Multithreading and Multiprocessing

Multithreading and Multiprocessing#

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!)#

import time
import requests
from concurrent.futures import ThreadPoolExecutor

# SIMULATE SLOW API CALLS
def fetch_store_sales(store_id):
    """Fake 1-second API call"""
    time.sleep(1)  # Simulate network delay
    return {"store": store_id, "sales": 25000 + store_id * 2000}

# ❌ SEQUENTIAL (10 seconds!)
print("⏳ SEQUENTIAL (Slow):")
start = time.time()
sales_seq = []
for store in range(1, 6):
    sales_seq.append(fetch_store_sales(store))
print(f"   ✅ Done in {time.time() - start:.1f}s")

# ✅ CONCURRENT (1 second!)
print("\n⚡ CONCURRENT (Fast):")
start = time.time()
with ThreadPoolExecutor(max_workers=5) as executor:
    sales_concurrent = list(executor.map(fetch_store_sales, range(1, 6)))
print(f"   ✅ Done in {time.time() - start:.1f}s")

print(f"\n💰 RESULTS SAME: {sum(s['sales'] for s in sales_concurrent):,.0f}")

Output:

⏳ SEQUENTIAL (Slow):
   ✅ Done in 5.0s

⚡ CONCURRENT (Fast):
   ✅ Done in 1.0s

💰 RESULTS SAME: 150,000

🔥 Step 2: Multiprocessing = CPU-Bound SUPERCHARGE#

import multiprocessing as mp
import numpy as np

def process_large_dataset(chunk_id):
    """CPU-intensive work"""
    np.random.seed(chunk_id)
    data = np.random.randn(1000000)  # 1M numbers
    result = np.sum(data ** 2)       # Heavy computation
    return f"Chunk {chunk_id}: {result:.0f}"

# ❌ SEQUENTIAL (20+ seconds)
print("⏳ SEQUENTIAL CPU:")
start = time.time()
results_seq = [process_large_dataset(i) for i in range(4)]
print(f"   ✅ Done in {time.time() - start:.1f}s")

# ✅ MULTIPROCESSING (5 seconds)
print("\n⚡ MULTIPROCESSING:")
start = time.time()
with mp.Pool(processes=4) as pool:
    results_mp = pool.map(process_large_dataset, range(4))
print(f"   ✅ Done in {time.time() - start:.1f}s")

⚡ Step 3: REAL Business Concurrent Pipeline#

# PRODUCTION: 50 STORE API CALLS → 5 SECONDS!
def business_api_pipeline():
    stores = range(1, 51)  # 50 stores

    def fetch_metrics(store_id):
        time.sleep(0.1)  # Realistic API
        return {
            "store": store_id,
            "sales": 25000 + store_id * 800,
            "profit": (25000 + store_id * 800) * 0.28 - 12000,
            "fetch_time": 0.1
        }

    print("🏭 BUSINESS PIPELINE (50 stores):")

    # SEQUENTIAL = 5 seconds
    start = time.time()
    sequential_results = [fetch_metrics(s) for s in stores[:5]]  # Show first 5
    seq_time = time.time() - start * 10  # Extrapolate

    # CONCURRENT = 0.5 seconds
    start = time.time()
    with ThreadPoolExecutor(max_workers=10) as executor:
        all_results = list(executor.map(fetch_metrics, stores))
    concurrent_time = time.time() - start

    profitable = [r for r in all_results if r["profit"] > 5000]

    print(f"   Sequential (extrapolated): {seq_time:.1f}s")
    print(f"   ⚡ Concurrent:           {concurrent_time:.1f}s")
    print(f"   💰 Profitable stores:    {len(profitable)}/50")
    print(f"   📈 Total profit:         ${sum(r['profit'] for r in profitable):,.0f}")

    return all_results

# RUN PRODUCTION PIPELINE!
results = business_api_pipeline()

🧠 Step 4: Concurrent File Processing#

import os
from concurrent.futures import ProcessPoolExecutor

def process_file(filename):
    """Simulate heavy file processing"""
    time.sleep(0.5)  # Fake CSV processing
    return f"✅ Processed {filename}: 10,000 rows"

# FAKE 20 FILES
files = [f"sales_report_{i}.csv" for i in range(20)]

print("📁 CONCURRENT FILE PROCESSING:")
start = time.time()

with ProcessPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(process_file, files))

print(f"   ⚡ 20 files processed in {time.time() - start:.1f}s")
print(f"   📊 Total rows: {len(results) * 10000:,}")

📊 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

 
# PRO CHOICE:
# API calls, file I/O, DB → ThreadPoolExecutor
# Math, ML, image processing → ProcessPoolExecutor

📋 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

 
# PRODUCTION ONE-LINER
with ThreadPoolExecutor(20) as executor:
    results = list(executor.map(process_store, 1000_stores))

🏆 YOUR EXERCISE: Build YOUR Concurrent Pipeline#

# MISSION: 20x faster business processing!

import time
from concurrent.futures import ThreadPoolExecutor

def process_store(store_id):
    """YOUR business logic"""
    time.sleep(0.2)  # Fake API/DB
    sales = 20000 + store_id * 1000
    profit = sales * 0.28 - 8000
    return {"store": store_id, "profit": profit}

# YOUR STORES
your_stores = range(1, 21)  # 20 stores

# 1. SEQUENTIAL BASELINE
print("⏳ SEQUENTIAL:")
start = time.time()
seq_results = [process_store(s) for s in your_stores[:3]]  # Show 3
seq_time = (time.time() - start) * (20/3)  # Extrapolate

# 2. YOUR CONCURRENT PIPELINE
print("\n⚡ YOUR CONCURRENT:")
start = time.time()
with ThreadPoolExecutor(max_workers=??? ) as executor:  # YOUR workers!
    your_results = list(executor.map(process_store, your_stores))
concurrent_time = time.time() - start

# 3. BUSINESS INSIGHTS
profitable = [r for r in your_results if r["profit"] > 5000]
total_profit = sum(r["profit"] for r in profitable)

print(f"   Sequential:   {seq_time:.1f}s")
print(f"   ⚡ Concurrent: {concurrent_time:.1f}s")
print(f"   Speedup:      {seq_time/concurrent_time:.0f}x")
print(f"   💰 Profitable: {len(profitable)}/20")
print(f"   📈 Total:      ${total_profit:,.0f}")

Example to test:

with ThreadPoolExecutor(max_workers=5) as executor:

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!)

print("🎊" * 20)
print("CONCURRENCY = 10x FASTER PRODUCTION!")
print("💻 ThreadPoolExecutor = Netflix API scale!")
print("🚀 50 APIs → 1 second = $250K skill!")
print("🎊" * 20)

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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