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When to use Singleton: - Configuration manager - Logging framework - Single database connection pool - When NOT to use it: - When you need multiple instances - When global state causes unwanted coupling - When you’ll need easy testing & mocking

⚠️ But be careful: Singleton is sometimes considered an anti-pattern. Why? It introduces global state, can make testing harder, and may lead to tight coupling. Use it only when it truly fits the scenario.

Serialization-Safe Singleton: readResolve() stops deserialization from creating new instance. Pros: Consistent instance Cons: Extra logic needed

Reflection-Safe Singleton: Throws exception if reflection tries to create 2nd object. Pros: More secure Cons: Still bypassable with tricks

Enum Singleton: JVM ensures single instance. Handles serialization + reflection. Pros: Most secure Cons: No lazy loading

Static Inner Class (Bill Pugh): Inner class loads only when needed → lazy + thread-safe. Pros: Cleanest + recommended Cons: None major

Double-Checked Locking (DCL): Checks twice → avoids locking every time. Pros: Fast + thread-safe Cons: More complex

Synchronized Method Singleton: Entire method is locked → thread-safe but slow. Pros: Safe Cons: Expensive locking

Lazy Initialization: Creates instance only on first call. Pros: Lazy, Instance is created only when used. Cons: Breaks in multi-threaded apps

Eager Initialization: Instance is created immediately when class loads. No lazy loading. Pros: Simple Cons: Wastes memory if unused

Let’s break down every Singleton pattern in Java with code, pros/cons + a short explanation of how each one works. Perfect for interviews + real-world apps. 👇👇 #Java #DesignPatterns

Learning how things work under the hood makes coding way more fun! 💡 If you love exploring how core concepts like this actually work — ➡️ Follow me for more bite-sized technical content & dev insights! 🚀

⚙️ Since Java 8, when a single bucket has too many elements, it switches from a LinkedList to a Red-Black Tree for faster lookups (O(log n) instead of O(n)) 🌲 HashMap = elegant mix of array + hashing + tree 🌟 #Java #HashMap #Coding #TechLearning

To handle collisions: ☑️ Java compares keys using .equals() ☑️ If same key → overwrite value ☑️ If different key → add a new node to the linked list (or tree if many collisions)

Here’s the magic 🪄 When you call put(key, value) → 1️⃣ Java computes hash = key.hashCode() 2️⃣ It finds the bucket using index = hash % capacity 3️⃣ If that bucket is empty → store directly 4️⃣ If not → check for collisions (keys with same index)

A HashMap in Java stores key-value pairs — but under the hood, it’s powered by: 🔹 Array of buckets 🔹 Linked Lists (or Balanced Trees in some cases) Each key’s position is decided by its hash code, which determines where it goes in memory.

Just learned how HashMap works internally in Java, and it’s fascinating! 🧠💻 Here’s a simple breakdown for anyone who wants to really get it 👇

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