引言
在生产环境中，事务性能问题往往在系统压力增大时才暴露出来。如何实时监控事务状态？如何识别和优化事务性能瓶颈？本文将深入探讨Spring事务的监控方案和性能优化实战技巧，帮助你构建高性能、可观测的事务系统。
事务监控体系搭建
1. 事务监控指标定义
核心监控指标：
- 事务执行时间
- 事务成功率/失败率
- 事务回滚率
- 活跃事务数量
- 事务超时统计
- 数据库连接池使用情况
@Component
@Slf4j
public class TransactionMetrics {
private final MeterRegistry meterRegistry;
private final Counter transactionCounter;
private final Timer transactionTimer;
private final Gauge activeTransactions;
private final AtomicInteger activeTxCount = new AtomicInteger(0);
public TransactionMetrics(MeterRegistry meterRegistry) {
this.meterRegistry = meterRegistry;
// 事务计数器
this.transactionCounter = Counter.builder("transaction.total")
.description("总事务数量")
.tag("type", "all")
.register(meterRegistry);
// 事务计时器
this.transactionTimer = Timer.builder("transaction.duration")
.description("事务执行时间")
.register(meterRegistry);
// 活跃事务指标
this.activeTransactions = Gauge.builder("transaction.active")
.description("活跃事务数量")
.register(meterRegistry, activeTxCount);
}
public void incrementTransaction() {
transactionCounter.increment();
activeTxCount.incrementAndGet();
}
public void decrementTransaction() {
activeTxCount.decrementAndGet();
}
public Timer.Sample startTimer() {
return Timer.start(meterRegistry);
}
public void stopTimer(Timer.Sample sample, String transactionName) {
sample.stop(transactionTimer);
}
}
2. 事务事件监听
@Component
@Slf4j
public class TransactionEventListener {
@Autowired
private TransactionMetrics transactionMetrics;
// 监听事务开始事件
@EventListener
public void handleTransactionBegin(TransactionStartedEvent event) {
String transactionName = event.getTransactionName();
log.debug("事务开始: {}", transactionName);
transactionMetrics.incrementTransaction();
// 记录事务开始时间
TransactionContextHolder.setStartTime(transactionName, System.currentTimeMillis());
}
// 监听事务提交事件
@EventListener
public void handleTransactionCommit(TransactionCompletedEvent event) {
String transactionName = event.getTransactionName();
log.debug("事务提交: {}", transactionName);
transactionMetrics.decrementTransaction();
Long startTime = TransactionContextHolder.getStartTime(transactionName);
if (startTime != null) {
long duration = System.currentTimeMillis() - startTime;
log.info("事务 {} 执行时间: {}ms", transactionName, duration);
// 记录慢事务
if (duration > 1000) {
log.warn("慢事务警告: {} 执行了 {}ms", transactionName, duration);
}
}
}
// 监听事务回滚事件
@EventListener
public void handleTransactionRollback(TransactionRolledBackEvent event) {
String transactionName = event.getTransactionName();
log.warn("事务回滚: {}", transactionName);
transactionMetrics.decrementTransaction();
// 回滚统计
meterRegistry.counter("transaction.rollback",
"reason", getRollbackReason(event)).increment();
}
private String getRollbackReason(TransactionRolledBackEvent event) {
// 分析回滚原因
return event.getException() != null ? "exception" : "manual";
}
}
// 事务上下文持有器
@Component
public class TransactionContextHolder {
private static final ThreadLocal<Map<String, Object>> context =
ThreadLocal.withInitial(HashMap::new);
public static void setStartTime(String transactionName, long startTime) {
context.get().put(transactionName + ".startTime", startTime);
}
public static Long getStartTime(String transactionName) {
return (Long) context.get().get(transactionName + ".startTime");
}
public static void clear() {
context.remove();
}
}
3. 自定义事务拦截器
@Component
public class TransactionMonitoringInterceptor implements MethodInterceptor {
@Autowired
private TransactionMetrics transactionMetrics;
@Override
public Object invoke(MethodInvocation invocation) throws Throwable {
Method method = invocation.getMethod();
Transactional transactional = method.getAnnotation(Transactional.class);
if (transactional == null) {
return invocation.proceed();
}
String transactionName = method.getDeclaringClass().getSimpleName() + "." + method.getName();
Timer.Sample sample = transactionMetrics.startTimer();
transactionMetrics.incrementTransaction();
try {
Object result = invocation.proceed();
transactionMetrics.stopTimer(sample, transactionName);
return result;
} catch (Exception e) {
// 记录失败事务
meterRegistry.counter("transaction.failure",
"exception", e.getClass().getSimpleName()).increment();
throw e;
} finally {
transactionMetrics.decrementTransaction();
}
}
}
// 配置拦截器
@Configuration
@EnableAspectJAutoProxy
public class InterceptorConfig {
@Bean
public TransactionMonitoringInterceptor transactionMonitoringInterceptor() {
return new TransactionMonitoringInterceptor();
}
@Bean
public DefaultPointcutAdvisor transactionMonitoringAdvisor() {
DefaultPointcutAdvisor advisor = new DefaultPointcutAdvisor();
advisor.setPointcut(new AnnotationMatchingPointcut(null, Transactional.class));
advisor.setAdvice(transactionMonitoringInterceptor());
return advisor;
}
}
事务性能优化实战
1. 事务超时优化
问题识别：
@Service
public class TimeoutDetectionService {
@Transactional(timeout = 30)
public void detectTimeoutIssues() {
// 模拟长时间操作
heavyDatabaseOperation();
externalServiceCall();
fileProcessing();
// 可能触发超时
}
}
优化方案：
@Service
public class OptimizedTimeoutService {
@Autowired
private TransactionTemplate transactionTemplate;
// 分阶段处理，避免长事务
public void processInStages(ProcessRequest request) {
// 阶段1：快速操作（短超时）
Stage1Result stage1 = transactionTemplate.execute(status -> {
// 设置短超时
((DataSourceTransactionManager) status.getTransactionManager())
.setDefaultTimeout(10);
return processStage1(request);
});
// 阶段2：耗时操作（独立事务）
Stage2Result stage2 = transactionTemplate.execute(status -> {
((DataSourceTransactionManager) status.getTransactionManager())
.setDefaultTimeout(60);
return processStage2(stage1);
});
// 阶段3：最终提交（短超时）
transactionTemplate.execute(status -> {
((DataSourceTransactionManager) status.getTransactionManager())
.setDefaultTimeout(10);
return finalizeProcess(stage1, stage2);
});
}
// 动态超时配置
@Transactional
public void dynamicTimeoutOperation(OperationType type) {
// 根据操作类型动态调整超时
TransactionStatus status = TransactionAspectSupport.currentTransactionStatus();
if (status instanceof DefaultTransactionStatus) {
DefaultTransactionStatus defaultStatus = (DefaultTransactionStatus) status;
int timeout = calculateTimeout(type);
defaultStatus.setTimeout(timeout);
}
performOperation(type);
}
private int calculateTimeout(OperationType type) {
switch (type) {
case QUICK: return 5;
case NORMAL: return 30;
case SLOW: return 120;
default: return 30;
}
}
}
2. 连接池优化配置
@Configuration
public class DataSourceConfig {
@Bean
@Primary
public DataSource dataSource() {
HikariDataSource dataSource = new HikariDataSource();
// 基础配置
dataSource.setJdbcUrl("jdbc:mysql://localhost:3306/test");
dataSource.setUsername("username");
dataSource.setPassword("password");
dataSource.setDriverClassName("com.mysql.cj.jdbc.Driver");
// 连接池优化配置
dataSource.setMaximumPoolSize(20); // 最大连接数
dataSource.setMinimumIdle(5); // 最小空闲连接
dataSource.setConnectionTimeout(30000); // 连接超时30秒
dataSource.setIdleTimeout(600000); // 空闲连接超时10分钟
dataSource.setMaxLifetime(1800000); // 连接最大生命周期30分钟
dataSource.setConnectionTestQuery("SELECT 1");
// 事务相关优化
dataSource.setTransactionIsolation("TRANSACTION_READ_COMMITTED");
dataSource.setAutoCommit(false); // 由Spring管理事务提交
// 监控配置
dataSource.setMetricRegistry(meterRegistry);
dataSource.setHealthCheckRegistry(healthCheckRegistry);
return dataSource;
}
@Bean
public PlatformTransactionManager transactionManager(DataSource dataSource) {
DataSourceTransactionManager transactionManager =
new DataSourceTransactionManager(dataSource);
// 事务管理器优化
transactionManager.setDefaultTimeout(30);
transactionManager.setNestedTransactionAllowed(true);
transactionManager.setValidateExistingTransaction(true);
return transactionManager;
}
}
3. 批量操作性能优化
问题代码：
@Service
public class NaiveBatchService {
@Transactional
public void processBatch(List<Data> dataList) {
for (Data data : dataList) {
// N+1 问题，性能极差
processSingle(data);
}
}
private void processSingle(Data data) {
// 每次循环都开启新事务（错误用法）
processInNewTransaction(data);
}
}
优化方案：
@Service
public class OptimizedBatchService {
@Autowired
private JdbcTemplate jdbcTemplate;
@Autowired
private TransactionTemplate transactionTemplate;
// 批量插入优化
@Transactional
public void batchInsert(List<Data> dataList) {
jdbcTemplate.batchUpdate(
"INSERT INTO data_table (col1, col2) VALUES (?, ?)",
new BatchPreparedStatementSetter() {
@Override
public void setValues(PreparedStatement ps, int i) throws SQLException {
Data data = dataList.get(i);
ps.setString(1, data.getCol1());
ps.setString(2, data.getCol2());
}
@Override
public int getBatchSize() {
return dataList.size();
}
}
);
}
// 分批次处理
public void processLargeBatch(List<Data> dataList, int batchSize) {
List<List<Data>> batches = partition(dataList, batchSize);
for (List<Data> batch : batches) {
transactionTemplate.execute(status -> {
processBatchInTransaction(batch);
return null;
});
// 批次间短暂休息，避免数据库压力过大
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
break;
}
}
}
// 并行处理（谨慎使用）
public void parallelProcess(List<Data> dataList, int parallelism) {
ExecutorService executor = Executors.newFixedThreadPool(parallelism);
List<CompletableFuture<Void>> futures = new ArrayList<>();
List<List<Data>> partitions = partition(dataList, dataList.size() / parallelism);
for (List<Data> partition : partitions) {
CompletableFuture<Void> future = CompletableFuture.runAsync(() -> {
transactionTemplate.execute(status -> {
processBatchInTransaction(partition);
return null;
});
}, executor);
futures.add(future);
}
// 等待所有任务完成
CompletableFuture.allOf(futures.toArray(new CompletableFuture[0])).join();
executor.shutdown();
}
private <T> List<List<T>> partition(List<T> list, int size) {
List<List<T>> partitions = new ArrayList<>();
for (int i = 0; i < list.size(); i += size) {
partitions.add(list.subList(i, Math.min(i + size, list.size())));
}
return partitions;
}
}
4. 读写分离优化
@Configuration
public class ReadWriteDataSourceConfig {
@Bean
@Primary
public DataSource routingDataSource() {
Map<Object, Object> targetDataSources = new HashMap<>();
// 写数据源（主库）
targetDataSources.put("write", createDataSource(
"jdbc:mysql://master:3306/db", "user", "pass"));
// 读数据源（从库）
targetDataSources.put("read1", createDataSource(
"jdbc:mysql://slave1:3306/db", "user", "pass"));
targetDataSources.put("read2", createDataSource(
"jdbc:mysql://slave2:3306/db", "user", "pass"));
ReadWriteRoutingDataSource routingDataSource = new ReadWriteRoutingDataSource();
routingDataSource.setDefaultTargetDataSource(targetDataSources.get("write"));
routingDataSource.setTargetDataSources(targetDataSources);
return routingDataSource;
}
@Bean
public PlatformTransactionManager transactionManager(DataSource dataSource) {
return new DataSourceTransactionManager(dataSource);
}
}
// 读写分离路由数据源
public class ReadWriteRoutingDataSource extends AbstractRoutingDataSource {
@Override
protected Object determineCurrentLookupKey() {
return TransactionSynchronizationManager.isCurrentTransactionReadOnly() ?
"read" : "write";
}
}
// 只读事务服务
@Service
public class ReadOnlyService {
@Transactional(readOnly = true)
public List<Data> findLargeDataset(QueryCondition condition) {
// 自动路由到读库
return dataMapper.selectByCondition(condition);
}
@Transactional(readOnly = true)
public ComplexReport generateReport(ReportRequest request) {
// 复杂查询在只读库执行
List<Data> data = dataMapper.selectForReport(request);
return reportGenerator.generate(data);
}
}
事务死锁检测与处理
1. 死锁监控
@Component
@Slf4j
public class DeadlockMonitor {
@Autowired
private DataSource dataSource;
private final ScheduledExecutorService scheduler =
Executors.newScheduledThreadPool(1);
@PostConstruct
public void startMonitoring() {
// 每30秒检查一次死锁
scheduler.scheduleAtFixedRate(this::checkForDeadlocks, 30, 30, TimeUnit.SECONDS);
}
private void checkForDeadlocks() {
try (Connection conn = dataSource.getConnection();
Statement stmt = conn.createStatement()) {
// MySQL死锁检查
ResultSet rs = stmt.executeQuery(
"SHOW ENGINE INNODB STATUS");
if (rs.next()) {
String status = rs.getString("Status");
if (status.contains("DEADLOCK")) {
log.error("检测到数据库死锁: {}", status);
alertDeadlock(status);
}
}
} catch (SQLException e) {
log.error("死锁检查失败", e);
}
}
private void alertDeadlock(String deadlockInfo) {
// 发送告警通知
// 记录死锁详情
log.warn("死锁详情: {}", deadlockInfo);
// 可以集成到监控系统
meterRegistry.counter("database.deadlock").increment();
}
@PreDestroy
public void stopMonitoring() {
scheduler.shutdown();
}
}
2. 死锁预防策略
@Service
public class DeadlockPreventionService {
// 统一资源访问顺序
@Transactional
public void consistentResourceAccess(Long resourceAId, Long resourceBId) {
// 总是按照ID顺序访问资源
Long firstId = Math.min(resourceAId, resourceBId);
Long secondId = Math.max(resourceAId, resourceBId);
Resource first = resourceMapper.selectForUpdate(firstId);
Resource second = resourceMapper.selectForUpdate(secondId);
// 业务逻辑...
}
// 使用乐观锁避免死锁
@Transactional
public void optimisticUpdate(Long resourceId, UpdateRequest request) {
int retryCount = 0;
final int maxRetries = 3;
while (retryCount < maxRetries) {
try {
Resource resource = resourceMapper.selectById(resourceId);
resource.updateFrom(request);
int updated = resourceMapper.updateWithVersion(resource);
if (updated > 0) {
return; // 更新成功
}
// 版本冲突，重试
retryCount++;
if (retryCount < maxRetries) {
Thread.sleep(100 * retryCount); // 指数退避
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException("更新被中断", e);
}
}
throw new OptimisticLockingException("更新失败，超过最大重试次数");
}
// 设置锁超时
@Transactional
public void updateWithLockTimeout(Long resourceId) {
try (Connection conn = DataSourceUtils.getConnection(dataSource);
Statement stmt = conn.createStatement()) {
// 设置锁等待超时（5秒）
stmt.execute("SET innodb_lock_wait_timeout = 5");
// 执行需要加锁的操作
performLockingOperation(resourceId);
} catch (SQLException e) {
if (e.getErrorCode() == 1205) { // MySQL锁超时错误码
throw new LockTimeoutException("获取锁超时", e);
}
throw new DataAccessException("数据库操作失败", e);
}
}
}
事务监控面板实现
1. Spring Boot Actuator集成
# application.yml
management:
endpoints:
web:
exposure:
include: health,info,metrics,transactions
endpoint:
health:
show-details: always
transactions:
enabled: true
2. 自定义监控端点
@Component
@Endpoint(id = "transactions")
@Slf4j
public class TransactionMonitoringEndpoint {
@Autowired
private PlatformTransactionManager transactionManager;
@Autowired
private TransactionMetrics transactionMetrics;
@ReadOperation
public TransactionStats getTransactionStats() {
TransactionStats stats = new TransactionStats();
// 获取事务统计信息
stats.setActiveTransactions(getActiveTransactionCount());
stats.setTransactionDuration(getAverageTransactionDuration());
stats.setRollbackRate(getRollbackRate());
stats.setSlowTransactionCount(getSlowTransactionCount());
return stats;
}
@WriteOperation
public String resetTransactionMetrics() {
// 重置事务指标（用于测试）
transactionMetrics.reset();
return "Transaction metrics reset successfully";
}
private int getActiveTransactionCount() {
// 实现获取活跃事务数量的逻辑
return transactionMetrics.getActiveTransactionCount();
}
private double getAverageTransactionDuration() {
// 实现获取平均事务时长的逻辑
return transactionMetrics.getAverageDuration();
}
// 其他统计方法...
}
// 事务统计DTO
public class TransactionStats {
private int activeTransactions;
private double averageDuration;
private double rollbackRate;
private int slowTransactionCount;
// getters and setters
}
3. Grafana监控面板配置
{
"dashboard": {
"title": "Spring事务监控",
"panels": [
{
"title": "活跃事务数量",
"type": "stat",
"targets": [
{
"expr": "transaction_active",
"legendFormat": "活跃事务"
}
]
},
{
"title": "事务执行时间",
"type": "graph",
"targets": [
{
"expr": "rate(transaction_duration_seconds_sum[5m]) / rate(transaction_duration_seconds_count[5m])",
"legendFormat": "平均执行时间"
}
]
},
{
"title": "事务回滚率",
"type": "gauge",
"targets": [
{
"expr": "rate(transaction_rollback_total[5m]) / rate(transaction_total[5m])",
"legendFormat": "回滚率"
}
]
}
]
}
}
性能测试与调优
1. 事务性能测试
@SpringBootTest
@Slf4j
public class TransactionPerformanceTest {
@Autowired
private TestService testService;
@Autowired
private DataSource dataSource;
@Test
public void testTransactionPerformanceUnderLoad() {
int threadCount = 50;
int operationsPerThread = 100;
ExecutorService executor = Executors.newFixedThreadPool(threadCount);
List<CompletableFuture<ThreadResult>> futures = new ArrayList<>();
long startTime = System.currentTimeMillis();
for (int i = 0; i < threadCount; i++) {
CompletableFuture<ThreadResult> future = CompletableFuture.supplyAsync(() -> {
return executeOperations(operationsPerThread);
}, executor);
futures.add(future);
}
// 收集结果
List<ThreadResult> results = futures.stream()
.map(CompletableFuture::join)
.collect(Collectors.toList());
long totalTime = System.currentTimeMillis() - startTime;
analyzeResults(results, totalTime, threadCount * operationsPerThread);
}
private ThreadResult executeOperations(int operationCount) {
ThreadResult result = new ThreadResult();
for (int i = 0; i < operationCount; i++) {
long operationStart = System.currentTimeMillis();
try {
testService.performBusinessOperation();
result.incrementSuccess();
} catch (Exception e) {
result.incrementFailure();
log.debug("操作失败", e);
}
long operationTime = System.currentTimeMillis() - operationStart;
result.recordOperationTime(operationTime);
}
return result;
}
private void analyzeResults(List<ThreadResult> results, long totalTime, int totalOperations) {
long totalSuccess = results.stream().mapToLong(ThreadResult::getSuccessCount).sum();
long totalFailure = results.stream().mapToLong(ThreadResult::getFailureCount).sum();
double avgOperationTime = results.stream()
.flatMapToDouble(r -> r.getOperationTimes().stream().mapToDouble(t -> t))
.average()
.orElse(0);
log.info("性能测试结果:");
log.info("总操作数: {}", totalOperations);
log.info("成功: {}, 失败: {}", totalSuccess, totalFailure);
log.info("总耗时: {}ms", totalTime);
log.info("吞吐量: {}/s", totalOperations * 1000.0 / totalTime);
log.info("平均操作时间: {}ms", avgOperationTime);
log.info("成功率: {}%", totalSuccess * 100.0 / totalOperations);
// 连接池使用情况
HikariDataSource hikariDataSource = (HikariDataSource) dataSource;
log.info("连接池活跃连接: {}", hikariDataSource.getHikariPoolMXBean().getActiveConnections());
log.info("连接池空闲连接: {}", hikariDataSource.getHikariPoolMXBean().getIdleConnections());
}
}
class ThreadResult {
private long successCount = 0;
private long failureCount = 0;
private List<Long> operationTimes = new ArrayList<>();
// getters and increment methods
}
2. 性能调优建议
@Configuration
public class PerformanceTuningConfig {
@Bean
public PlatformTransactionManager optimizedTransactionManager(DataSource dataSource) {
DataSourceTransactionManager tm = new DataSourceTransactionManager(dataSource);
// 性能优化配置
tm.setDefaultTimeout(30); // 合理设置超时
tm.setNestedTransactionAllowed(true); // 允许嵌套事务
tm.setValidateExistingTransaction(false); // 关闭验证提升性能
return tm;
}
@Bean
public HikariDataSource optimizedDataSource() {
HikariConfig config = new HikariConfig();
// 连接池优化
config.setMaximumPoolSize(50); // 根据系统负载调整
config.setMinimumIdle(10); // 适当的最小空闲连接
config.setConnectionTimeout(10000); // 连接超时10秒
config.setIdleTimeout(300000); // 空闲超时5分钟
config.setMaxLifetime(1800000); // 最大生命周期30分钟
config.setLeakDetectionThreshold(60000); // 泄漏检测阈值
// MySQL特定优化
config.addDataSourceProperty("cachePrepStmts", "true");
config.addDataSourceProperty("prepStmtCacheSize", "250");
config.addDataSourceProperty("prepStmtCacheSqlLimit", "2048");
config.addDataSourceProperty("useServerPrepStmts", "true");
return new HikariDataSource(config);
}
}
生产环境最佳实践
1. 监控告警配置
# alert-rules.yml
groups:
- name: transaction_alerts
rules:
- alert: HighTransactionRollbackRate
expr: rate(transaction_rollback_total[5m]) / rate(transaction_total[5m]) > 0.1
for: 2m
labels:
severity: warning
annotations:
summary: "事务回滚率过高"
description: "当前事务回滚率超过10%，需要关注"
- alert: SlowTransactionDetection
expr: transaction_duration_seconds > 5
labels:
severity: critical
annotations:
summary: "检测到慢事务"
description: "事务执行时间超过5秒，事务名: {{ $labels.transaction }}"
- alert: DatabaseConnectionPoolExhausted
expr: hikaricp_active_connections / hikaricp_maximum_pool_size > 0.8
for: 1m
labels:
severity: critical
annotations:
summary: "数据库连接池即将耗尽"
description: "活跃连接数超过连接池最大容量的80%"
2. 事务日志规范
@Aspect
@Component
@Slf4j
public class TransactionLoggingAspect {
private static final Logger TX_LOG = LoggerFactory.getLogger("TRANSACTION");
@Around("@annotation(transactional)")
public Object logTransaction(ProceedingJoinPoint joinPoint, Transactional transactional) throws Throwable {
String methodName = joinPoint.getSignature().getName();
String className = joinPoint.getTarget().getClass().getSimpleName();
String transactionName = className + "." + methodName;
long startTime = System.currentTimeMillis();
TX_LOG.info("事务开始: {}, 参数: {}", transactionName, getMethodArgs(joinPoint));
try {
Object result = joinPoint.proceed();
long duration = System.currentTimeMillis() - startTime;
TX_LOG.info("事务提交: {}, 耗时: {}ms, 结果: {}",
transactionName, duration, getResultInfo(result));
return result;
} catch (Exception e) {
long duration = System.currentTimeMillis() - startTime;
TX_LOG.error("事务回滚: {}, 耗时: {}ms, 异常: {}",
transactionName, duration, e.getMessage(), e);
throw e;
}
}
private String getMethodArgs(ProceedingJoinPoint joinPoint) {
Object[] args = joinPoint.getArgs();
if (args == null || args.length == 0) {
return "无参数";
}
// 敏感信息脱敏
return Arrays.stream(args)
.map(this::maskSensitiveInfo)
.collect(Collectors.joining(", "));
}
private String maskSensitiveInfo(Object arg) {
if (arg instanceof String) {
String str = (String) arg;
if (str.contains("@") || str.matches(".*\\d{4,}.*")) {
return "***"; // 脱敏处理
}
}
return String.valueOf(arg);
}
}
总结
通过系统的监控和优化，可以显著提升Spring事务的性能和可靠性：
监控要点：
- 实时监控事务执行状态和性能指标
- 设置合理的告警规则及时发现问题
- 记录详细的事务日志便于问题排查
优化策略：
- 合理配置连接池和事务管理器参数
- 优化事务边界，避免长事务和大事务
- 使用批量操作减少数据库交互次数
- 实施读写分离分担数据库压力
- 预防和检测死锁问题
持续改进：
- 定期进行性能测试和压力测试
- 分析监控数据识别优化机会
- 根据业务变化调整事务策略
记住，事务监控和优化是一个持续的过程，需要结合具体业务场景和系统特点进行调整。
下期预告：《Spring事务在微服务架构中的实践与挑战》
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