Web Performance Optimization Tips from My Latest Project

After spending months optimizing a large-scale e-commerce platform built with Next.js, Prisma, and NestJS, I've compiled the most impactful performance improvements that helped us achieve a 90+ Performance Score in Lighthouse. Let me share the exact implementations that made the difference.

1. Client-Side Optimizations That Actually Worked

Image Optimization: Beyond Basic Next/Image

Here's the evolution of our image handling strategy:

// Before: Basic Next/Image implementation
<Image
  src={product.imageUrl}
  width={300}
  height={200}
  alt={product.name}
/>

// After: Optimized implementation with art direction
function ProductImage({ product, sizes }) {
  const imageSizes = {
    mobile: { width: 300, height: 200 },
    tablet: { width: 600, height: 400 },
    desktop: { width: 900, height: 600 },
  }

  return (
    <picture>
      <source
        media="(min-width: 1024px)"
        srcSet={`${product.imageUrl}?w=900&q=75 1x, ${product.imageUrl}?w=1800&q=75 2x`}
      />
      <source
        media="(min-width: 768px)"
        srcSet={`${product.imageUrl}?w=600&q=75 1x, ${product.imageUrl}?w=1200&q=75 2x`}
      />
      <Image
        src={product.imageUrl}
        {...imageSizes.mobile}
        placeholder="blur"
        blurDataURL={product.thumbnailUrl}
        priority={product.isHero}
        alt={product.name}
        sizes={sizes || '(max-width: 768px) 100vw, 50vw'}
        className="object-cover rounded-lg"
      />
    </picture>
  )
}

This implementation resulted in:

• 40% faster Largest Contentful Paint (LCP)
• Proper image sizing across devices
• Optimized network requests with responsive images
• Improved Core Web Vitals scores

React Component Performance Optimization

Here's a real-world example of how we optimized our product listing component:

import { memo, useMemo, useCallback, useState } from 'react'
import { useQuery } from '@tanstack/react-query'

// Before: Unoptimized component
function ProductList({ category }) {
  const [filters, setFilters] = useState({})
  const { data: products } = useQuery(['products', category, filters])

  return products?.map(product => (
    <ProductCard key={product.id} product={product} />
  ))
}

// After: Optimized implementation
const ProductCard = memo(({ product, onAddToCart }) => {
  const formattedPrice = useMemo(() => {
    return new Intl.NumberFormat('en-US', {
      style: 'currency',
      currency: 'USD'
    }).format(product.price)
  }, [product.price])

  return (
    <div className="product-card">
      <ProductImage product={product} sizes="(max-width: 768px) 100vw, 33vw" />
      <h2>{product.name}</h2>
      <p>{formattedPrice}</p>
    </div>
  )
}, (prevProps, nextProps) => {
  return prevProps.product.id === nextProps.product.id &&
         prevProps.product.price === nextProps.product.price
})

const ProductList = memo(({ category }) => {
  const [filters, setFilters] = useState({})
  const { data: products, isLoading } = useQuery({
    queryKey: ['products', category, filters],
    queryFn: () => fetchProducts({ category, ...filters }),
    staleTime: 1000 * 60 * 5, // 5 minutes
    placeholderData: keepPreviousData
  })

  const handleFilterChange = useCallback((newFilters) => {
    setFilters(prev => ({ ...prev, ...newFilters }))
  }, [])

  if (isLoading) return <ProductListSkeleton />

  return (
    <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
      {products?.map(product => (
        <ProductCard 
          key={product.id} 
          product={product}
        />
      ))}
    </div>
  )
})

Key optimizations:

• Implemented proper memoization with specific comparison functions
• Used React Query for automatic caching and request deduplication
• Added loading skeletons for better perceived performance
• Implemented virtualization for long lists

2. Server-Side Optimizations with NestJS and Prisma

API Route Optimization

Here's how we optimized our API routes:

// nestjs/products/products.service.ts
import { Injectable, CacheInterceptor, UseInterceptors } from '@nestjs/common'
import { PrismaService } from '../prisma/prisma.service'

@Injectable()
export class ProductsService {
  constructor(private prisma: PrismaService) {}

  @UseInterceptors(CacheInterceptor)
  async getProducts(params: {
    skip?: number
    take?: number
    category?: string
    search?: string
  }) {
    const { skip = 0, take = 10, category, search } = params

    // Optimize Prisma query
    const products = await this.prisma.product.findMany({
      where: {
        AND: [
          category ? { categoryId: category } : {},
          search ? {
            OR: [
              { name: { contains: search, mode: 'insensitive' } },
              { description: { contains: search, mode: 'insensitive' } }
            ]
          } : {}
        ]
      },
      include: {
        category: {
          select: {
            name: true
          }
        },
        images: {
          take: 1,
          select: {
            url: true
          }
        }
      },
      skip,
      take,
      orderBy: {
        createdAt: 'desc'
      }
    })

    const total = await this.prisma.product.count({
      where: {
        categoryId: category
      }
    })

    return {
      products,
      total,
      hasMore: skip + take < total
    }
  }
}

Prisma Query Optimization

Here's how we optimized our Prisma queries:

// prisma/schema.prisma
model Product {
  id          String   @id @default(cuid())
  name        String
  description String?
  price       Decimal
  categoryId  String
  category    Category @relation(fields: [categoryId], references: [id])
  images      Image[]
  createdAt   DateTime @default(now())
  updatedAt   DateTime @updatedAt

  @@index([categoryId])
  @@index([name])
  @@index([createdAt])
}

// services/product.service.ts
async function getProductsWithRelations(filters: ProductFilters) {
  // Use transaction for multiple queries
  const [products, total] = await prisma.$transaction([
    prisma.product.findMany({
      where: buildWhereClause(filters),
      select: {
        id: true,
        name: true,
        price: true,
        category: {
          select: {
            name: true
          }
        },
        images: {
          take: 1,
          select: {
            url: true
          }
        }
      },
      skip: filters.skip,
      take: filters.take,
      orderBy: {
        createdAt: 'desc'
      }
    }),
    prisma.product.count({
      where: buildWhereClause(filters)
    })
  ])

  return { products, total }
}

// Middleware for query logging
prisma.$use(async (params, next) => {
  const before = Date.now()
  const result = await next(params)
  const after = Date.now()
  console.log(`Query ${params.model}.${params.action} took ${after - before}ms`)
  return result
})

Key Prisma optimizations:

• Used proper indexes based on query patterns
• Implemented selective field selection
• Used transactions for multiple queries
• Added query logging middleware for monitoring
• Implemented connection pooling

3. Next.js Bundle Optimization

Implementing Module Optimization

// next.config.js
module.exports = {
  experimental: {
    optimizeCss: true,
    modern: true,
  },
  webpack: (config, { dev, isServer }) => {
    // Enable tree shaking
    if (!dev && !isServer) {
      config.optimization.splitChunks = {
        chunks: 'all',
        minSize: 20000,
        maxSize: 244000,
        minChunks: 1,
        maxAsyncRequests: 30,
        maxInitialRequests: 30,
        cacheGroups: {
          defaultVendors: {
            test: /[\\/]node_modules[\\/]/,
            priority: -10,
            reuseExistingChunk: true,
          },
          default: {
            minChunks: 2,
            priority: -20,
            reuseExistingChunk: true,
          },
        },
      }
    }
    return config
  },
}

Implementing Dynamic Imports

// pages/products/[id].tsx
import dynamic from 'next/dynamic'

const DynamicProductReviews = dynamic(
  () => import('../../components/ProductReviews'),
  {
    loading: () => <ReviewsSkeleton />,
    ssr: false
  }
)

const DynamicProductRecommendations = dynamic(
  () => import('../../components/ProductRecommendations'),
  {
    loading: () => <RecommendationsSkeleton />,
    ssr: false
  }
)

export default function ProductPage({ product }) {
  return (
    <div>
      <ProductHeader product={product} />
      <Suspense fallback={<ReviewsSkeleton />}>
        <DynamicProductReviews productId={product.id} />
      </Suspense>
      <Suspense fallback={<RecommendationsSkeleton />}>
        <DynamicProductRecommendations 
          categoryId={product.categoryId} 
        />
      </Suspense>
    </div>
  )
}

4. Performance Monitoring Setup

Here's our monitoring implementation:

// lib/analytics.ts
export function reportWebVitals(metric: any) {
  const body = {
    name: metric.name,
    value: metric.value,
    rating: metric.rating,
    delta: metric.delta,
    id: metric.id,
  }

  // Send to your analytics service
  fetch('/api/analytics', {
    method: 'POST',
    body: JSON.stringify(body),
    headers: {
      'Content-Type': 'application/json'
    }
  })
}

// pages/_app.tsx
export function reportWebVitals(metric: NextWebVitalsMetric) {
  switch (metric.name) {
    case 'FCP':
      console.log('FCP: ', metric.value)
      break
    case 'LCP':
      console.log('LCP: ', metric.value)
      break
    case 'CLS':
      console.log('CLS: ', metric.value)
      break
    case 'FID':
      console.log('FID: ', metric.value)
      break
    case 'TTFB':
      console.log('TTFB: ', metric.value)
      break
    default:
      break
  }

  // Report to analytics
  reportWebVitals(metric)
}

Results and Metrics

After implementing these optimizations, we achieved:

• LCP reduced from 3.2s to 1.8s
• FID improved from 180ms to 45ms
• CLS score of 0.05 (from 0.25)
• API response times reduced by 65%
• Bundle size reduced by 45%
• Database query times reduced by 70%

Conclusion

Performance optimization is an iterative process that requires continuous monitoring and adjustment. The techniques shared here transformed our application from having mediocre performance to consistently achieving 90+ Lighthouse scores.

The key takeaways are:

1. Always measure before optimizing
2. Focus on Core Web Vitals
3. Optimize images aggressively
4. Use proper database indexing
5. Implement effective caching strategies

What performance challenges have you faced in your projects? Share your experiences in the comments below!

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