将 2GB 的大文件分割为 2048 个大小为 512KB 的小文件，采用流式读取方式处理，避免一次性加载整个文件导致内存溢出。
初始化一个长度为 2048 的哈希表数组，用于分别统计各个小文件中单词的出现频率。
利用多线程并行处理机制遍历所有 2048 个小文件，对每个文件中的单词执行哈希取模运算（int hash = Math.abs(word.hashCode() % hashTableSize)），并通过hashTables[hash].merge(word, 1, Integer::sum)的方式将单词频率累加到对应的哈希表中。
遍历这 2048 个哈希表，对每个哈希表中的单词按频率排序，将频率排名前 100 的单词存入一个小顶堆。
经过上述处理后，小顶堆中最终保留的 100 个单词即为整个文件中出现频率最高的前 100 个单词。

package com.example.bigfiletop100;import java.io.*;
import java.nio.charset.StandardCharsets;
import java.nio.file.*;
import java.util.*;
import java.util.concurrent.*;public class Top100Words {private static final int HASH_TABLE_SIZE = 2048;private static final int TOP_WORDS_COUNT = 100;public static void main(String[] args) throws IOException, InterruptedException {String largeFilePath = "C:\\Users\\亚洲\\IdeaProjects\\springboot-easyexcel-mybatisplus\\src\\main\\java\\com\\example\\bigfiletop100\\input.txt"; // 替换为大文件的路径String tempDirPath = "C:\\Users\\亚洲\\IdeaProjects\\springboot-easyexcel-mybatisplus\\src\\main\\java\\com\\example\\bigfiletop100\\temp_files"; // 替换为临时目录的路径Path tempDir = Paths.get(tempDirPath);if (!Files.exists(tempDir)) {Files.createDirectories(tempDir);}// 1. 分割大文件为512KB的小文件（改进版）splitFile(largeFilePath, tempDir.toString(), 512 * 1024);// 2. 初始化哈希表数组ConcurrentHashMap<Integer, Map<String, Integer>> hashTables = new ConcurrentHashMap<>();for (int i = 0; i < HASH_TABLE_SIZE; i++) {hashTables.put(i, new ConcurrentHashMap<>());}// 3. 并行遍历小文件，统计单词频率File[] smallFiles = new File(tempDir.toString()).listFiles((dir, name) -> name.endsWith(".txt"));if (smallFiles == null || smallFiles.length == 0) {System.err.println("No small files found.");return;}ExecutorService executor = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());CountDownLatch latch = new CountDownLatch(smallFiles.length);for (File smallFile : smallFiles) {executor.submit(() -> {try (BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(smallFile), StandardCharsets.UTF_8))) {String line;while ((line = reader.readLine()) != null) {String[] words = line.trim().split("\\s+");for (String word : words) {if (word.isEmpty()) continue;int hash = Math.abs(word.hashCode() % HASH_TABLE_SIZE);hashTables.get(hash).merge(word, 1, Integer::sum);}}} catch (IOException e) {System.err.println("Error reading file: " + smallFile.getName());e.printStackTrace();} finally {latch.countDown();}});}executor.shutdown();latch.await(); // 等待所有任务完成// 4. 合并哈希表，并把频率前100的单词存入小顶堆PriorityQueue<Map.Entry<String, Integer>> minHeap = new PriorityQueue<>(TOP_WORDS_COUNT,(o1, o2) -> o1.getValue().compareTo(o2.getValue()));hashTables.values().stream().filter(Objects::nonNull).flatMap(map -> map.entrySet().stream()).forEach(entry -> {if (minHeap.size() < TOP_WORDS_COUNT) {minHeap.offer(entry);} else if (entry.getValue() > minHeap.peek().getValue()) {minHeap.poll();minHeap.offer(entry);}});// 5. 输出结果System.out.println("Top " + TOP_WORDS_COUNT + " words:");List<Map.Entry<String, Integer>> topWords = new ArrayList<>(minHeap);topWords.sort((o1, o2) -> o2.getValue().compareTo(o1.getValue())); // 按降序排列topWords.forEach(entry -> System.out.println(entry.getKey() + ": " + entry.getValue()));// 清理临时文件和目录Arrays.stream(smallFiles).forEach(File::delete);Files.deleteIfExists(tempDir);}private static void splitFile(String largeFilePath, String tempDir, int size) throws IOException {try (BufferedInputStream bis = new BufferedInputStream(new FileInputStream(largeFilePath))) {byte[] buffer = new byte[size];int bytesRead;int fileCount = 0;while ((bytesRead = bis.read(buffer)) != -1) {File smallFile = new File(tempDir, "part-" + fileCount++ + ".txt");// 避免截断单词int actualBytesRead = bytesRead;if (bytesRead <= size && buffer[bytesRead - 1] != '\n') {// 查找最后一个空格或换行符作为分割点for (int i = bytesRead - 1; i >= 0; i--) {if (buffer[i] == ' ' || buffer[i] == '\n') {actualBytesRead = i + 1;bis.reset();bis.skip(actualBytesRead);break;}}}try (BufferedOutputStream bos = new BufferedOutputStream(new FileOutputStream(smallFile))) {bos.write(buffer, 0, actualBytesRead);}}}}
}