Monitoring: Prometheus + Grafana
Logging: ELK Stack/App insight
Infrastructure as Code: Terraform

Key Considerations

Data Consistency: Choose between strong and eventual consistency based on use cases
Cost Optimization: Use spot instances for stateless workloads
Disaster Recovery: Multi-region active-active setup
Security: Zero-trust architecture with mutual TLS

Zero-Trust Architecture with Mutual TLS (mTLS)

Core Principles

Never Trust, Always Verify: Every request must be authenticated and authorized
Least Privilege Access: Grant minimum necessary permissions
Micro-Segmentation: Isolate workloads and services

Compliance: Data residency requirements per region- GDPR/

A/B Testing Infrastructure Setup (Brief)

(((Core Components

Feature Flag System - Central service to control variants (LaunchDarkly, Optimizely, or in-house)
Traffic Splitter - Router to distribute users (at CDN, load balancer, or application layer)
Data Pipeline - Collect metrics (clickstream, conversions) to analytics
Statistical Engine - Calculate significance of results (Python/R or SaaS tool)

Minimal Setup Steps

Integrate SDK from your feature flag provider
Define experiments with control/variant ratios
Implement tracking for key metrics
Analyze results with built-in dashboards or custom tools

Key Considerations

User Stickiness: Ensure consistent experience per user
Sample Size: Determine minimum required users before analysis
Isolation: Test one change at a time for clear attribution)))))

3. How Improve performance of UI /Angular base application

To improve the performance of your Web API, focus on scalability, latency reduction, and efficient resource utilization. Below are key strategies:

1. Optimize API Design & Architecture

Use Efficient Protocols & Formats

HTTP/2 or HTTP/3 (QUIC) for multiplexing and reduced latency.
JSON over XML (smaller payloads, faster parsing).
Protocol Buffers (Protobuf) or MessagePack for binary serialization (faster than JSON).

REST vs. GraphQL vs. gRPC

REST: Simple caching, but over-fetching issues.
GraphQL: Reduces over-fetching but needs query optimization.
gRPC: High performance (HTTP/2 + Protobuf), ideal for microservices.

2. Caching Strategies

Client-Side Caching

Cache-Control, ETag, and Last-Modified headers.
Service Workers for PWA caching.

Server-Side Caching

In-Memory Caching (Redis, Memcached) for frequent queries.
CDN Caching for static assets and API responses.
Database Query Caching (PostgreSQL, MySQL).

Edge Caching

Varnish, Cloudflare Workers for caching API responses at the edge.

3. Database Optimization

Query Optimization

Indexing (avoid full table scans).
Use EXPLAIN ANALYZE (PostgreSQL) to debug slow queries.
Avoid N+1 queries (use JOINs or batch loading).

Database Scaling

Read Replicas for read-heavy workloads.
Sharding for write-heavy workloads.
Connection Pooling (PgBouncer, HikariCP).

4. Load Balancing & Scaling

Horizontal Scaling

Kubernetes (auto-scaling API pods).
Serverless (AWS Lambda, Cloud Functions) for burst traffic.

Load Balancing

Round-Robin, Least Connections, or IP Hash (NGINX, HAProxy).
Global Load Balancing (AWS ALB, Cloudflare LB).

5. Asynchronous Processing

Background Jobs

Message Queues (Kafka, RabbitMQ, SQS) for slow tasks.
Event-Driven Architecture (Webhooks, WebSockets).

Non-Blocking I/O

Node.js, Go, or .NET Core for async APIs.
Use async/await (avoid blocking calls).

6. Compression & Payload Optimization

Response Compression

Gzip/Brotli compression (Accept-Encoding header).
Minify JSON responses (remove whitespace).

Pagination & Partial Responses

limit & offset for large datasets.
GraphQL fragments or REST fields parameter to fetch only needed data.

7. Rate Limiting & Throttling

Leaky Bucket or Token Bucket algorithms.
Redis-based rate limiting (e.g., redis-cell).
Cloudflare Rate Limiting or API Gateway policies.

8. Monitoring & Performance Tuning

Logging & Metrics

Prometheus + Grafana for real-time monitoring.
Distributed Tracing (Jaeger, Zipkin) for latency analysis.

Performance Testing

Locust, k6 for load testing.
Profiling (Python: cProfile, Java: JProfiler).

9. Security & Latency Trade-offs

mTLS adds overhead → balance with connection pooling.
JWT vs. Sessions: JWT is stateless but larger payloads.

10. Infrastructure Optimization

Use HTTP/3 (QUIC) if high-latency networks.
Keep-Alive Connections (reduces TCP handshake overhead).
Geographically Distributed DBs (CockroachDB, DynamoDB Global Tables).

Quick Checklist for Fast APIs

✅ Use HTTP/2 or HTTP/3
✅ Enable Gzip/Brotli compression
✅ Cache responses (Redis, CDN)
✅ Optimize DB queries (indexes, JOINs)
✅ Implement pagination & partial responses
✅ Use async processing for heavy tasks
✅ Load test before scaling

Example: FastAPI (Python) with Redis Caching

from fastapi import FastAPI
from fastapi_cache import FastAPICache
from fastapi_cache.backends.redis import RedisBackend
from redis import Redis

app = FastAPI()

@app.on_event("startup")
async def startup():
    redis = Redis(host="redis", port=6379)
    FastAPICache.init(RedisBackend(redis), prefix="api-cache")

@app.get("/data")
@cache(expire=60)  # Cache for 60 seconds
async def get_data():
    return {"message": "Cached response!"}

Final Thoughts

Measure first (use APM tools like New Relic, Datadog).
Optimize bottlenecks (DB, network, CPU).
Scale horizontally when needed.

Improving the performance of a Web API involves several strategies that focus on reducing latency, optimizing resource usage, and ensuring scalability. Below are key areas to focus on:

1. Optimize Database Queries

Indexing: Ensure your database tables are properly indexed for faster search and retrieval.
Efficient Queries: Avoid unnecessary joins and select only the required columns.
Caching: Cache frequently accessed data at the database or API level to reduce the need for repeated queries.
Database Connection Pooling: Use connection pooling to manage database connections efficiently.
Lazy Loading & Pagination: For large datasets, implement pagination or lazy loading to load data incrementally.

2. Use Caching

Response Caching: Cache responses at the API level (using HTTP cache headers or reverse proxies like Varnish or CDN) to avoid repetitive computation.
Data Caching: Use in-memory caching systems like Redis or Memcached for frequently accessed data, reducing the load on your backend.
Distributed Caching: Use distributed caches if your API is horizontally scaled.

3. Asynchronous Processing

Non-blocking APIs: Implement asynchronous request handling, especially for long-running tasks.
Background Jobs: Offload heavy or non-critical tasks (like sending emails or image processing) to background workers, using tools like RabbitMQ, Kafka, or Celery.
Task Queues: Use queues to manage tasks and avoid overloading your API.

4. Rate Limiting and Throttling

Limit Requests: Protect your API from being overwhelmed by excessive requests by limiting the rate at which clients can call your API (e.g., using a sliding window or token bucket approach).
Error Handling: Implement proper rate-limiting strategies and return meaningful error codes (e.g., HTTP 429 Too Many Requests).

5. Optimize Serialization

Efficient Formats: Use binary formats (e.g., Protocol Buffers, MessagePack) for data serialization instead of JSON or XML, as they are more efficient in terms of size and speed.
Partial Responses: Allow clients to request only the necessary fields (e.g., using GraphQL or field selection in REST APIs).
Compression: Compress large response bodies (e.g., using GZIP or Brotli) to reduce transfer times.

6. Use Content Delivery Networks (CDN)

For static assets (images, JavaScript files, CSS, etc.), offload the delivery to a CDN to reduce the load on your origin server and improve response times for end-users across different regions.

7. Implement Load Balancing

Use load balancers to distribute incoming requests across multiple instances of your API, improving both availability and performance. Tools like NGINX, HAProxy, or cloud-based load balancing solutions are helpful.

8. Optimize Network Performance

Reduce Latency: Deploy your API in multiple regions close to your users to reduce round-trip time.
Keep-alive Connections: Use HTTP persistent connections to reduce overhead caused by opening and closing connections repeatedly.

9. API Gateway

Use an API Gateway to manage tasks like authentication, rate limiting, caching, and monitoring in a centralized way, offloading these responsibilities from your API servers.

10. Use HTTP/2 or HTTP/3

Use HTTP/2 or HTTP/3 for better multiplexing, header compression, and faster load times, as they improve how browsers and servers communicate.

11. Use Efficient Algorithms and Data Structures

Ensure that the logic behind your API endpoints uses efficient algorithms and data structures. This reduces CPU time and memory consumption.

12. Optimize Logging and Monitoring

Avoid excessive logging: Too much logging can slow down performance. Log only essential information and use a proper log management tool (e.g., ELK stack).
Monitor Performance: Use tools like New Relic, Prometheus, or Datadog to monitor your API’s performance, response times, and bottlenecks.

13. API Versioning

When making breaking changes, ensure that older versions of the API are supported for a reasonable time. Versioning helps in managing upgrades smoothly without impacting performance.

14. Service Optimization

Microservices Architecture: If you are scaling out your application, consider breaking it into microservices. This allows for better load distribution and fault isolation.
Serverless: For sporadic traffic, consider serverless architectures (e.g., AWS Lambda, Azure Functions) where you pay only for what you use and automatically scale based on demand.

15. Use HTTP/2 Server Push

For certain resources like images, JavaScript, and CSS files, HTTP/2 Server Push can preemptively send these resources to the client before they are requested, reducing load time.

Conclusion

Improving the performance of your Web API is an ongoing process that requires evaluating and optimizing different components of your system, from the database layer to the network. By focusing on caching, efficient query handling, network optimizations, and monitoring, you can significantly enhance the performance of your API. Regular profiling and testing are crucial to ensure that you’re continuously optimizing based on real-world usage patterns.

3. How Improve performance of UI /Angular base application

Angular Application Performance Optimization Guide

To significantly improve the performance of your Angular application, focus on load time, rendering efficiency, and runtime optimizations. Below are actionable strategies:

1. Build Optimization

Enable Production Mode

ng build --configuration=production

Enables:
- Ahead-of-Time (AOT) compilation
- Tree-shaking (dead code elimination)
- Minification (UglifyJS/Terser)
- CSS/JS optimization

Lazy Loading Modules

const routes: Routes = [
  { 
    path: 'dashboard', 
    loadChildren: () => import('./dashboard/dashboard.module').then(m => m.DashboardModule) 
  }
];

Reduces initial bundle size by loading features on-demand.

Analyze Bundle Size

npm run build --stats-json && npx webpack-bundle-analyzer dist/stats.json

Identify large dependencies (e.g., moment.js → switch to date-fns).

2. Change Detection Optimization

Use `OnPush` Change Detection

@Component({
  selector: 'app-user',
  templateUrl: './user.component.html',
  changeDetection: ChangeDetectionStrategy.OnPush
})

Reduces unnecessary checks (only updates when @Input() changes or events fire).

Detach Change Detector

constructor(private cd: ChangeDetectorRef) {}

ngOnInit() {
  this.cd.detach(); // Manual control
  setTimeout(() => this.cd.detectChanges(), 1000);
}

Useful for static components.

3. Rendering Performance

Virtual Scrolling (`<cdk-virtual-scroll-viewport>`)

<cdk-virtual-scroll-viewport itemSize="50" class="list-container">
  <div *cdkVirtualFor="let item of items">{{item.name}}</div>
</cdk-virtual-scroll-viewport>

Run HTML

Renders only visible items (critical for large lists).

**TrackBy in `*ngFor`**

<div *ngFor="let item of items; trackBy: trackById">{{item.name}}</div>

Run HTML

trackById(index: number, item: any): number {
  return item.id; // Prevents DOM re-creation
}

4. Network & Loading Optimizations

Preload Critical Resources

<link rel="preload" href="critical.css" as="style">
<link rel="preload" href="main.js" as="script">

Run HTML

Server-Side Rendering (SSR)

ng add @nguniversal/express-engine

Improves Time-to-Interactive (TTI) and SEO.

Service Workers (PWA)

ng add @angular/pwa

Caches assets for offline use.

5. Memory Management

Unsubscribe from Observables

private destroy$ = new Subject<void>();

ngOnInit() {
  this.dataService.getData()
    .pipe(takeUntil(this.destroy$))
    .subscribe(data => ...);
}

ngOnDestroy() {
  this.destroy$.next();
  this.destroy$.complete();
}

Prevents memory leaks.

Avoid Memory-Hungry Libraries

Replace lodash with native methods or smaller alternatives like lodash-es.

6. Runtime Optimizations

Debounce Input Events

searchTerm = new FormControl('');

ngOnInit() {
  this.searchTerm.valueChanges
    .pipe(debounceTime(300))
    .subscribe(term => this.search(term));
}

Reduces unnecessary API calls.

Web Workers for Heavy Tasks

const worker = new Worker('./app.worker', { type: 'module' });
worker.postMessage({ data: largeDataSet });

Offload CPU-intensive operations.

7. Angular-Specific Tips

**Avoid `*ngIf` with Complex Logic**

<!-- Bad -->
<div *ngIf="(user$ | async)?.isAdmin && (config$ | async)?.allowEdit"></div>

<!-- Better -->
<ng-container *ngIf="{ user: user$ | async, config: config$ | async } as vm">
  <div *ngIf="vm.user?.isAdmin && vm.config?.allowEdit"></div>
</ng-container>

Run HTML

Use Pure Pipes

@Pipe({ name: 'filter', pure: true }) // Only re-runs on input change
export class FilterPipe implements PipeTransform { ... }

8. Monitoring & Debugging

Angular DevTools

Profile change detection cycles.
Inspect component hierarchies.

Lighthouse Audit

npm install -g lighthouse
lighthouse http://localhost:4200 --view

Identifies performance bottlenecks.

Quick Checklist

✅ Enable AOT + production build
✅ Lazy-load modules
✅ Use OnPush change detection
✅ Virtual scroll for large lists
✅ Preload critical assets
✅ Unsubscribe from observables
✅ Audit with Lighthouse

Improving the performance of a UI/Angular-based application involves optimizing both the frontend (UI/UX) and the interactions between the frontend and backend. Here are some effective strategies for enhancing the performance of an Angular application:

1. Lazy Loading Modules

Lazy load Angular modules: Instead of loading the entire application on the initial load, break the application into feature modules and use Angular's lazy loading feature to load only the required parts as needed.
```
typescript
const routes: Routes = [
  {
    path: 'feature',
    loadChildren: () => import('./feature/feature.module').then(m => m.FeatureModule)
  }
];
```
This reduces the initial bundle size, speeding up load times.

2. AOT Compilation

Enable Ahead-of-Time (AOT) Compilation: AOT compiles the Angular application during build time instead of runtime, improving the load time as there is no need to compile Angular templates and components in the browser.
- Use ng build --prod or enable AOT in the Angular configuration.

3. Change Detection Optimization

Use OnPush Change Detection Strategy: By default, Angular uses the Default change detection strategy, which checks every component for changes on each event (click, HTTP request, etc.). With the OnPush strategy, Angular only checks components when inputs change or events occur within the component.
```
typescript
@Component({
  selector: 'app-component',
  changeDetection: ChangeDetectionStrategy.OnPush,
  templateUrl: './app.component.html',
})
```
Detach Change Detection: For parts of the application that don’t need to update frequently, you can detach change detection to prevent unnecessary checks.

4. Minimize Bundles and Optimize Assets

Bundle Optimization: Use Angular CLI's built-in features to minimize JavaScript and CSS files (ng build --prod). This will minimize and tree-shake unused code, reducing the size of your application.
Tree Shaking: Angular automatically performs tree-shaking during production builds. Ensure that unused or unnecessary imports are removed from the code to keep the final bundle size smaller.
Minify Assets: Use tools like Terser to minimize JavaScript and CSS files.
Optimize Images: Use image optimization techniques, like compression (e.g., WebP format), responsive images, or tools like ImageOptim or ImageMagick.

5. HTTP Optimization

HTTP/2: Ensure your server supports HTTP/2 for faster multiplexed connections.
Debounce User Input: For search or filtering features, debounce the input before making HTTP requests to avoid making too many requests in a short period.
API Response Caching: Cache frequently requested API responses using localStorage or sessionStorage, or even a service worker with Angular PWA support.
Optimize API Calls: Reduce the number of HTTP requests and ensure that API endpoints return only the necessary data.

6. Use Service Workers for Caching (Progressive Web App - PWA)

Implement Service Workers: Use Angular’s Service Worker capabilities to cache assets and data for offline use and faster load times.
Cache HTTP Requests: Service workers can cache API responses, making the app work offline or load faster on subsequent visits.
```
bash
ng add @angular/pwa
```

7. Efficient Rendering

Virtual Scrolling: For long lists or data tables, implement virtual scrolling to load only a portion of the data at a time. This avoids rendering large sets of data and improves performance.
```
typescript
<cdk-virtual-scroll-viewport itemSize="50" class="viewport">
  <div *cdkVirtualFor="let item of items">{{ item }}</div>
</cdk-virtual-scroll-viewport>
```

Track By in *ngFor: When using *ngFor to loop through large lists, use the trackBy function to optimize rendering and reduce the number of DOM elements Angular needs to check.

typescript
<div *ngFor="let item of items; trackBy: trackById">
  {{ item.name }}
</div>

typescript
trackById(index: number, item: any): any {
  return item.id;
}

8. Optimize CSS

Remove Unused CSS: Remove unused CSS using tools like PurgeCSS or CSSNano. This will reduce the size of your CSS files.
CSS Specificity and Redundancy: Avoid excessive and redundant CSS selectors that can increase the rendering time.
Critical CSS: Inline critical CSS to ensure fast rendering for the first view.

9. Reduce Third-Party Dependencies

Audit Dependencies: Review and remove unnecessary third-party libraries. Use lightweight alternatives when possible.
Lazy-load Libraries: If a third-party library is only needed for certain features, load it lazily to avoid increasing the initial bundle size.

10. Optimize Angular's Zone.js

Use NgZone for Performance: Angular uses Zone.js to trigger change detection. However, excessive or unnecessary triggers can hurt performance. You can use NgZone to manually control when to run code inside the Angular zone and trigger change detection.
```
typescript
constructor(private zone: NgZone) {}

this.zone.runOutsideAngular(() => {
  // Perform long-running tasks outside Angular zone to avoid triggering change detection.
});
```

11. Minimize the Use of `ngOnInit` and `ngDoCheck`

Avoid heavy logic inside ngOnInit and ngDoCheck because these lifecycle hooks run frequently. If the logic is expensive, consider moving it to a service and using async operations.

12. Improve Font Loading

Lazy-load Fonts: Use font-display: swap in your CSS to avoid blocking the rendering of the page while fonts are loading.
Subset Fonts: Consider using font subsetting to reduce the size of the font files, only including the characters that are actually used in your application.

13. Profile and Analyze Performance

Use Angular's Performance Tools: Use tools like Angular DevTools, Chrome DevTools, or Lighthouse to profile your Angular app and pinpoint performance bottlenecks.
Web Vitals: Use Web Vitals to measure the performance of your app from a user perspective (e.g., FCP, LCP, CLS).

14. Preload Data and Assets

Preload Critical Data: Use the <link rel="preload"> HTML tag to preload critical assets like CSS, JavaScript, and fonts to improve the perceived load time.

15. Server-Side Rendering (SSR)

Use Angular Universal: Implement Angular Universal to render pages on the server side before they are sent to the client. This reduces the initial load time and provides SEO benefits.
```
bash
ng add @nguniversal/express-engine
```

Conclusion

Optimizing Angular applications for performance involves a combination of best practices and the right tooling. Focus on reducing the initial bundle size, optimizing the change detection mechanism, reducing unnecessary HTTP requests, and caching frequently used data. Regularly profile your application to spot potential bottlenecks and continuously improve its performance based on real user interactions.

Thursday, April 17, 2025

Performance Improvement of API /DB/UI

or High-Traffic Architecture for Millions of Concurrent Users

Indexing Strategies

Query Optimization

Database Structure

Infrastructure Improvements

Application-Level Techniques

Monitoring and Maintenance

What is the N+1 Problem?

Why N+1 is Problematic

Eager Loading with JOINs

Batch Loading (WHERE IN)

ORM-Specific Solutions (context.Users.Include(u => u.Orders).Where(u => u.Active).ToList();

)

4. Data Loader Pattern (GraphQL/common)

Advanced Techniques

2. High-Traffic Architecture for Millions of Concurrent Users

Detailed Architecture Components

1. Global Traffic Distribution

2. Application Layer

3. Data Layer

4. Caching Strategy

5. Asynchronous Processing

6. Monitoring and Observability

Implementation Roadmap

Phase 1: Foundation

Phase 2: Resilience

Phase 3: Optimization

Phase 4: Continuous Improvement

Technology Stack Example

Key Considerations

Zero-Trust Architecture with Mutual TLS (mTLS)

Core Principles

A/B Testing Infrastructure Setup (Brief)

(((Core Components

Minimal Setup Steps

Key Considerations

1. Optimize API Design & Architecture

Use Efficient Protocols & Formats

REST vs. GraphQL vs. gRPC

2. Caching Strategies

Client-Side Caching

Server-Side Caching

Edge Caching

3. Database Optimization

Query Optimization

Database Scaling

4. Load Balancing & Scaling

Horizontal Scaling

Load Balancing

5. Asynchronous Processing

Background Jobs

Non-Blocking I/O

6. Compression & Payload Optimization

Response Compression

Pagination & Partial Responses

7. Rate Limiting & Throttling

8. Monitoring & Performance Tuning

Logging & Metrics

Performance Testing

9. Security & Latency Trade-offs

10. Infrastructure Optimization

Quick Checklist for Fast APIs

Example: FastAPI (Python) with Redis Caching

Final Thoughts

1. Optimize Database Queries

2. Use Caching

3. Asynchronous Processing

4. Rate Limiting and Throttling

5. Optimize Serialization

6. Use Content Delivery Networks (CDN)

7. Implement Load Balancing

8. Optimize Network Performance

9. API Gateway

10. Use HTTP/2 or HTTP/3

11. Use Efficient Algorithms and Data Structures

12. Optimize Logging and Monitoring

13. API Versioning

14. Service Optimization

Use `OnPush` Change Detection

Virtual Scrolling (`<cdk-virtual-scroll-viewport>`)

**TrackBy in `*ngFor`**

**Avoid `*ngIf` with Complex Logic**

11. Minimize the Use of `ngOnInit` and `ngDoCheck`