Multithreading and Multiprocessing

Multithreading and Multiprocessing#

⏳ Loading Pyodide…

ThreadPoolExecutor = 10 seconds → 1 second Multiprocessing = CPU-bound 100x speedup

Netflix/Spotify = 90% concurrent code

🎯 Concurrency = Speed Multiplier#

Task

Sequential

Concurrent

Speedup

Business Win

10 API calls

10 seconds

1 second

10x

Real-time dashboards

1000 files

60 seconds

6 seconds

10x

Batch processing

ML predictions

300 seconds

30 seconds

10x

Live recommendations

Image processing

120 seconds

12 seconds

10x

Photo uploads

🚀 Step 1: ThreadPoolExecutor = FAST API Calls (Run this!)#

Output:

⏳ SEQUENTIAL (Slow):
   ✅ Done in 5.0s

⚡ CONCURRENT (Fast):
   ✅ Done in 1.0s

💰 RESULTS SAME: 150,000

🔥 Step 2: Multiprocessing = CPU-Bound SUPERCHARGE#

⚡ Step 3: REAL Business Concurrent Pipeline#

🧠 Step 4: Concurrent File Processing#

📊 Step 5: ThreadPool vs ProcessPool Decision Matrix#

Task Type

Use

Why

Example

I/O Bound

Threads

Network waits

API calls ✅

CPU Bound

Processes

CPU cores

ML training ✅

Mixed

Threads

Simpler

File + API

Database

Threads

Connection pooling

SQL queries

📋 Concurrency Cheat Sheet (Interview Gold)#

Pattern

Code

Speedup

Business Use

API Calls

executor.map(fetch_api, urls)

10x

Competitor pricing

File Batch

executor.map(process_file, files)

8x

Report generation

CPU Math

mp.Pool().map(compute, data)

16x

Risk calculations

Mixed

as_completed(futures)

12x

Full pipelines

🏆 YOUR EXERCISE: Build YOUR Concurrent Pipeline#

Example to test:

YOUR MISSION:

  1. Set YOUR max_workers (4-10)

  2. Run + compare speeds

  3. Screenshot → “I write 20x faster code!”

🎉 What You Mastered#

Concurrency

Status

Business Power

ThreadPool

✅

10x API speed

ProcessPool

✅

16x CPU speed

Production pipelines

✅

Batch automation

Decision matrix

✅

Pro architecture

$250K patterns

✅

Staff engineer

Next: APIs/Webscraping (requests + BeautifulSoup = Live competitor data!)

can we appreciate how ThreadPoolExecutor(max_workers=10).map() just turned 50-second API waits into 5-second concurrent magic that processes 1000 stores simultaneously? Your students went from sequential hell to writing Netflix-grade concurrent pipelines that fetch live competitor pricing in real-time. While senior devs still wait 10 minutes for batch jobs, your class is architecting ProcessPoolExecutor for 16x ML speedups. This isn’t threading theory—it’s the $250K+ production accelerator that scales Spotify’s 500M+ API calls without breaking a sweat!

# Your code here
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