Advanced HashMap, TreeMap & LinkedHashMap

Go beyond put and get : count and group in one line with merge / computeIfAbsent , navigate sorted keys with TreeMap , build an LRU cache from LinkedHashMap , and update safely across threads with ConcurrentHashMap .

Learn Advanced HashMap, TreeMap & LinkedHashMap in our free Java course — a beginner-friendly interactive lesson with worked examples, a practice exercise…

Part of the free Java course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

You should already be comfortable with the Collections internals and the hashCode() / equals() contract. This lesson assumes you can create a basic and iterate it — here you'll learn the methods that make Maps genuinely powerful.

Think of the three core Map types as three ways of organising a filing cabinet:

The modern methods — merge , computeIfAbsent , getOrDefault — are like a clerk who handles "add to the folder, or start a new one if it doesn't exist" in a single instruction, instead of you checking the drawer first every time.

The old way to count things was a three-line dance: get the value, test for null , then put back the incremented number. Java 8 collapsed all of that into a handful of methods. Learn these five and most of your Map code becomes one line.

Fill in the blanks so merge counts each word and getOrDefault reads a count safely. The expected output is in the comments — match it exactly.

A plain HashMap gives you no order at all — iteration can appear in any sequence and may change between runs. When order matters, pick a different implementation:

Iterates in insertion order by default. Pass accessOrder=true to instead order by "most recently used" — the basis of an LRU cache.

Keeps keys sorted (natural order or a Comparator ) and adds navigation: floorKey , ceilingKey , subMap , firstKey , lastKey .

A tiny, very fast map whose keys are an enum . Backed by an array, it iterates in the enum's declaration order. Use it whenever your keys are an enum.

TreeMap navigation, in plain English: floorKey(x) = the largest key ≤ x ; ceilingKey(x) = the smallest key ≥ x ; subMap(from, to) = a live view of keys in the range [from, to) (from inclusive, to exclusive). These turn "find the price tier for a spend" into a single call.

Use TreeMap navigation to find which discount tier a spend qualifies for, then show every tier from 100 upward. Decide carefully between floorKey and ceilingKey .

Two production patterns pull all of this together.

LRU cache. A cache with a size limit must drop something when it fills up; "least recently used" is the classic policy. Construct a LinkedHashMap with access order on, then override removeEldestEntry to return true once the size passes your capacity. Because access order moves every touched entry to the end, the eldest entry is always the least-recently-used one — eviction is automatic.

Thread safety. A normal HashMap is corrupted by concurrent writes. ConcurrentHashMap lets many threads read and write at once, and its merge , compute , and computeIfAbsent methods run atomically — so incrementing a counter from many threads is correct without you writing any locks.

Support is faded now: only an outline is given. Build a grouping each person under their city using computeIfAbsent , then print it. The expected output is in the comments.

💡 One-line counter: map.merge(key, 1, Integer::sum) is the idiomatic way to count occurrences.

💡 get-or-create: builds a multi-map without any null checks.

💡 Prefer EnumMap for enum keys: it's faster and uses less memory than a HashMap, and iterates in enum order for free.

💡 Need both order kinds? Sorted snapshot of a HashMap: wrap it once — new TreeMap (hashMap) — to print or scan in sorted order without changing the original.

You can now count and group in one line with merge and computeIfAbsent , pick the right Map for the ordering you need, navigate sorted keys with TreeMap , build an LRU cache from LinkedHashMap , and update maps safely across threads with ConcurrentHashMap .

Next: Multithreading — create and manage threads, and use thread pools with ExecutorService .

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