Swift map filter and reduce

🔬 Swift map filter and reduce (Advanced) – Interview & Production Deep Dive

In Swift map filter and reduce are higher-order functions that enable functional programming in Swift.
They are heavily tested in interviews and used widely in real-world Swift apps.

1️⃣ Quick Concept Recap ⭐

2️⃣ map – Advanced Usage ⭐⭐⭐

Transform Model Objects

Output

map vs compactMap ⭐⭐⭐

Output:

📌 compactMap removes nil.

3️⃣ filter – Complex Conditions ⭐⭐⭐

Output

Filtering Custom Objects

4️⃣ reduce – Powerful Aggregations ⭐⭐⭐

Sum

Dictionary from Array

Output:

📌 reduce(into:) is more efficient.

5️⃣ Chaining Functions ⭐⭐⭐ (Interview Favorite)

Output

6️⃣ flatMap vs compactMap ⭐⭐⭐

Output:

7️⃣ Performance & Laziness ⭐⭐⭐

Using lazy

📌 Stops processing early → performance gain

8️⃣ Side Effects: forEach vs map ⚠️

❌ Wrong:

✔ Correct:

📌 map should return values, not side effects.

9️⃣ Common Interview Pitfalls ❌

❌ Using map instead of forEach
❌ Forgetting compactMap for optionals
❌ Using reduce when simple loop is clearer
❌ Ignoring performance for large collections

🔥 Interview Questions (Advanced)

Q1. Difference between map and compactMap?
👉 compactMap removes nil

Q2. When to use reduce(into:)?
👉 When mutating accumulator efficiently

Q3. What does lazy do?
👉 Delays execution until needed

Q4. Can these replace loops?
👉 Often yes, but not always

✅ Summary

✔ map transforms
✔ filter selects
✔ reduce aggregates
✔ compactMap removes nil
✔ Chaining enables clean logic
✔ lazy improves performance
✔ Core for Swift functional programming & interviews