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占位符解析

• 问题背景：在多数据源切换场景中，environment.resolvePlaceholders(value) 用于解析配置文件中的占位符（如 ${db.master}）。如果频繁调用此方法，尤其是在高并发场景下，重复解析相同的占位符字符串会带来不必要的性能开销。通过对比优化前后的实现，验证缓存占位符解析结果对性能的提升。

1、原始实现：无缓存解析

以下代码每次调用时直接解析占位符：

public class OriginalDataSourceResolver {private Environment environment;public String resolve(String dataSourceId) {// 每次调用都解析占位符return environment.resolvePlaceholders(dataSourceId);}
}

性能问题：

• 当 dataSourceId 是固定值（如 ${db.master}）时，每次解析相同字符串，重复计算
• resolvePlaceholders 内部需遍历 PropertySources，解析嵌套占位符（如 ${db.${env}.url}），存在计算开销

2、优化实现：缓存解析结果

通过 ConcurrentHashMap 缓存已解析的值：

public class OptimizedDataSourceResolver {private Environment environment;private final Map<String, String> cache = new ConcurrentHashMap<>();public String resolve(String dataSourceId) {// 使用缓存避免重复解析return cache.computeIfAbsent(dataSourceId, environment::resolvePlaceholders);}
}

优化关键：

• 相同 dataSourceId 仅解析一次，后续直接从缓存读取。

性能测试验证：JMH基准测试

测试目标：对比无缓存与有缓存的解析性能

• 测试数据：
  • 简单占位符：${db.master}
  • 复杂占位符：${db.${env}.url}
• 并发量：模拟 16 个线程并发调用

@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
@State(Scope.Thread)
@Fork(value = 4)
@Warmup(iterations = 5, time = 5)
@Measurement(iterations = 5, time = 10)
@Threads(16)
public class PlaceholderResolveBenchmark {private OriginalDataSourceResolver originalResolver;private OptimizedDataSourceResolver optimizedResolver;private final String simplePlaceholder = "${db.master}";private final String complexPlaceholder = "${db.${env}.url}";@Setup(Level.Trial)public void setup() {MockEnvironment env = new MockEnvironment();env.setProperty("db.master", "masterDataSource");env.setProperty("env", "prod");env.setProperty("db.prod.url", "jdbc:mysql://localhost:3306/db");originalResolver = new OriginalDataSourceResolver();originalResolver.setEnvironment(env);optimizedResolver = new OptimizedDataSourceResolver();optimizedResolver.setEnvironment(env);}/*** 测试原始解析器-简单占位符* @param bh*/@Benchmarkpublic void testOriginalResolver_Simple(Blackhole bh) {String result = originalResolver.resolve(simplePlaceholder);bh.consume(result);}/*** 测试优化解析器-简单占位符* @param bh*/@Benchmarkpublic void testOptimizedResolver_Simple(Blackhole bh) {String result = optimizedResolver.resolve(simplePlaceholder);bh.consume(result);}/*** 测试原始解析器-复杂占位符* @param bh*/@Benchmarkpublic void testOriginalResolver_Complex(Blackhole bh) {String result = originalResolver.resolve(complexPlaceholder);bh.consume(result);}/*** 测试优化解析器-复杂占位符* @param bh*/@Benchmarkpublic void testOptimizedResolver_Complex(Blackhole bh) {String result = optimizedResolver.resolve(complexPlaceholder);bh.consume(result);}
}

测试结果及分析

• 测试结果如下：

Benchmark                                                      Mode  Cnt       Score       Error   Units
TT.PlaceholderResolveBenchmark.testOptimizedResolver_Complex  thrpt   20  315340.801 ± 59664.044  ops/ms
TT.PlaceholderResolveBenchmark.testOptimizedResolver_Simple   thrpt   20  332918.070 ± 51588.217  ops/ms
TT.PlaceholderResolveBenchmark.testOriginalResolver_Complex   thrpt   20      18.058 ±     2.609  ops/ms
TT.PlaceholderResolveBenchmark.testOriginalResolver_Simple    thrpt   20      36.011 ±     3.894  ops/ms

• 结论：

优化后的 OptimizedDataSourceResolver 性能提升显著，吞吐量达到原始方案的 10,000 倍，缓存策略对高频占位符解析场景有非常明显的优化效果。