Js Performance Patterns

JavaScript Performance Patterns

Table of Contents

• When to Use
• Instructions
• Details
• Source

Runtime performance micro-patterns for JavaScript hot paths. These patterns matter most in tight loops, frequent callbacks (scroll, resize, animation frames), and data-heavy operations. They apply to any JavaScript environment — React, Vue, vanilla, Node.js.

When to Use

Reference these patterns when:

• Profiling reveals a hot function or tight loop
• Processing large datasets (1,000+ items)
• Handling high-frequency events (scroll, mousemove, resize)
• Optimizing build-time or server-side scripts
• Reviewing code for performance in critical paths

Instructions

• Apply these patterns only in measured hot paths — code that runs frequently or processes large datasets. Don't apply them to cold code paths where readability is more important than nanosecond gains.

Details

Overview

Micro-optimizations are not a substitute for algorithmic improvements. Address the algorithm first (O(n^2) to O(n), removing waterfalls, reducing DOM mutations). Once the algorithm is right, these patterns squeeze additional performance from hot paths.

1. Use Set and Map for Lookups

Impact: HIGH for large collections — O(1) vs O(n) per lookup.

Array methods like .includes(), .find(), and .indexOf() scan linearly. For repeated lookups against the same collection, convert to Set or Map first.

Avoid — O(n) per check:

const allowedIds = ['a', 'b', 'c', /* ...hundreds more */]
function isAllowed(id: string) {  return allowedIds.includes(id) // scans entire array}
items.filter(item => allowedIds.includes(item.id)) // O(n * m)

Prefer — O(1) per check:

const allowedIds = new Set(['a', 'b', 'c', /* ...hundreds more */])
function isAllowed(id: string) {  return allowedIds.has(id)}
items.filter(item => allowedIds.has(item.id)) // O(n)

For key-value lookups, use Map instead of scanning an array of objects:

// Avoidconst users = [{ id: 1, name: 'Alice' }, { id: 2, name: 'Bob' }]const user = users.find(u => u.id === targetId) // O(n)
// Preferconst userMap = new Map(users.map(u => [u.id, u]))const user = userMap.get(targetId) // O(1)

2. Batch DOM Reads and Writes

Impact: HIGH — Prevents layout thrashing.

Interleaving DOM reads (e.g., offsetHeight, getBoundingClientRect) with DOM writes (e.g., style.height = ...) forces the browser to recalculate layout multiple times. Batch all reads first, then all writes.

Avoid — layout thrashing (read/write/read/write):

elements.forEach(el => {  const height = el.offsetHeight    // read → forces layout  el.style.height = `${height * 2}px` // write})// Each iteration forces a layout recalculation

Prefer — batched reads then writes:

// Read phaseconst heights = elements.map(el => el.offsetHeight)
// Write phaseelements.forEach((el, i) => {  el.style.height = `${heights[i] * 2}px`})

For complex cases, use requestAnimationFrame to defer writes to the next frame, or use a library like fastdom.

CSS class approach — single reflow:

// Avoid multiple style mutationsel.style.width = '100px'el.style.height = '200px'el.style.margin = '10px'
// Prefer — one reflowel.classList.add('expanded')// orel.style.cssText = 'width:100px;height:200px;margin:10px;'

3. Cache Property Access in Tight Loops

Impact: MEDIUM — Reduces repeated property resolution.

Accessing deeply nested properties or array .length in every iteration adds overhead in tight loops.

Avoid:

for (let i = 0; i < data.items.length; i++) {  process(data.items[i].value.nested.prop)}

Prefer:

const { items } = datafor (let i = 0, len = items.length; i < len; i++) {  const val = items[i].value.nested.prop  process(val)}

This matters for arrays with 10,000+ items or when called at 60fps. For small arrays or infrequent calls, the readable version is fine.

4. Memoize Expensive Function Results

Impact: MEDIUM-HIGH — Avoids recomputing the same result.

When a pure function is called repeatedly with the same arguments, cache the result.

Simple single-value cache:

function memoize<T extends (...args: any[]) => any>(fn: T): T {  let lastArgs: any[] | undefined  let lastResult: any
  return ((...args: any[]) => {    if (lastArgs && args.every((arg, i) => Object.is(arg, lastArgs![i]))) {      return lastResult    }    lastArgs = args    lastResult = fn(...args)    return lastResult  }) as T}
const expensiveCalc = memoize((data: number[]) => {  return data.reduce((sum, n) => sum + heavyTransform(n), 0)})

Multi-key cache with Map:

const cache = new Map<string, Result>()
function getResult(key: string): Result {  if (cache.has(key)) return cache.get(key)!  const result = computeExpensiveResult(key)  cache.set(key, result)  return result}

For caches that can grow unbounded, use an LRU strategy or WeakMap for object keys.

5. Combine Iterations Over the Same Data

Impact: MEDIUM — Single pass instead of multiple.

Chaining .filter().map().reduce() creates intermediate arrays and iterates the data multiple times. For large arrays in hot paths, combine into a single loop.

Avoid — 3 iterations, 2 intermediate arrays:

const result = users  .filter(u => u.active)  .map(u => u.name)  .reduce((acc, name) => acc + name + ', ', '')

Prefer — single pass:

let result = ''for (const u of users) {  if (u.active) {    result += u.name + ', '  }}

For small arrays (< 100 items), the chained version is fine and more readable. Optimize only when profiling shows it matters.

6. Short-Circuit with Length Checks First

Impact: LOW-MEDIUM — Avoids expensive operations on empty inputs.

Before running expensive comparisons or transformations, check if the input is empty.

function findMatchingItems(items: Item[], query: string): Item[] {  if (items.length === 0 || query.length === 0) return []
  const normalized = query.toLowerCase()  return items.filter(item =>    item.name.toLowerCase().includes(normalized)  )}

7. Return Early to Skip Unnecessary Work

Impact: LOW-MEDIUM — Reduces average-case execution.

Structure functions to exit as soon as possible for common non-matching cases.

Avoid — always does full work:

function processEvent(event: AppEvent) {  let result = null  if (event.type === 'click') {    if (event.target && event.target.matches('.actionable')) {      result = handleAction(event)    }  }  return result}

Prefer — exits early:

function processEvent(event: AppEvent) {  if (event.type !== 'click') return null  if (!event.target?.matches('.actionable')) return null  return handleAction(event)}

8. Hoist RegExp and Constant Creation Outside Loops

Impact: LOW-MEDIUM — Avoids repeated compilation.

Creating RegExp objects or constant values inside loops or frequently-called functions wastes CPU.

Avoid — compiles regex 10,000 times:

function validate(items: string[]) {  return items.filter(item => {    const pattern = /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/    return pattern.test(item)  })}

Prefer — compile once:

const EMAIL_PATTERN = /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/
function validate(items: string[]) {  return items.filter(item => EMAIL_PATTERN.test(item))}

9. Use toSorted(), toReversed(), toSpliced() for Immutability

Impact: LOW — Correct immutability without manual copying.

The new non-mutating array methods avoid the [...arr].sort() pattern and communicate intent more clearly.

Avoid — manual copy then mutate:

const sorted = [...items].sort((a, b) => a.price - b.price)const reversed = [...items].reverse()const without = [...items]; without.splice(index, 1)

Prefer — non-mutating methods:

const sorted = items.toSorted((a, b) => a.price - b.price)const reversed = items.toReversed()const without = items.toSpliced(index, 1)

These are available in all modern browsers and Node.js 20+.

10. Use requestAnimationFrame for Visual Updates

Impact: MEDIUM — Syncs with the browser's render cycle.

DOM updates triggered outside the rendering cycle (from timers, event handlers, etc.) can cause jank. Batch visual updates inside requestAnimationFrame.

Avoid — updates outside render cycle:

window.addEventListener('scroll', () => {  progressBar.style.width = `${getScrollPercent()}%`  counter.textContent = `${getScrollPercent()}%`}, { passive: true })

Prefer — synced to render:

let ticking = false
window.addEventListener('scroll', () => {  if (!ticking) {    requestAnimationFrame(() => {      const pct = getScrollPercent()      progressBar.style.width = `${pct}%`      counter.textContent = `${pct}%`      ticking = false    })    ticking = true  }}, { passive: true })

11. Use structuredClone for Deep Copies

Impact: LOW — Correct deep cloning without libraries.

structuredClone() handles circular references, typed arrays, Dates, RegExps, Maps, and Sets — unlike JSON.parse(JSON.stringify()).

// Avoid — loses Dates, Maps, Sets, undefined valuesconst copy = JSON.parse(JSON.stringify(original))
// Prefer — handles all standard typesconst copy = structuredClone(original)

Note: structuredClone cannot clone functions or DOM nodes. For those cases, implement a custom clone.

12. Prefer Map Over Plain Objects for Dynamic Keys

Impact: LOW-MEDIUM — Better performance for frequent additions/deletions.

V8 optimizes plain objects for static shapes. When keys are added and removed dynamically (caches, counters, registries), Map provides consistently better performance.

// Avoid for dynamic keysconst counts: Record<string, number> = {}items.forEach(item => {  counts[item.category] = (counts[item.category] || 0) + 1})
// Prefer for dynamic keysconst counts = new Map<string, number>()items.forEach(item => {  counts.set(item.category, (counts.get(item.category) ?? 0) + 1)})

Source

Patterns from patterns.dev — JavaScript performance guidance for the broader web engineering community.