🟢 Junior Level

In HashMap, the main operations (get, put, remove) work very fast — on average in O(1), that is, constant time.

What this means:

HashMap<String, Integer> map = new HashMap<>();

map.put("key1", 100);   // O(1) — instant
map.get("key1");        // O(1) — instant
map.remove("key1");     // O(1) — instant

With a good hashCode, lookup takes roughly the same time regardless of the map’s size. With a poor hashCode (many collisions) — time grows proportionally.

Worst case: If all keys landed in one bucket, search slows to O(n) in Java 7 or O(log n) in Java 8+. But this rarely happens with a proper hashCode().

🟡 Middle Level

Summary Table

Operation	Average Case	Worst Case (Java 7)	Worst Case (Java 8+)
get()	O(1)	O(n)	O(log n)
put()	O(1)*	O(n)	O(log n)
remove()	O(1)	O(n)	O(log n)
containsKey()	O(1)	O(n)	O(log n)
iteration	O(capacity + size)	O(capacity + size)	O(capacity + size)

*put() — amortized O(1), since periodic resize = O(n)

Why O(1)?

1. Computing hash(key): O(1) — a few bitwise operations
2. Determining index: O(1) — (n-1) & hash
3. Array access: O(1) — direct access by index
4. Comparison in bucket: O(1) — on average 1-2 elements

Amortized Cost of put()

Resize is an O(n) operation, but occurs rarely (geometric progression):

• 16 → 32 → 64 → 128 → …
• Amortized cost: O(1)

Factors Affecting Real-World Speed

Factor	Impact
Cost of equals()	Heavy equals = large constant
Quality of hashCode()	Many collisions → O(log n)
Load Factor	Higher = more collisions
Key size	Long strings = slow hashCode/equals

Common Mistakes

1. Thinking O(1) = same time always — the constant depends on hashCode quality
2. Ignoring amortization — resize can cause latency spikes
3. Confusing with iteration — forEach = O(capacity + size), not O(1)

When NOT to Use HashMap

1. Predictable latency needed — resize causes spikes, consider LinkedHashMap with fixed capacity
2. Sorting needed — TreeMap O(log n)
3. Multi-threaded access — ConcurrentHashMap

🔴 Senior Level

Internal Breakdown

// get() internals:
// 1. hash(key):         ~3 cycles (XOR + shift)
// 2. index = (n-1)&hash: ~1 cycle
// 3. tab[index]:         ~1 cycle (L1 cache hit) or ~100 cycles (RAM miss)
// 4. equals():           from 1 cycle (reference) to O(k) (string comparison)

Real latency depends on cache locality:

• L1 hit: ~1 ns
• L2 hit: ~4 ns
• L3 hit: ~15 ns
• RAM miss: ~100 ns

Worst Case: When O(n) is Possible

In Java 8+, O(n) persists in three cases:

1. capacity < 64 — treeification doesn’t trigger, at 8+ collisions = O(n)
2. hashCode() returns a constant for all keys — all elements in one bucket
3. Java 7: without treeification, always O(n) on collisions

O(log n) — degradation limit on treeification.

Benchmark Data (Java 21, Intel i9)

Operation	Size	Average Latency	P99
get()	1K	5 ns	12 ns
get()	1M	8 ns	25 ns
put()	1K	8 ns	20 ns
put() (with resize)	1M→2M	150 ns	5000 ns
iteration	1M	200 µs	250 µs

ConcurrentHashMap vs HashMap

Metric	HashMap	ConcurrentHashMap
get (single thread)	5 ns	8 ns
get (8 threads)	N/A	60 ns
put (single thread)	8 ns	15 ns
put (8 threads)	N/A	120 ns

Edge Cases

1. hashCode() = constant: O(n) for Java 7, O(log n) for Java 8+
2. Huge capacity, few elements: iteration O(capacity) dominates
3. String keys with long values: equals() = O(k), where k is string length

Production Tuning

• Pre-set capacity eliminates O(n) resize
• Monitoring P99 latency reveals resize spikes
• JFR event jdk.HashMapResize (Java 17+) for profiling

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🎯 Interview Cheat Sheet

Must know:

• Average case: get/put/remove = O(1) via hash + indexing + equals
• Worst case Java 7: O(n) — linked list in bucket
• Worst case Java 8+: O(log n) — tree on collisions
• Iteration: O(capacity + size) — NOT O(1), traverses all buckets
• put() — amortized O(1): resize O(n) but occurs rarely (geometric progression)
• Real latency depends on cache locality: L1 hit ~1ns, RAM miss ~100ns

Common follow-up questions:

• Why O(1) and not O(log n)? — on average 1-2 elements in a bucket with a good hashCode
• What is amortized complexity? — individual resize is expensive, but on average over N insertions = O(1)
• When is O(n) possible in Java 8+? — capacity < 64, hashCode = constant, or Java 7
• ConcurrentHashMap overhead? — ~60% on single-thread, but the only correct option for multi-thread

Red flags (DO NOT say):

• “O(1) = same time always” — no, the constant depends on hashCode, equals, cache locality
• “Iteration = O(1)” — no, O(capacity + size)
• “HashMap is always faster than TreeMap” — no, TreeMap is O(log n) but gives sorting and predictable latency

Related topics:

• [[21. When Can Time Complexity Become O(n)]]
• [[22. How HashMap Works in a Multi-threaded Environment]]
• [[04. What is a Collision in HashMap]]