JDK17 解析：从特性实现到生产实践
一、版本演进与性能对比分析
架构演进对比图
JDK 17 (2021) JDK 8 (2014) 演进 演进 演进 演进 新特性 模块化系统
显式依赖声明 模块化JAR
JLink定制JRE 平台类加载器
模块感知 ZGC/Shenandoah
亚毫秒暂停 GraalVM支持
AOT编译 类路径 Classpath
所有类平等访问 胖JAR部署
全量依赖 扩展机制
ExtClassLoader GC策略
Parallel/CMS/G1
性能基准测试矩阵
| 测试场景 | JDK 8 (基准) | JDK 11 | JDK 17 | 技术原理 |
|---|---|---|---|---|
| Web应用QPS | 10,000 req/s | 11,500 req/s | 13,800 req/s | HTTP/2 + 向量化优化 |
| GC暂停时间 | 200ms (G1) | 150ms (G1) | <1ms (ZGC) | 着色指针 + 读屏障 |
| 启动时间 | 4.2s | 3.5s | 2.8s | 类数据共享 + 模块化 |
| 内存占用 | 512MB | 460MB | 380MB | 压缩类指针 + 字符串去重 |
| 序列化吞吐 | 100MB/s | 120MB/s | 180MB/s | Record类优化 |
二、语法特性深度剖析
🔍 1. 文本块：不只是语法糖，而是工程实践
java
// com.lee.demosql.SqlTemplateEngine
public class AdvancedTextBlocks {
// 1. 复杂SQL模板
public String buildAnalyticsQuery(String tenantId, LocalDate startDate) {
return """
WITH user_metrics AS (
SELECT
u.id,
u.username,
COUNT(DISTINCT o.id) as order_count,
SUM(oi.quantity * oi.unit_price) as total_spent,
AVG(o.delivery_days) as avg_delivery_time
FROM %s.users u
LEFT JOIN %s.orders o ON u.id = o.user_id
LEFT JOIN %s.order_items oi ON o.id = oi.order_id
WHERE u.registered_at >= '%tF'
AND u.tenant_id = '%s'
GROUP BY u.id, u.username
), rank_data AS (
SELECT
*,
RANK() OVER (ORDER BY total_spent DESC) as spend_rank,
PERCENT_RANK() OVER (ORDER BY order_count) as order_percentile
FROM user_metrics
)
SELECT
id,
username,
order_count,
total_spent,
avg_delivery_time,
CASE
WHEN spend_rank <= 10 THEN 'VIP'
WHEN order_percentile >= 0.8 THEN '高频'
ELSE '普通'
END as user_segment
FROM rank_data
ORDER BY total_spent DESC
LIMIT 100
""".formatted(tenantId, tenantId, tenantId, startDate, tenantId);
}
// 2. JSON/XML生成
public String generateOpenAPI() {
String apiVersion = "3.0.1";
String title = "用户服务API";
return """
openapi: %s
info:
title: %s
version: 1.0.0
description: |
这是用户管理服务的OpenAPI定义。
支持以下功能：
- 用户注册与登录
- 个人信息管理
- 权限控制
**注意**: 所有API都需要认证令牌。
contact:
name: 李技术团队
url: https://tech.lee.com
email: api@lee.com
license:
name: Apache 2.0
url: http://www.apache.org/licenses/LICENSE-2.0.html
servers:
- url: https://api.lee.com/v1
description: 生产环境
- url: https://staging-api.lee.com/v1
description: 预发环境
paths:
/users/{id}:
get:
summary: 获取用户详情
description: 根据用户ID获取详细信息
parameters:
- name: id
in: path
required: true
schema:
type: integer
format: int64
example: 123
responses:
'200':
description: 成功返回用户信息
content:
application/json:
schema:
$ref: '#/components/schemas/User'
components:
schemas:
User:
type: object
required:
- id
- username
- email
properties:
id:
type: integer
format: int64
readOnly: true
username:
type: string
minLength: 3
maxLength: 50
pattern: '^[a-zA-Z0-9_]+$'
email:
type: string
format: email
createdAt:
type: string
format: date-time
readOnly: true
""".formatted(apiVersion, title);
}
// 3. 特殊字符与对齐处理
public String generateMarkdownReport(List<User> users) {
return """
# 用户活跃度报告
生成时间: %tF %<tT
## 统计摘要
| 指标 | 数值 |
|------|------|
| 总用户数 | %d |
| 活跃用户 | %d |
| 平均登录频次 | %.2f 次/天 |
## 用户详情
```
%s
```
## 特殊说明
1. 数据包含以下特殊字符处理:
- 制表符: \t
- 换行符: \n
- 双引号: \"
- 反斜杠: \\
2. 对齐规则: 使用\s确保空格保留
---
*报告结束*
""".formatted(
LocalDateTime.now(),
users.size(),
users.stream().filter(User::isActive).count(),
users.stream().mapToDouble(User::getDailyLogin).average().orElse(0),
users.stream()
.map(u -> " - " + u.getUsername() + ": " + u.getEmail())
.collect(Collectors.joining("\n"))
);
}
}🏗️ 2. 模式匹配：类型安全的革命
java
// com.lee.patternmatching.TypeSystem
public class AdvancedPatternMatching {
// 1. 复杂业务逻辑简化
public String processPayment(Object paymentMethod, BigDecimal amount) {
return switch (paymentMethod) {
case CreditCard card when card.isValid() && card.getBalance().compareTo(amount) >= 0 -> {
card.charge(amount);
yield "信用卡支付成功: " + card.getMaskedNumber();
}
case Alipay alipay when alipay.isBound() -> {
String tradeNo = alipay.createTrade(amount);
yield "支付宝支付创建: " + tradeNo;
}
case WechatPay wechat when wechat.hasSufficientBalance(amount) -> {
wechat.deduct(amount);
yield "微信支付成功";
}
case BankTransfer transfer -> {
transfer.initiateTransfer(amount);
yield "银行转账已发起，请等待处理";
}
case null -> throw new IllegalArgumentException("支付方式不能为空");
default -> throw new UnsupportedOperationException(
"不支持的支付方式: " + paymentMethod.getClass().getSimpleName()
);
};
}
// 2. 嵌套模式匹配
public void processEvent(Object event) {
if (event instanceof OrderEvent(
OrderStatus status,
List<OrderItem> items,
User user
) && status == OrderStatus.PAID && !items.isEmpty()) {
// 直接使用解构后的变量
BigDecimal total = items.stream()
.map(item -> item.getPrice().multiply(BigDecimal.valueOf(item.getQuantity())))
.reduce(BigDecimal.ZERO, BigDecimal::add);
sendReceipt(user, items, total);
}
}
// 3. 守卫表达式增强
public Optional<String> validateConfig(Object config) {
return switch (config) {
case DatabaseConfig db when
db.getUrl() != null &&
db.getUsername() != null &&
db.getPassword() != null ->
Optional.of("数据库配置有效");
case RedisConfig redis when
redis.getNodes().size() >= 1 &&
redis.getMaxConnections() > 0 ->
Optional.of("Redis集群配置有效");
case CacheConfig cache when
cache.getType() == CacheType.REDIS ||
cache.getType() == CacheType.MEMCACHED ->
Optional.of("缓存配置有效");
default -> Optional.empty();
};
}
// 4. 记录类模式匹配
public record ApiResponse<T>(int code, String message, T data, long timestamp) {}
public void handleResponse(Object response) {
if (response instanceof ApiResponse<?> resp) {
System.out.printf("[%d] %s - %tF%n",
resp.code(), resp.message(), Instant.ofEpochMilli(resp.timestamp()));
// 泛型感知的模式匹配（预览特性）
if (response instanceof ApiResponse<String> stringResp) {
System.out.println("字符串响应: " + stringResp.data());
} else if (response instanceof ApiResponse<List<?>> listResp) {
System.out.println("列表响应大小: " + listResp.data().size());
}
}
}
}三、模块化系统深度实现
模块依赖关系架构
工具模块层 框架集成层 业务模块层 基础模块层 反射访问 反射访问 com.lee.common.utils
通用工具 com.lee.validation
验证框架 com.lee.cache.redis
Redis实现 spring.context
Spring核心 jakarta.persistence
JPA规范 spring.cache
缓存抽象 com.lee.bank.api
API定义 com.lee.bank.model
数据模型 com.lee.bank.service
业务服务 com.lee.bank.dao
数据访问 java.base
核心运行时 java.sql
数据库访问 java.net.http
HTTP客户端 java.logging
日志框架
模块化配置实践
java
// com.lee.finance.core 模块配置
module com.lee.finance.core {
// ============ 编译时依赖 ============
// 核心Java模块
requires java.base; // 隐式包含
requires java.sql; // 数据库访问
requires java.transaction.xa; // 分布式事务
requires java.management; // JMX监控
// 网络与序列化
requires java.net.http; // HTTP客户端 (JDK11+)
requires java.desktop; // 图像处理（验证码等）
// ============ 静态依赖（编译时）============
requires static lombok; // 代码生成
requires static org.mapstruct.processor;// 对象映射
requires static com.github.spotbugs; // 代码检查
// ============ 传递依赖 ============
requires transitive com.lee.common; // 公共组件
requires transitive com.fasterxml.jackson.databind; // JSON处理
requires transitive org.slf4j; // 日志门面
// ============ 服务提供 ============
provides com.lee.spi.Validator
with com.lee.finance.validators.AmountValidator,
com.lee.finance.validators.DateValidator;
provides com.lee.spi.Encoder
with com.lee.finance.encoders.AESEncoder,
com.lee.finance.encoders.RSAEncoder;
// ============ 对外API ============
// 公开给所有模块
exports com.lee.finance.api;
exports com.lee.finance.constants;
// 受限导出（仅特定模块可访问）
exports com.lee.finance.model to
com.lee.finance.web,
com.lee.finance.mobile,
com.lee.finance.batch;
exports com.lee.finance.service.spi to
com.lee.finance.integration;
// ============ 反射访问权限 ============
// Spring框架需要的反射访问
opens com.lee.finance.config to
spring.core,
spring.beans,
spring.context;
opens com.lee.finance.entities to
spring.data.jpa,
hibernate.core,
jakarta.persistence;
opens com.lee.finance.controller to
spring.web,
spring.boot.autoconfigure;
// 测试框架需要的反射访问
opens com.lee.finance.service.impl to
junit,
mockito.core;
// ============ 资源包导出 ============
opens com.lee.finance.resources;
// ============ 服务使用声明 ============
uses com.lee.spi.CacheProvider;
uses com.lee.spi.MessageQueue;
uses javax.sql.DataSource; // 数据库连接池SPI
}模块化构建与部署
java
// com.lee.build.ModuleBuildTools
public class ModuleBuildTools {
/**
* 模块化构建配置示例
* 使用Maven/Gradle构建模块化应用
*/
// Maven多模块配置示例
/*
<project>
<modelVersion>4.0.0</modelVersion>
<groupId>com.lee</groupId>
<artifactId>finance-platform</artifactId>
<version>1.0.0</version>
<packaging>pom</packaging>
<modules>
<module>finance-core</module>
<module>finance-web</module>
<module>finance-batch</module>
</modules>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.11.0</version>
<configuration>
<release>17</release>
<compilerArgs>
<arg>--module-path</arg>
<arg>${project.build.directory}/modules</arg>
</compilerArgs>
</configuration>
</plugin>
</plugins>
</build>
</project>
*/
// Gradle模块化配置示例
/*
// settings.gradle.kts
rootProject.name = "finance-platform"
include(":finance-core")
include(":finance-web")
include(":finance-batch")
// finance-core/build.gradle.kts
plugins {
`java-library`
`java-module`
}
java {
modularity.inferModulePath.set(true)
toolchain {
languageVersion.set(JavaLanguageVersion.of(17))
}
}
dependencies {
implementation(project(":finance-common"))
implementation("org.springframework:spring-context:6.0.0")
// 模块化依赖声明
compileOnly("org.projectlombok:lombok:1.18.24")
annotationProcessor("org.projectlombok:lombok:1.18.24")
}
*/
}
bash
#!/bin/bash
# build-modules.sh - 模块化构建脚本
# 1. 编译所有模块
javac -d out \
--module-source-path src \
--module com.lee.finance.core,com.lee.finance.web
# 2. 打包模块化JAR
jar --create --file=lib/finance-core.jar \
--module-version=1.0.0 \
-C out/com.lee.finance.core .
# 3. 创建定制化JRE（最小运行时）
jlink \
--module-path $JAVA_HOME/jmods:lib \
--add-modules com.lee.finance.core,java.sql,java.net.http \
--strip-debug \
--compress=2 \
--no-header-files \
--no-man-pages \
--output finance-runtime
# 4. 生成模块依赖图
jdeps --dot-output=deps-dot \
--module-path lib \
--module com.lee.finance.core
dot -Tpng deps-dot/com.lee.finance.core.dot -o deps-graph.png
# 5. 运行模块化应用
./finance-runtime/bin/java \
--module-path lib:finance-web.jar \
-m com.lee.finance.web/com.lee.finance.web.Main四、性能探讨
📈 1. 简单的性能测试
xml
// pom.xml 依赖配置
<dependencies>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.openjdk.jmh</groupId>
<artifactId>jmh-core</artifactId>
</dependency>
<dependency>
<groupId>org.openjdk.jmh</groupId>
<artifactId>jmh-generator-annprocess</artifactId>
<scope>provided</scope>
</dependency>
</dependencies>
java
// src/main/java/com/lee/benchmark/UserData.java
package com.lee.benchmark;
import lombok.*;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
public class UserData {
// 用户状态枚举
public enum UserStatus {
ACTIVE, INACTIVE, SUSPENDED, DELETED, PENDING
}
// 传统POJO类 - 使用Lombok Builder
@Getter
@ToString
@EqualsAndHashCode
@AllArgsConstructor(access = AccessLevel.PRIVATE)
@Builder(toBuilder = true)
public static class TraditionalUser implements Serializable {
private String name;
private String email;
private UserStatus status;
private int age;
private double score;
}
// Record类 - JDK 16+ (Lombok不支持简化Record，保持原样)
public record UserRecord(
String name,
String email,
UserStatus status,
int age,
double score
) implements Serializable {
// Record自动生成equals, hashCode, toString等方法
}
// Vip用户类 - 使用Lombok
@Getter
@ToString
@EqualsAndHashCode
@AllArgsConstructor(access = AccessLevel.PRIVATE)
@Builder(toBuilder = true)
public static class VipUser implements Serializable {
private String name;
private int level;
private int points;
private UserStatus status;
}
// 密封接口 - 用于测试密封类（Sealed Classes）
public sealed interface Shape permits Circle, Rectangle {
double area();
double perimeter();
}
// 圆形 - 密封类的子类，使用Lombok
@Getter
@ToString
@EqualsAndHashCode
@AllArgsConstructor
public static final class Circle implements Shape {
private final double radius;
@Override
public double area() {
return Math.PI * radius * radius;
}
@Override
public double perimeter() {
return 2 * Math.PI * radius;
}
}
// 矩形 - 密封类的子类，使用Lombok
@Getter
@ToString
@EqualsAndHashCode
@AllArgsConstructor
public static final class Rectangle implements Shape {
private final double width;
private final double height;
@Override
public double area() {
return width * height;
}
@Override
public double perimeter() {
return 2 * (width + height);
}
}
// 辅助类：配置类
@Getter
@Setter
@NoArgsConstructor
@AllArgsConstructor
@Builder
public static class Config {
private int userCount;
private int vipPercentage;
private double minScore;
private double maxScore;
private int minAge;
private int maxAge;
}
// 辅助类：统计信息
@Getter
@Setter
@NoArgsConstructor
@AllArgsConstructor
@Builder
@ToString
public static class Stats {
private int totalUsers;
private int activeUsers;
private int vipUsers;
private double averageScore;
private double averageAge;
}
// 辅助方法：生成随机形状
public static Shape createRandomShape(Random random) {
if (random.nextBoolean()) {
return new Circle(random.nextDouble() * 10);
} else {
return new Rectangle(random.nextDouble() * 10, random.nextDouble() * 10);
}
}
public static List<UserRecord> generateUserRecords(int count, Config config) {
List<UserRecord> users = new ArrayList<>(count);
Random random = new Random(42);
for (int i = 0; i < count; i++) {
UserRecord user = new UserRecord(
"user" + i,
"user" + i + "@example.com",
UserStatus.values()[i % UserStatus.values().length],
config.getMinAge() + random.nextInt(config.getMaxAge() - config.getMinAge()),
config.getMinScore() + random.nextDouble() * (config.getMaxScore() - config.getMinScore())
);
users.add(user);
}
return users;
}
// 辅助方法：批量生成传统用户数据
public static List<TraditionalUser> generateTraditionalUsers(int count) {
return generateTraditionalUsers(count, Config.builder()
.userCount(count)
.minScore(0.0)
.maxScore(100.0)
.minAge(18)
.maxAge(80)
.vipPercentage(20)
.build());
}
public static List<TraditionalUser> generateTraditionalUsers(int count, Config config) {
List<TraditionalUser> users = new ArrayList<>(count);
Random random = new Random(42);
for (int i = 0; i < count; i++) {
TraditionalUser user = TraditionalUser.builder()
.name("user" + i)
.email("user" + i + "@example.com")
.status(UserStatus.values()[i % UserStatus.values().length])
.age(config.getMinAge() + random.nextInt(config.getMaxAge() - config.getMinAge()))
.score(config.getMinScore() + random.nextDouble() * (config.getMaxScore() - config.getMinScore()))
.build();
users.add(user);
}
return users;
}
// 辅助方法：生成VIP用户列表
public static List<VipUser> generateVipUsers(int count) {
List<VipUser> users = new ArrayList<>(count);
Random random = new Random(42);
for (int i = 0; i < count; i++) {
VipUser user = VipUser.builder()
.name("vip-" + i)
.level(random.nextInt(5) + 1)
.points(random.nextInt(10000))
.status(UserStatus.ACTIVE)
.build();
users.add(user);
}
return users;
}
// 辅助方法：生成混合列表
public static List<Object> generateMixedList(int count) {
List<Object> mixedList = new ArrayList<>(count * 2);
Random random = new Random(42);
List<TraditionalUser> traditionalUsers = generateTraditionalUsers(count);
List<VipUser> vipUsers = generateVipUsers(count / 5);
for (int i = 0; i < count; i++) {
mixedList.add(traditionalUsers.get(i));
// 添加一些字符串和整数
if (i % 3 == 0) {
mixedList.add("String-" + i);
} else if (i % 3 == 1) {
mixedList.add(i);
}
// 添加VipUser
if (i % 5 == 0 && i/5 < vipUsers.size()) {
mixedList.add(vipUsers.get(i/5));
}
}
return mixedList;
}
}
java
// src/main/java/com/lee/benchmark/Jdk17PerformanceBenchmark.java
package com.lee.benchmark;
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.infra.Blackhole;
import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.stream.Collectors;
@State(Scope.Benchmark)
@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.SECONDS)
@Warmup(iterations = 3, time = 1)
@Measurement(iterations = 5, time = 2)
@Fork(value = 2, jvmArgs = {"-Xms2g", "-Xmx2g", "--enable-preview"})
@Threads(4)
public class Jdk17PerformanceBenchmark {
private List<UserData.TraditionalUser> traditionalUsers;
private List<UserData.UserRecord> recordUsers;
private List<UserData.VipUser> vipUsers;
private List<Object> mixedList;
private ExecutorService executorService;
private UserData.Config config;
@Setup
public void setup() {
System.out.println("开始初始化测试数据...");
config = UserData.Config.builder()
.userCount(10000)
.minScore(0.0)
.maxScore(100.0)
.minAge(18)
.maxAge(60)
.vipPercentage(20)
.build();
// 使用辅助方法生成数据
traditionalUsers = UserData.generateTraditionalUsers(config.getUserCount(), config);
recordUsers = UserData.generateUserRecords(config.getUserCount(), config);
vipUsers = UserData.generateVipUsers(config.getUserCount() / 5);
mixedList = UserData.generateMixedList(config.getUserCount());
// 创建线程池
executorService = Executors.newFixedThreadPool(
Math.max(4, Runtime.getRuntime().availableProcessors() * 2)
);
System.out.println("测试数据初始化完成: " + config.getUserCount() + " 条记录");
}
@TearDown
public void tearDown() throws Exception {
if (executorService != null) {
executorService.shutdown();
if (!executorService.awaitTermination(5, TimeUnit.SECONDS)) {
executorService.shutdownNow();
}
}
System.out.println("资源清理完成");
}
// 测试1: Record类 vs 传统POJO 创建性能
@Benchmark
public void testRecordCreation(Blackhole bh) {
Random random = new Random(42);
for (int i = 0; i < 1000; i++) {
UserData.UserRecord record = new UserData.UserRecord(
"test" + i,
"test" + i + "@test.com",
UserData.UserStatus.ACTIVE,
20 + random.nextInt(40),
50 + random.nextDouble() * 50
);
bh.consume(record);
}
}
@Benchmark
public void testTraditionalCreation(Blackhole bh) {
Random random = new Random(42);
for (int i = 0; i < 1000; i++) {
UserData.TraditionalUser user = UserData.TraditionalUser.builder()
.name("test" + i)
.email("test" + i + "@test.com")
.status(UserData.UserStatus.ACTIVE)
.age(20 + random.nextInt(40))
.score(50 + random.nextDouble() * 50)
.build();
bh.consume(user);
}
}
// 测试2: Record类 vs 传统POJO 哈希和相等性能
@Benchmark
public void testRecordHashEquals(Blackhole bh) {
Set<UserData.UserRecord> set = new HashSet<>();
for (UserData.UserRecord record : recordUsers.subList(0, 1000)) {
set.add(record);
bh.consume(set.contains(record));
}
bh.consume(set.size());
}
@Benchmark
public void testTraditionalHashEquals(Blackhole bh) {
Set<UserData.TraditionalUser> set = new HashSet<>();
for (UserData.TraditionalUser user : traditionalUsers.subList(0, 1000)) {
set.add(user);
bh.consume(set.contains(user));
}
bh.consume(set.size());
}
// 测试3: 文本块性能测试
@Benchmark
public void testTextBlockPerformance(Blackhole bh) {
StringBuilder result = new StringBuilder();
for (int i = 0; i < 100; i++) {
String json = """
{
"user": {
"id": %d,
"name": "用户%s",
"email": "user%s@example.com",
"status": "%s",
"age": %d,
"score": %.2f
}
}
""".formatted(
i,
i,
i,
UserData.UserStatus.ACTIVE,
20 + i % 40,
50.0 + i % 50.0
);
result.append(json);
bh.consume(json);
}
bh.consume(result.toString());
}
// 测试4: 传统字符串拼接性能对比
@Benchmark
public void testTraditionalStringPerformance(Blackhole bh) {
StringBuilder result = new StringBuilder();
for (int i = 0; i < 100; i++) {
String json = "{\n" +
" \"user\": {\n" +
" \"id\": " + i + ",\n" +
" \"name\": \"用户" + i + "\",\n" +
" \"email\": \"user" + i + "@example.com\",\n" +
" \"status\": \"" + UserData.UserStatus.ACTIVE + "\",\n" +
" \"age\": " + (20 + i % 40) + ",\n" +
" \"score\": " + String.format("%.2f", 50.0 + i % 50.0) + "\n" +
" }\n" +
"}";
result.append(json);
bh.consume(json);
}
bh.consume(result.toString());
}
// 测试5: 模式匹配性能
@Benchmark
public void testPatternMatching(Blackhole bh) {
int stringCount = 0;
int intCount = 0;
int userCount = 0;
int vipCount = 0;
for (Object item : mixedList.subList(0, 1000)) {
if (item instanceof String s && s.startsWith("String-")) {
stringCount++;
bh.consume(s.length());
} else if (item instanceof Integer i && i > 500) {
intCount++;
bh.consume(i * 2);
} else if (item instanceof UserData.TraditionalUser user) {
userCount++;
bh.consume(user.getName());
} else if (item instanceof UserData.VipUser vip && vip.getLevel() > 3) {
vipCount++;
bh.consume(vip.getPoints());
}
}
bh.consume(stringCount);
bh.consume(intCount);
bh.consume(userCount);
bh.consume(vipCount);
}
// 测试6: 线程池性能测试
@Benchmark
@Threads(1)
public void testThreadPoolPerformance(Blackhole bh) throws Exception {
int taskCount = 100;
List<Future<String>> futures = new ArrayList<>(taskCount);
for (int i = 0; i < taskCount; i++) {
final int taskId = i;
Future<String> future = executorService.submit(() -> {
Thread.sleep(1);
return "Task-" + taskId + "-" +
(taskId * taskId) + "-" +
System.currentTimeMillis();
});
futures.add(future);
}
for (Future<String> future : futures) {
String result = future.get();
bh.consume(result);
}
}
// 测试7: CompletableFuture性能测试
@Benchmark
@Threads(1)
public void testCompletableFuturePerformance(Blackhole bh) throws Exception {
int taskCount = 100;
List<CompletableFuture<String>> futures = new ArrayList<>(taskCount);
for (int i = 0; i < taskCount; i++) {
final int taskId = i;
CompletableFuture<String> future = CompletableFuture.supplyAsync(() -> {
try {
Thread.sleep(1);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
return "Task-" + taskId + "-" +
(taskId * taskId) + "-" +
System.currentTimeMillis();
}, executorService);
futures.add(future);
}
CompletableFuture<Void> allFutures = CompletableFuture.allOf(
futures.toArray(new CompletableFuture[0])
);
allFutures.join();
for (CompletableFuture<String> future : futures) {
String result = future.get();
bh.consume(result);
}
}
// 测试8: Switch表达式性能
@Benchmark
public void testSwitchExpression(Blackhole bh) {
int activeCount = 0;
int inactiveCount = 0;
int otherCount = 0;
for (UserData.UserRecord user : recordUsers.subList(0, 1000)) {
String statusDesc = switch (user.status()) {
case ACTIVE -> {
activeCount++;
yield "活跃用户";
}
case INACTIVE -> {
inactiveCount++;
yield "非活跃用户";
}
case SUSPENDED, DELETED, PENDING -> {
otherCount++;
yield "其他状态";
}
};
bh.consume(statusDesc);
}
bh.consume(activeCount);
bh.consume(inactiveCount);
bh.consume(otherCount);
}
// 测试9: Stream API 性能优化
@Benchmark
public void testStreamPerformance(Blackhole bh) {
// 使用Stream处理数据
double averageScore = recordUsers.stream()
.limit(1000)
.mapToDouble(UserData.UserRecord::score)
.average()
.orElse(0.0);
List<String> activeUserEmails = recordUsers.stream()
.limit(1000)
.filter(user -> user.status() == UserData.UserStatus.ACTIVE)
.map(UserData.UserRecord::email)
.sorted()
.toList();
Map<UserData.UserStatus, Long> statusCount = recordUsers.stream()
.limit(1000)
.collect(Collectors.groupingBy(
UserData.UserRecord::status,
Collectors.counting()
));
bh.consume(averageScore);
bh.consume(activeUserEmails);
bh.consume(statusCount);
}
// 测试10: 密封类性能测试
@Benchmark
public void testSealedClasses(Blackhole bh) {
int shapeCount = 0;
int circleCount = 0;
int rectangleCount = 0;
List<UserData.Shape> shapes = new ArrayList<>();
Random random = new Random(42);
for (int i = 0; i < 1000; i++) {
shapes.add(UserData.createRandomShape(random));
}
for (UserData.Shape shape : shapes) {
shapeCount++;
if (shape instanceof UserData.Circle circle) {
circleCount++;
bh.consume(circle.area());
bh.consume(circle.perimeter());
} else if (shape instanceof UserData.Rectangle rectangle) {
rectangleCount++;
bh.consume(rectangle.area());
bh.consume(rectangle.perimeter());
}
}
bh.consume(shapeCount);
bh.consume(circleCount);
bh.consume(rectangleCount);
}
// 测试11: Record模式匹配
@Benchmark
public void testRecordPatternMatching(Blackhole bh) {
int highScoreCount = 0;
int activeHighScoreCount = 0;
int vipHighScoreCount = 0;
// 处理Record用户
for (int i = 0; i < 1000; i++) {
UserData.UserRecord user = recordUsers.get(i);
if (user.score() > 80 && user.status() == UserData.UserStatus.ACTIVE) {
highScoreCount++;
activeHighScoreCount++;
bh.consume("Active High Score: " + user.name() + " - " + user.score());
} else if (user.score() > 80) {
highScoreCount++;
bh.consume("High Score: " + user.name() + " - " + user.score());
}
}
// 处理VIP用户
for (int i = 0; i < 200 && i < vipUsers.size(); i++) {
UserData.VipUser vip = vipUsers.get(i);
if (vip.getPoints() > 5000) {
vipHighScoreCount++;
bh.consume("VIP High Points: " + vip.getName() + " - " + vip.getPoints());
}
}
bh.consume(highScoreCount);
bh.consume(activeHighScoreCount);
bh.consume(vipHighScoreCount);
}
// 测试12: 并行流性能测试
@Benchmark
public void testParallelStreamPerformance(Blackhole bh) {
// 并行流处理
double parallelAverageScore = recordUsers.stream()
.parallel()
.limit(1000)
.mapToDouble(UserData.UserRecord::score)
.average()
.orElse(0.0);
List<String> parallelActiveUserNames = recordUsers.stream()
.parallel()
.limit(1000)
.filter(user -> user.status() == UserData.UserStatus.ACTIVE && user.score() > 60)
.map(UserData.UserRecord::name)
.sorted()
.toList();
// 顺序流处理（对比）
double sequentialAverageScore = recordUsers.stream()
.sequential()
.limit(1000)
.mapToDouble(UserData.UserRecord::score)
.average()
.orElse(0.0);
bh.consume(parallelAverageScore);
bh.consume(parallelActiveUserNames);
bh.consume(sequentialAverageScore);
}
// 测试13: Optional性能测试
@Benchmark
public void testOptionalPerformance(Blackhole bh) {
AtomicInteger presentCount = new AtomicInteger();
AtomicInteger absentCount = new AtomicInteger();
for (int i = 0; i < 1000; i++) {
Optional<UserData.UserRecord> optionalUser = i % 3 == 0 ?
Optional.empty() :
Optional.of(recordUsers.get(i % recordUsers.size()));
optionalUser.ifPresentOrElse(
user -> {
presentCount.incrementAndGet();
bh.consume(user.name());
},
() -> {
absentCount.incrementAndGet();
bh.consume("No user found");
}
);
}
bh.consume(presentCount.get());
bh.consume(absentCount.get());
}
// 测试14: 方法引用性能测试
@Benchmark
public void testMethodReferencePerformance(Blackhole bh) {
// 使用方法引用
List<String> names = recordUsers.stream()
.limit(1000)
.map(UserData.UserRecord::name)
.filter(name -> name.length() > 5)
.map(String::toUpperCase)
.toList();
// 使用Lambda表达式（对比）
List<String> emails = recordUsers.stream()
.limit(1000)
.map(user -> user.email())
.filter(email -> email.contains("@"))
.map(email -> email.toLowerCase())
.toList();
bh.consume(names);
bh.consume(emails);
}
}
java
// src/main/java/com/lee/benchmark/RunBenchmark.java
package com.lee.benchmark;
import org.openjdk.jmh.runner.Runner;
import org.openjdk.jmh.runner.RunnerException;
import org.openjdk.jmh.runner.options.Options;
import org.openjdk.jmh.runner.options.OptionsBuilder;
public class RunBenchmark {
public static void main(String[] args) throws RunnerException {
Options options = new OptionsBuilder()
.include(Jdk17PerformanceBenchmark.class.getSimpleName())
.exclude("testTraditionalThreads") // 排除某些测试
.warmupIterations(2)
.measurementIterations(3)
.forks(1)
.threads(2)
.build();
new Runner(options).run();
}
}
text
# 测试宿主机: CPU: AMD Ryzen 7、内存: 31940 MB
Result "com.lee.benchmark.Jdk17PerformanceBenchmark.testTraditionalStringPerformance":
23709.348 ±(99.9%) 13171.489 ops/s [Average]
(min, avg, max) = (23206.763, 23709.348, 24536.663), stdev = 721.974
CI (99.9%): [10537.859, 36880.837] (assumes normal distribution)
# Run complete. Total time: 00:02:17
REMEMBER: The numbers below are just data. To gain reusable insights, you need to follow up on
why the numbers are the way they are. Use profilers (see -prof, -lprof), design factorial
experiments, perform baseline and negative tests that provide experimental control, make sure
the benchmarking environment is safe on JVM/OS/HW level, ask for reviews from the domain experts.
Do not assume the numbers tell you what you want them to tell.
NOTE: Current JVM experimentally supports Compiler Blackholes, and they are in use. Please exercise
extra caution when trusting the results, look into the generated code to check the benchmark still
works, and factor in a small probability of new VM bugs. Additionally, while comparisons between
different JVMs are already problematic, the performance difference caused by different Blackhole
modes can be very significant. Please make sure you use the consistent Blackhole mode for comparisons.
Benchmark Mode Cnt Score Error Units
Jdk17PerformanceBenchmark.testCompletableFuturePerformance thrpt 3 228.787 ± 59.312 ops/s
Jdk17PerformanceBenchmark.testMethodReferencePerformance thrpt 3 29128.912 ± 29344.912 ops/s
Jdk17PerformanceBenchmark.testOptionalPerformance thrpt 3 552546.892 ± 370788.668 ops/s
Jdk17PerformanceBenchmark.testParallelStreamPerformance thrpt 3 28726.746 ± 38811.960 ops/s
Jdk17PerformanceBenchmark.testPatternMatching thrpt 3 1171067.834 ± 281961.776 ops/s
Jdk17PerformanceBenchmark.testRecordCreation thrpt 3 70106.085 ± 104675.086 ops/s
Jdk17PerformanceBenchmark.testRecordHashEquals thrpt 3 76635.554 ± 13087.725 ops/s
Jdk17PerformanceBenchmark.testRecordPatternMatching thrpt 3 71279.741 ± 43437.196 ops/s
Jdk17PerformanceBenchmark.testSealedClasses thrpt 3 112467.618 ± 79999.561 ops/s
Jdk17PerformanceBenchmark.testStreamPerformance thrpt 3 21633.325 ± 57607.642 ops/s
Jdk17PerformanceBenchmark.testSwitchExpression thrpt 3 899835.565 ± 3818224.217 ops/s
Jdk17PerformanceBenchmark.testTextBlockPerformance thrpt 3 13966.466 ± 28706.663 ops/s
Jdk17PerformanceBenchmark.testThreadPoolPerformance thrpt 3 223.331 ± 72.352 ops/s
Jdk17PerformanceBenchmark.testTraditionalCreation thrpt 3 81143.699 ± 35156.088 ops/s
Jdk17PerformanceBenchmark.testTraditionalHashEquals thrpt 3 50258.543 ± 216417.654 ops/s
Jdk17PerformanceBenchmark.testTraditionalStringPerformance thrpt 3 23709.348 ± 13171.489 ops/s五、升级小建议
🎯 项目类型与JDK选择
| 项目特征 | JDK 8 | JDK 11 | JDK 17 | 技术依据 |
|---|---|---|---|---|
| 新启动项目 | ❌ 不推荐 | ⚠️ 可考虑 | ✅ 强烈推荐 | Spring 6+强制要求，长期支持 |
| 微服务架构 | ❌ 不推荐 | ⚠️ 过渡方案 | ✅ 最佳选择 | 容器友好，启动快，内存占用低 |
| 大数据处理 | ⚠️ 兼容模式 | ✅ 稳定选择 | ✅ 性能优先 | 向量化API，ZGC大堆支持 |
| 金融交易系统 | ⚠️ 风险较高 | ✅ 生产验证 | ✅ 低延迟优化 | ZGC亚毫秒暂停，记录类不变性 |
| 遗留系统维护 | ✅ 保持稳定 | ⚠️ 逐步升级 | ⚠️ 评估成本 | 依赖兼容性，团队技能储备 |
⚠️ 老项目升级风险提示
对于已有代码，升级JDK版本需谨慎（升级有风险。。）
-
依赖兼容性风险
- 第三方库极有可能不支持JDK17模块系统
- 反射大量使用的框架需要调整
- 内部API调用需要替换
-
构建工具链更新
- Maven/Gradle插件版本兼容性
- IDE需要同步升级支持
- CI/CD 需要适配
-
团队技能转型
- 模块化概念
- 新特性需要编码规范更新
- 性能调优策略变化