Advanced Collections

import org.apache.commons.collections4.Trie;
import org.apache.commons.collections4.trie.PatriciaTrie;
import java.util.SortedMap;

Trie<String, Integer> trie = new PatriciaTrie<>();

// Add key-value pairs
trie.put("cat", 1);
trie.put("car", 2);
trie.put("card", 3);
trie.put("care", 4);
trie.put("careful", 5);
trie.put("dog", 6);

// Standard map operations
Integer value = trie.get("car");                    // Returns 2
boolean hasKey = trie.containsKey("card");          // true
int size = trie.size();                             // 6

// Prefix-based operations
SortedMap<String, Integer> carPrefixed = trie.prefixMap("car");
// Returns: {car=2, card=3, care=4, careful=5}

SortedMap<String, Integer> cPrefixed = trie.prefixMap("c");
// Returns: {car=2, card=3, care=4, careful=5, cat=1}

// Empty prefix returns all entries
SortedMap<String, Integer> all = trie.prefixMap("");

import org.apache.commons.collections4.trie.PatriciaTrie;
import java.util.Map;

// Create for String keys
PatriciaTrie<String> dictionary = new PatriciaTrie<>();

// Add dictionary entries
dictionary.put("apple", "A red or green fruit");
dictionary.put("application", "A software program");
dictionary.put("apply", "To put to use");
dictionary.put("appreciate", "To value or understand");

// Autocomplete functionality
SortedMap<String, String> suggestions = dictionary.prefixMap("app");
System.out.println("Words starting with 'app':");
suggestions.forEach((word, definition) -> 
    System.out.println(word + ": " + definition));

// Create from existing map
Map<String, Integer> wordCounts = Map.of(
    "hello", 5,
    "world", 3,
    "help", 2,
    "helper", 1
);
PatriciaTrie<Integer> countTrie = new PatriciaTrie<>(wordCounts);

// Find all words with "hel" prefix
SortedMap<String, Integer> helWords = countTrie.prefixMap("hel");
// Returns: {hello=5, help=2, helper=1}

public class AutocompleteSystem {
    private final Trie<String, Integer> trie = new PatriciaTrie<>();
    
    public void addWord(String word, int frequency) {
        trie.put(word.toLowerCase(), frequency);
    }
    
    public List<String> getSuggestions(String prefix, int maxResults) {
        SortedMap<String, Integer> matches = trie.prefixMap(prefix.toLowerCase());
        
        return matches.entrySet().stream()
            .sorted(Map.Entry.<String, Integer>comparingByValue().reversed()) // Sort by frequency
            .limit(maxResults)
            .map(Map.Entry::getKey)
            .collect(Collectors.toList());
    }
    
    public boolean hasWord(String word) {
        return trie.containsKey(word.toLowerCase());
    }
    
    public void incrementFrequency(String word) {
        String key = word.toLowerCase();
        int currentFreq = trie.getOrDefault(key, 0);
        trie.put(key, currentFreq + 1);
    }
}

public class PhoneDirectory {
    private final Trie<String, Contact> byName = new PatriciaTrie<>();
    private final Trie<String, Contact> byPhone = new PatriciaTrie<>();
    
    public void addContact(Contact contact) {
        byName.put(contact.getName().toLowerCase(), contact);
        byPhone.put(contact.getPhone(), contact);
    }
    
    public List<Contact> searchByName(String namePrefix) {
        return new ArrayList<>(byName.prefixMap(namePrefix.toLowerCase()).values());
    }
    
    public List<Contact> searchByPhone(String phonePrefix) {
        return new ArrayList<>(byPhone.prefixMap(phonePrefix).values());
    }
    
    public Contact getExactMatch(String name) {
        return byName.get(name.toLowerCase());
    }
}

import org.apache.commons.collections4.bloomfilter.BloomFilter;
import org.apache.commons.collections4.bloomfilter.SimpleBloomFilter;
import org.apache.commons.collections4.bloomfilter.Shape;
import org.apache.commons.collections4.bloomfilter.Hasher;
import org.apache.commons.collections4.bloomfilter.EnhancedDoubleHasher;

// Create bloom filter shape (size and hash functions)
Shape shape = Shape.fromEstimates(1000, 0.01); // Expected elements: 1000, false positive rate: 1%

BloomFilter filter = new SimpleBloomFilter(shape);

// Add elements (using hashers)
Hasher hasher1 = new EnhancedDoubleHasher("element1".hashCode(), "element1".hashCode());
Hasher hasher2 = new EnhancedDoubleHasher("element2".hashCode(), "element2".hashCode());

filter.merge(hasher1);
filter.merge(hasher2);

// Test membership
boolean mayContain1 = filter.contains(hasher1);  // true (definitely in set)
Hasher hasher3 = new EnhancedDoubleHasher("element3".hashCode(), "element3".hashCode());
boolean mayContain3 = filter.contains(hasher3);  // false or true (maybe in set)

// Get filter statistics
int cardinality = filter.cardinality();          // Number of set bits
Shape filterShape = filter.getShape();           // Configuration details

import org.apache.commons.collections4.bloomfilter.CountingBloomFilter;

CountingBloomFilter countingFilter = new ArrayCountingBloomFilter(shape);

// Add elements
countingFilter.merge(hasher1);
countingFilter.merge(hasher1); // Add same element twice

// Remove elements  
boolean removed = countingFilter.remove(hasher1); // Decrements count
boolean removedAgain = countingFilter.remove(hasher1); // Decrements to zero

// Element no longer in filter
boolean stillThere = countingFilter.contains(hasher1); // false

public class CacheWithBloomFilter<K, V> {
    private final Map<K, V> cache = new HashMap<>();
    private final BloomFilter filter;
    private final Function<K, Hasher> hasherFunction;
    
    public CacheWithBloomFilter(Shape shape, Function<K, Hasher> hasherFunction) {
        this.filter = new SimpleBloomFilter(shape);
        this.hasherFunction = hasherFunction;
    }
    
    public void put(K key, V value) {
        cache.put(key, value);
        filter.merge(hasherFunction.apply(key));
    }
    
    public V get(K key) {
        // Fast negative check - if not in bloom filter, definitely not in cache
        if (!filter.contains(hasherFunction.apply(key))) {
            return null; // Definitely not in cache
        }
        
        // May or may not be in cache - check actual cache
        return cache.get(key);
    }
    
    public boolean mightContain(K key) {
        return filter.contains(hasherFunction.apply(key));
    }
}

import org.apache.commons.collections4.bloomfilter.ArrayCountingBloomFilter;
import org.apache.commons.collections4.bloomfilter.BitMapBloomFilter;
import org.apache.commons.collections4.bloomfilter.LayeredBloomFilter;

// Array-based counting bloom filter (supports removal)
ArrayCountingBloomFilter countingFilter = new ArrayCountingBloomFilter(shape);
countingFilter.merge(hasher1);
countingFilter.merge(hasher1); // Add twice
boolean removed = countingFilter.remove(hasher1); // Remove once

// Bitmap-based bloom filter for memory efficiency
BitMapBloomFilter bitMapFilter = new BitMapBloomFilter(shape);
bitMapFilter.merge(hasher1);
boolean contains = bitMapFilter.contains(hasher1); // Fast membership test

// Layered bloom filter for time-based data
LayeredBloomFilter layeredFilter = new LayeredBloomFilter(shape, 3); // 3 layers
layeredFilter.merge(hasher1);
layeredFilter.next(); // Advance to next layer
layeredFilter.merge(hasher2);

import org.apache.commons.collections4.bloomfilter.Shape;

// Create shape from estimates
Shape fromEstimates = Shape.fromEstimates(1000, 0.01); // 1000 items, 1% false positive rate

// Create shape from parameters
Shape fromParams = Shape.fromNP(1000, 10); // 1000 items, 10 hash functions

// Get shape properties
int numberOfBits = fromEstimates.getNumberOfBits();       // Total bits in filter
int numberOfHashFunctions = fromEstimates.getNumberOfHashFunctions(); // Hash functions used
double probability = fromEstimates.getProbability(500);   // False positive rate at 500 items

// Calculate optimal parameters for different scenarios
int optimalBits = Shape.calculateNumberOfBits(1000, 0.01);      // Bits needed
int optimalHashes = Shape.calculateNumberOfHashFunctions(1000, 9600); // Hash functions needed

import org.apache.commons.collections4.queue.CircularFifoQueue;

// Create with default size (32)
CircularFifoQueue<String> defaultQueue = new CircularFifoQueue<>();

// Create with specific size
CircularFifoQueue<Integer> numbers = new CircularFifoQueue<>(5);

// Add elements
numbers.offer(1);
numbers.offer(2);
numbers.offer(3);
numbers.offer(4);
numbers.offer(5);

System.out.println(numbers); // [1, 2, 3, 4, 5]

// Adding when full overwrites oldest
numbers.offer(6); // Overwrites 1
numbers.offer(7); // Overwrites 2

System.out.println(numbers); // [3, 4, 5, 6, 7]

// Queue properties
boolean isFull = numbers.isFull();           // true
int maxSize = numbers.maxSize();             // 5
int currentSize = numbers.size();            // 5

// Create from existing collection (takes only last N elements if oversized)
List<String> manyItems = Arrays.asList("a", "b", "c", "d", "e", "f", "g", "h");
CircularFifoQueue<String> fromList = new CircularFifoQueue<>(manyItems); // Takes last 32
CircularFifoQueue<String> fromListSized = new CircularFifoQueue<>(3, manyItems); // Takes "f", "g", "h"

import org.apache.commons.collections4.QueueUtils;
import org.apache.commons.collections4.Predicate;
import org.apache.commons.collections4.Transformer;
import java.util.LinkedList;
import java.util.Queue;

Queue<String> baseQueue = new LinkedList<>();

// Synchronized queue
Queue<String> syncQueue = QueueUtils.synchronizedQueue(baseQueue);

// Unmodifiable queue
baseQueue.offer("item1");
baseQueue.offer("item2");
Queue<String> readOnlyQueue = QueueUtils.unmodifiableQueue(baseQueue);

// Predicated queue (validates additions)
Predicate<String> nonEmpty = s -> s != null && !s.trim().isEmpty();
Queue<String> validatedQueue = QueueUtils.predicatedQueue(new LinkedList<>(), nonEmpty);

validatedQueue.offer("valid");     // Success
try {
    validatedQueue.offer("");      // Throws IllegalArgumentException
} catch (IllegalArgumentException e) {
    System.out.println("Empty string rejected");
}

// Transforming queue
Transformer<String, String> upperCase = String::toUpperCase;
Queue<String> transformingQueue = QueueUtils.transformingQueue(new LinkedList<>(), upperCase);
transformingQueue.offer("hello"); // Stored as "HELLO"

public class RecentItemsCache<T> {
    private final CircularFifoQueue<T> recentItems;
    private final Set<T> itemSet = new LinkedHashSet<>();
    
    public RecentItemsCache(int maxSize) {
        this.recentItems = new CircularFifoQueue<>(maxSize);
    }
    
    public void addItem(T item) {
        // Remove if already exists to avoid duplicates
        if (itemSet.contains(item)) {
            recentItems.remove(item);
            itemSet.remove(item);
        }
        
        // Add new item
        if (recentItems.isFull()) {
            T removed = recentItems.poll();
            itemSet.remove(removed);
        }
        
        recentItems.offer(item);
        itemSet.add(item);
    }
    
    public List<T> getRecentItems() {
        return new ArrayList<>(recentItems);
    }
    
    public boolean isRecent(T item) {
        return itemSet.contains(item);
    }
}

import org.apache.commons.collections4.OrderedIterator;
import org.apache.commons.collections4.iterators.ListIteratorWrapper;
import java.util.List;
import java.util.Arrays;

List<String> list = Arrays.asList("first", "second", "third", "fourth");
OrderedIterator<String> iterator = new ListIteratorWrapper<>(list.listIterator());

// Forward iteration
while (iterator.hasNext()) {
    String item = iterator.next();
    System.out.println("Forward: " + item);
}

// Backward iteration
while (iterator.hasPrevious()) {
    String item = iterator.previous();
    System.out.println("Backward: " + item);
}

import org.apache.commons.collections4.ResettableIterator;
import org.apache.commons.collections4.iterators.ArrayIterator;

String[] array = {"a", "b", "c", "d"};
ResettableIterator<String> resetIterator = new ArrayIterator<>(array);

// First iteration
while (resetIterator.hasNext()) {
    System.out.println("First pass: " + resetIterator.next());
}

// Reset and iterate again
resetIterator.reset();
while (resetIterator.hasNext()) {
    System.out.println("Second pass: " + resetIterator.next());
}

import org.apache.commons.collections4.MapIterator;
import org.apache.commons.collections4.map.LinkedMap;

LinkedMap<String, Integer> map = new LinkedMap<>();
map.put("apple", 5);
map.put("banana", 3);
map.put("cherry", 8);

MapIterator<String, Integer> mapIterator = map.mapIterator();
while (mapIterator.hasNext()) {
    String key = mapIterator.next();
    Integer value = mapIterator.getValue();
    
    // Modify value in-place
    mapIterator.setValue(value * 2);
    
    System.out.println(key + " -> " + mapIterator.getValue());
}

import org.apache.commons.collections4.IteratorUtils;
import java.util.Iterator;
import java.util.Arrays;

List<String> list1 = Arrays.asList("a", "b", "c");
List<String> list2 = Arrays.asList("d", "e", "f");
List<String> list3 = Arrays.asList("g", "h", "i");

// Chain multiple iterators
Iterator<String> chained = IteratorUtils.chainedIterator(
    list1.iterator(),
    list2.iterator(), 
    list3.iterator()
);

// Iterate through all elements in sequence
while (chained.hasNext()) {
    System.out.println(chained.next()); // a, b, c, d, e, f, g, h, i
}

import org.apache.commons.collections4.Predicate;

List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

// Filter even numbers
Predicate<Integer> evenPredicate = n -> n % 2 == 0;
Iterator<Integer> evenNumbers = IteratorUtils.filteredIterator(numbers.iterator(), evenPredicate);

System.out.println("Even numbers:");
while (evenNumbers.hasNext()) {
    System.out.println(evenNumbers.next()); // 2, 4, 6, 8, 10
}

import org.apache.commons.collections4.Transformer;

List<String> words = Arrays.asList("hello", "world", "java", "collections");

// Transform to uppercase
Transformer<String, String> upperCase = String::toUpperCase;
Iterator<String> upperCaseIterator = IteratorUtils.transformedIterator(words.iterator(), upperCase);

System.out.println("Uppercase words:");
while (upperCaseIterator.hasNext()) {
    System.out.println(upperCaseIterator.next()); // HELLO, WORLD, JAVA, COLLECTIONS
}

// Transform to word lengths
Transformer<String, Integer> lengthTransformer = String::length;
Iterator<Integer> lengthIterator = IteratorUtils.transformedIterator(words.iterator(), lengthTransformer);

import java.util.Comparator;

List<Integer> list1 = Arrays.asList(1, 4, 7, 10);  // Sorted
List<Integer> list2 = Arrays.asList(2, 5, 8, 11);  // Sorted
List<Integer> list3 = Arrays.asList(3, 6, 9, 12);  // Sorted

// Merge into single sorted sequence
Iterator<Integer> collated = IteratorUtils.collatingIterator(
    Comparator.naturalOrder(),
    list1.iterator(),
    list2.iterator(),
    list3.iterator()
);

System.out.println("Merged sorted sequence:");
while (collated.hasNext()) {
    System.out.println(collated.next()); // 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
}

// Trie operations are O(k) where k is key length, not collection size
Trie<String, Object> largeTrie = new PatriciaTrie<>();

// Adding 1M entries - each operation is O(key_length)
for (int i = 0; i < 1_000_000; i++) {
    largeTrie.put("key" + i, i);
}

// Prefix search is still O(prefix_length + result_size)
SortedMap<String, Object> results = largeTrie.prefixMap("key123"); // Fast even with 1M entries

// CircularFifoQueue offers O(1) operations
CircularFifoQueue<String> efficient = new CircularFifoQueue<>(1000);

// All operations are constant time
efficient.offer("item"); // O(1)
String item = efficient.poll(); // O(1)
boolean full = efficient.isFull(); // O(1)

// Much more efficient than removing from front of ArrayList
List<String> inefficient = new ArrayList<>();
// Adding to end: O(1), removing from front: O(n)

// Memory-efficient iteration over large collections
List<String> hugeList = generateHugeList(); // Millions of items

// Memory efficient - only holds current position
Iterator<String> memoryEfficient = hugeList.iterator();

// Less memory efficient - may hold references to filtered items
List<String> filtered = hugeList.stream()
    .filter(s -> s.startsWith("prefix"))
    .collect(Collectors.toList());

// Use iterator utilities for memory-efficient processing
Iterator<String> filteredIterator = IteratorUtils.filteredIterator(
    hugeList.iterator(),
    s -> s.startsWith("prefix")
);

// Use Trie for prefix-based operations
Trie<String, Object> autocomplete = new PatriciaTrie<>(); // Autocomplete, dictionaries

// Use Bloom Filter for membership pre-filtering  
BloomFilter membershipFilter = new SimpleBloomFilter(shape); // Cache layers, deduplication

// Use CircularFifoQueue for fixed-size buffers
CircularFifoQueue<LogEntry> logBuffer = new CircularFifoQueue<>(1000); // Recent items, logs

// Use specialized iterators for complex traversal
Iterator<String> complexTraversal = IteratorUtils.chainedIterator(
    IteratorUtils.filteredIterator(source1.iterator(), predicate1),
    IteratorUtils.transformedIterator(source2.iterator(), transformer)
);

// Safe trie operations
public List<String> safeGetSuggestions(Trie<String, Integer> trie, String prefix) {
    if (prefix == null || trie == null) {
        return Collections.emptyList();
    }
    
    try {
        SortedMap<String, Integer> matches = trie.prefixMap(prefix);
        return new ArrayList<>(matches.keySet());
    } catch (Exception e) {
        // Log error and return empty results
        return Collections.emptyList();
    }
}

// Safe queue operations
public boolean safeOffer(CircularFifoQueue<String> queue, String item) {
    if (queue == null || item == null) {
        return false;
    }
    
    return queue.offer(item); // Always succeeds for CircularFifoQueue
}