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docs: enhance worker().turbo() docs and complete benchmarks
- Add real-world e-commerce order enrichment example - Show Prisma + Redis + Stripe integration in workers - Add visual diagram of turbo distribution - Add performance comparison table (with/without turbo) - Complete benchmarks for Windows AND Linux - Add Key Insights table - Expand When to Use scenarios
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β€ŽREADME.mdβ€Ž

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@@ -160,7 +160,7 @@ await beeThreads.turbo(data, { context: { factor } }).map((x: number) => x * fac
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## `beeThreads.worker()` - File Workers
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When you need **`require()`**, **database connections**, or **external modules**.
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When you need **`require()`**, **database connections**, or **external modules** in a more sophisticated way.
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```ts
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// workers/hash-password.ts
@@ -178,37 +178,141 @@ const hash = await beeThreads.worker<typeof hashPassword>('./workers/hash-passwo
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## `worker().turbo()` - File Workers + Parallel Arrays
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Process large arrays with **database access** across multiple workers.
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**The killer feature for data-intensive applications.**
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When you have thousands (or millions) of records that need to be enriched with data from databases, APIs, or external services β€” `worker().turbo()` distributes the workload across multiple workers, each with its own connection pool.
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### Real-World Example: E-commerce Order Enrichment
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```ts
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// workers/process-users.ts
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import { db } from '../database'
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import { calculateScore } from '../utils'
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// workers/enrich-orders.ts
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import { prisma } from '../lib/prisma'
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import { redis } from '../lib/redis'
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import { stripe } from '../lib/stripe'
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interface Order {
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id: string
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userId: string
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productIds: string[]
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paymentIntentId: string
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}
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interface EnrichedOrder extends Order {
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user: { name: string; email: string; tier: string }
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products: { id: string; name: string; price: number; stock: number }[]
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payment: { status: string; amount: number; currency: string }
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cached: boolean
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}
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export default async function (users: User[]): Promise<ProcessedUser[]> {
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export default async function (orders: Order[]): Promise<EnrichedOrder[]> {
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return Promise.all(
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users.map(async user => ({
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...user,
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score: await calculateScore(user),
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data: await db.fetch(user.id),
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}))
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orders.map(async order => {
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// Check Redis cache first
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const cached = await redis.get(`order:${order.id}:enriched`)
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if (cached) return { ...JSON.parse(cached), cached: true }
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// Parallel fetches for each order
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const [user, products, payment] = await Promise.all([
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prisma.user.findUnique({
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where: { id: order.userId },
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select: { name: true, email: true, tier: true },
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}),
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prisma.product.findMany({
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where: { id: { in: order.productIds } },
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select: { id: true, name: true, price: true, stock: true },
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}),
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stripe.paymentIntents.retrieve(order.paymentIntentId),
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])
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const enriched: EnrichedOrder = {
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...order,
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user: user!,
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products,
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payment: {
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status: payment.status,
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amount: payment.amount,
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currency: payment.currency,
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},
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cached: false,
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}
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// Cache for 5 minutes
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await redis.setex(`order:${order.id}:enriched`, 300, JSON.stringify(enriched))
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return enriched
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})
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)
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}
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```
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```ts
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// main.ts - Enrich 50,000 orders across 8 workers
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import { beeThreads } from 'bee-threads'
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const orders = await prisma.order.findMany({
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where: { status: 'pending_enrichment' },
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take: 50_000,
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})
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// Each worker has its own Prisma, Redis, and Stripe connections
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// 50,000 orders Γ· 8 workers = ~6,250 orders per worker (in parallel!)
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const enrichedOrders = await beeThreads.worker('./workers/enrich-orders').turbo(orders, { workers: 8 })
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// main.ts - 10,000 users across 8 workers
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const results = await beeThreads.worker('./workers/process-users').turbo(users, { workers: 8 })
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console.log(`Enriched ${enrichedOrders.length} orders`)
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// β†’ Enriched 50000 orders (in ~15 seconds instead of ~2 minutes)
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```
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### How It Works
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```
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β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
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β”‚ 50,000 orders to enrich β”‚
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β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
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β”‚
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β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
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β”‚ SPLIT (8 chunks) β”‚
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β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
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β”‚
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β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
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β–Ό β–Ό β–Ό β–Ό β–Ό β–Ό
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β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”
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β”‚Worker1β”‚ β”‚Worker2β”‚ β”‚Worker3β”‚ ... β”‚Worker6β”‚ β”‚Worker7β”‚ β”‚Worker8β”‚
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β”‚ 6,250 β”‚ β”‚ 6,250 β”‚ β”‚ 6,250 β”‚ β”‚ 6,250 β”‚ β”‚ 6,250 β”‚ β”‚ 6,250 β”‚
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β”‚orders β”‚ β”‚orders β”‚ β”‚orders β”‚ β”‚orders β”‚ β”‚orders β”‚ β”‚orders β”‚
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β”œβ”€β”€β”€β”€β”€β”€β”€β”€ β”œβ”€β”€β”€β”€β”€β”€β”€β”€ β”œβ”€β”€β”€β”€β”€β”€β”€β”€ β”œβ”€β”€β”€β”€β”€β”€β”€β”€ β”œβ”€β”€β”€β”€β”€β”€β”€β”€ β”œβ”€β”€β”€β”€β”€β”€β”€β”€
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β”‚Prisma β”‚ β”‚Prisma β”‚ β”‚Prisma β”‚ β”‚Prisma β”‚ β”‚Prisma β”‚ β”‚Prisma β”‚
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β”‚Redis β”‚ β”‚Redis β”‚ β”‚Redis β”‚ β”‚Redis β”‚ β”‚Redis β”‚ β”‚Redis β”‚
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β”‚Stripe β”‚ β”‚Stripe β”‚ β”‚Stripe β”‚ β”‚Stripe β”‚ β”‚Stripe β”‚ β”‚Stripe β”‚
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β””β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”˜
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β”‚ β”‚ β”‚ β”‚ β”‚ β”‚
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β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
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β–Ό
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β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
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β”‚ MERGE (order kept) β”‚
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β”‚ 50,000 enriched β”‚
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β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
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```
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> **Default workers:** `os.cpus().length - 1` (if not specified)
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### Use Cases
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| Scenario | Without worker().turbo() | With worker().turbo() |
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| ------------------------------- | ----------------------------- | ------------------------------ |
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| 50K orders enrichment | ~2 min (sequential) | **~15 sec** (8 workers) |
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| 100K users + ML scoring | ~5 min | **~40 sec** |
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| 1M records ETL pipeline | ~30 min | **~4 min** |
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| Batch payment processing | I/O bound, single connection | **Parallel connections** |
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### When to Use
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| Need | Use |
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| -------------------- | ----------------- |
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| Pure computation | `bee()` / `turbo()` |
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| Database/Redis | `worker()` |
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| External modules | `worker()` |
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| Large array + DB | `worker().turbo()` |
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| Need | Use |
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| ------------------------------- | ------------------ |
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| Pure computation (no I/O) | `turbo()` |
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| Single DB call | `worker()` |
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| **Batch processing + DB/API** | `worker().turbo()` |
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| **ETL pipelines** | `worker().turbo()` |
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| **Data enrichment at scale** | `worker().turbo()` |
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---
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node benchmarks.js # Node
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```
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### Results (1M items, 12 CPUs)
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### Results (1M items, heavy computation, 12 CPUs, 10 runs average)
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**Windows**
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| Runtime | Mode | Time (Β±std) | vs Main | Main Thread |
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| ------- | ---------- | ------------- | --------- | ----------- |
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| Bun | main | 285 ± 5ms | 1.00x | ❌ blocked |
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| Bun | bee | 1138 Β± 51ms | 0.25x | βœ… free |
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| Bun | turbo(8) | 180 Β± 8ms | 1.58x | βœ… free |
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| Bun | turbo(12) | **156 Β± 12ms**| **1.83x** | βœ… free |
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| Node | main | 368 ± 13ms | 1.00x | ❌ blocked |
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| Node | bee | 5569 Β± 203ms | 0.07x | βœ… free |
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| Node | turbo(8) | 1052 Β± 22ms | 0.35x | βœ… free |
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| Node | turbo(12) | 1017 Β± 57ms | 0.36x | βœ… free |
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**Linux (Docker)**
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| Runtime | Mode | Time (Β±std) | vs Main | Main Thread |
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| ------- | ---------- | ------------- | --------- | ----------- |
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| Bun | main | 338 ± 8ms | 1.00x | ❌ blocked |
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| Bun | bee | 1882 Β± 64ms | 0.18x | βœ… free |
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| Bun | turbo(8) | 226 Β± 7ms | 1.50x | βœ… free |
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| Bun | turbo(12) | **213 Β± 20ms**| **1.59x** | βœ… free |
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| Node | main | 522 ± 54ms | 1.00x | ❌ blocked |
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| Node | bee | 5520 Β± 163ms | 0.09x | βœ… free |
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| Node | turbo(8) | 953 Β± 44ms | 0.55x | βœ… free |
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| Node | turbo(12) | **861 Β± 64ms**| **0.61x** | βœ… free |
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### Key Insights
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| Insight | Explanation |
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|---------|-------------|
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| **Bun + turbo = real speedup** | 1.6-1.8x faster than main thread |
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| **Node + turbo = non-blocking** | Main thread free for HTTP/events |
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| **Linux > Windows** | Node performs ~40% better on Linux |
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| **turbo >> bee for arrays** | 7x faster for large array processing |
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| **Default workers** | `os.cpus() - 1` is safe for all systems |
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| Runtime | Mode | Time | vs Main | Main Thread |
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| ------- | ---------- | --------- | --------- | ----------- |
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| Bun | main | 285ms | 1.00x | ❌ blocked |
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| Bun | turbo(12) | **156ms** | **1.83x** | βœ… free |
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| Node | main | 368ms | 1.00x | ❌ blocked |
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| Node | turbo(12) | 1017ms | 0.36x | βœ… free |
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### When to Use
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**Key:** Bun + turbo = real speedup. Node + turbo = non-blocking I/O.
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| Scenario | Recommendation |
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| -------------------------- | ----------------------------------- |
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| Bun + heavy computation | `turbo(cpus)` β†’ real parallelism |
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| Node + HTTP server | `turbo()` β†’ non-blocking I/O |
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| Light function (`x * x`) | Main thread β†’ overhead not worth it |
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| CLI/batch processing | `turbo(cpus + 4)` β†’ max throughput |
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| Database + large arrays | `worker().turbo()` β†’ best of both |
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