LinkedHashMap and Specialized Maps

Master LinkedHashMap for predictable iteration and LRU caching. Explore WeakHashMap for memory-sensitive caching and IdentityHashMap for reference-based equality. Build production-ready caches in under 20 lines of code.

📋 At a Glance

🎯 What You'll Learn

After completing this article, you will be able to:

1. Implement LRU cache with LinkedHashMap in 20 lines
2. Understand accessOrder vs insertionOrder in LinkedHashMap
3. Use WeakHashMap for memory-sensitive caching
4. Apply IdentityHashMap for reference equality scenarios
5. Choose the right specialized map for each use case

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Production Story: The 20-Line LRU Cache That Saved the Day

The Incident

Our image processing service was running out of memory. Each processed image was being cached indefinitely, and with thousands of requests per minute, we were hitting OOM errors.

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The ops team was scaling up servers, but the real problem was unbounded cache growth.

The 20-Line Fix

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How It Works

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The removeEldestEntry Hook

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Impact

• Memory usage dropped from 8GB to 500MB
• Response time improved (less GC pressure)
• Zero additional dependencies
• Solution fits in a single file

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Mental Model: The Restaurant Waiting List

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Deep Dive: LinkedHashMap Internals

Entry Structure

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Visual: HashMap + Linked List

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Construction Options

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Access Order Behavior

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Deep Dive: LRU Cache Implementation

Basic LRU Cache

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LRU Cache with Expiration

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LRU Cache with Load Function

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Deep Dive: WeakHashMap

How WeakHashMap Works

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WeakHashMap Internals

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WeakHashMap Use Cases

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WeakHashMap Gotchas

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Deep Dive: IdentityHashMap

Reference Equality vs Object Equality

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IdentityHashMap Use Cases

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IdentityHashMap Internals

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Deep Dive: EnumMap

Ultra-Fast Enum Key Maps

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EnumMap Performance

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Specialized Map Comparison

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⚠️ Common Mistakes

Mistake 1: Forgetting accessOrder Parameter

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Mistake 2: WeakHashMap with Strong Value References

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Mistake 3: Using WeakHashMap for Value-Based Caching

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Mistake 4: Modifying LinkedHashMap During Iteration

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Mistake 5: IdentityHashMap with Immutable Keys

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🐛 Debug This

Challenge 1: The Non-Evicting Cache

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1. After puts: A, B, C (insertion order)
2. get("A") moves A to end: B, C, A
3. put("D") adds D, size=4 > 3, removes eldest (B): C, A, D

Challenge 2: The Immortal Entry

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• "key" is a string literal (interned), always reachable → stays
• new String("key2") has no other references → collected by GC

Challenge 3: The Identity Puzzle

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Integer caches values from -128 to 127. So:

• 100 is cached → same object both times → 1 entry
• 200 is NOT cached → different objects → 2 entries

Wait, that would be 3 entries. Let me reconsider...

Actually:

• First 100 and second 100 are same cached object → 1 entry
• First 200 and second 200 are different objects (no caching) → 2 entries

Total: 3 entries? Let me verify the Integer cache behavior...

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💻 Exercises

Exercise 1: Thread-Safe LRU Cache

Implement a thread-safe LRU cache:

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Exercise 2: WeakValueHashMap

Implement a map where values (not keys) are weakly referenced:

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Exercise 3: Most Recently Used (MRU) Cache

Implement a cache that evicts the MOST recently used item (opposite of LRU):

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Exercise 4: Caching with Soft References

Implement a memory-sensitive cache using SoftReferences:

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Exercise 5: Object Graph Size Calculator

Use IdentityHashMap to calculate unique object count in an object graph:

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🎤 Senior-Level Interview Questions

Question 1: LRU Cache Implementation

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Question 2: LinkedHashMap Access Order

Operations that trigger reordering (move to end):

• get(key) when key exists
• put(key, value) - both insert and update
• putIfAbsent(key, value) when key doesn't exist
• compute(), computeIfAbsent(), computeIfPresent()
• merge()
• replace()

Operations that DON'T trigger reordering:

• containsKey(key) - read-only check
• containsValue(value) - read-only check
• Iteration - doesn't modify structure
• remove(key) - removes, doesn't reorder

Question 3: WeakHashMap GC Behavior

Entries are removed lazily, not immediately after key is GC'd:

1. Key becomes unreachable
2. GC runs and collects the key
3. WeakReference is added to ReferenceQueue
4. Next WeakHashMap operation polls queue and removes stale entries

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Question 4: IdentityHashMap Use Cases

Use IdentityHashMap when:

1. Object graph traversal: Track visited objects without equals() interference
2. Serialization: Map objects to IDs by identity
3. Proxy/wrapper tracking: Original object → proxy mapping
4. Interned object handling: When you specifically want reference equality

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Question 5: EnumMap Performance

EnumMap uses the enum's ordinal as array index:

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Question 6: Thread Safety in LinkedHashMap

Three approaches:

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📝 Summary & Key Takeaways

LinkedHashMap

• HashMap + doubly-linked list for ordering
• insertionOrder (default) or accessOrder (for LRU)
• removeEldestEntry() hook for cache eviction
• O(1) operations, slight memory overhead for links

LRU Cache Pattern

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WeakHashMap

• Keys are WeakReferences, auto-collected by GC
• Use for caches where key lifecycle controls entry lifecycle
• Cleanup is lazy (on next operation)
• Don't use with string literals or when value references key

IdentityHashMap

• Uses == instead of equals()
• Use for object graph traversal, serialization
• More memory-efficient for small maps
• Linear probing instead of chaining

EnumMap

• O(1) array-indexed by ordinal
• Always use for enum keys (no exceptions!)
• Most memory-efficient map for enums

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🏁 Conclusion

Specialized maps solve specific problems elegantly. LinkedHashMap enables LRU caches in 20 lines, WeakHashMap provides automatic cleanup tied to GC, and IdentityHashMap handles reference equality scenarios. Knowing when to use each saves hours of custom implementation.

Key takeaways:

1. LRU cache = LinkedHashMap with accessOrder=true + removeEldestEntry
2. WeakHashMap for key-lifecycle-based caching - not for value-based expiration
3. IdentityHashMap for object identity - graph traversal, serialization
4. EnumMap for enum keys - always, no exceptions
5. Thread safety requires extra work - use Caffeine for production caches

In the next article, we'll explore PriorityQueue and heap-based data structures - essential for scheduling, task prioritization, and the classic "top K" problems.

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📅 Review Schedule

To solidify your understanding, review this material:

• Tomorrow: Implement an LRU cache from memory
• In 3 days: Explain WeakHashMap GC behavior to a colleague
• In 1 week: Review IdentityHashMap use cases
• In 2 weeks: Compare all specialized maps from memory

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