本文目录导读：
我来详细解释Java分布式数据条件器的逻辑实现。
核心概念
分布式数据条件器（Distributed Data Filter/Conditioner）用于在分布式环境中对数据进行条件筛选、判断和处理。
基础架构设计
// 条件器接口
public interface DistributedConditioner<T> {
boolean evaluate(T data, ConditionContext context);
void process(T data, ConditionContext context);
}
// 条件上下文
public class ConditionContext {
private String nodeId;
private Map<String, Object> sharedState;
private DistributedLock lock;
private ConditionChain chain;
}
条件链模式实现
public abstract class AbstractConditionNode<T> {
private AbstractConditionNode<T> next;
public abstract boolean check(T data, ConditionContext context);
public boolean execute(T data, ConditionContext context) {
if (!check(data, context)) {
return false;
}
return next == null || next.execute(data, context);
}
}
// 具体条件节点
public class TimeRangeNode extends AbstractConditionNode<Transaction> {
@Override
public boolean check(Transaction data, ConditionContext context) {
long currentTime = System.currentTimeMillis();
return currentTime >= data.getStartTime()
&& currentTime <= data.getEndTime();
}
}
分布式协调逻辑
1 一致性哈希路由
public class ConsistentHashConditioner<T> {
private ConsistentHashRouter<T> router;
private Map<String, LocalConditionProcessor> processors;
public boolean evaluateAcrossNodes(T data, ConditionContext context) {
// 根据数据key计算应该路由到的节点
String targetNode = router.getNode(data.getKey());
if (isLocalNode(targetNode)) {
// 本地处理
return localProcess(data, context);
} else {
// 远程调用
return remoteEvaluate(targetNode, data, context);
}
}
}
2 分布式锁机制
public class DistributedLockConditioner<T> {
private RedisDistributedLock lock;
public boolean evaluateWithLock(T data, ConditionContext context) {
String lockKey = generateLockKey(data);
boolean locked = false;
try {
// 尝试获取分布式锁
locked = lock.tryLock(lockKey, 1000, TimeUnit.MILLISECONDS);
if (!locked) {
// 降级处理或等待
return evaluateWithFallback(data, context);
}
// 在锁保护下执行条件判断
return doEvaluate(data, context);
} finally {
if (locked) {
lock.unlock(lockKey);
}
}
}
}
复杂条件组合逻辑
public class CompositeConditioner<T> {
private List<ConditionOperator> conditions;
private CombineMode mode; // AND/OR
public boolean evaluate(T data, ConditionContext context) {
switch (mode) {
case AND:
return evaluateAnd(data, context);
case OR:
return evaluateOr(data, context);
case XOR:
return evaluateXor(data, context);
default:
throw new UnsupportedOperationException();
}
}
private boolean evaluateAnd(T data, ConditionContext context) {
return conditions.stream()
.allMatch(cond -> cond.evaluate(data, context));
}
private boolean evaluateOr(T data, ConditionContext context) {
return conditions.stream()
.anyMatch(cond -> cond.evaluate(data, context));
}
}
状态管理与缓存
public class StatefulConditioner<T> {
private Cache<Long, Boolean> conditionCache;
private StateManager stateManager;
public boolean evaluateWithCache(T data, ConditionContext context) {
long cacheKey = generateCacheKey(data);
// 尝试从缓存获取
Boolean cached = conditionCache.getIfPresent(cacheKey);
if (cached != null) {
return cached;
}
// 检查状态是否发生变化
if (stateManager.isStateChanged(data)) {
// 重新评估
boolean result = doEvaluate(data, context);
conditionCache.put(cacheKey, result);
return result;
}
// 使用分布式状态检查
return evaluateWithDistributedState(data, context);
}
}
容错与降级逻辑
public class FaultTolerantConditioner<T> {
private CircuitBreaker circuitBreaker;
private FallbackStrategy fallbackStrategy;
public boolean evaluate(T data, ConditionContext context) {
try {
if (!circuitBreaker.isAvailable()) {
return fallbackStrategy.evaluate(data, context);
}
return tryEvaluate(data, context);
} catch (DistributedException e) {
// 记录失败
circuitBreaker.recordFailure();
// 根据策略降级
return handleDegradation(data, context, e);
}
}
private boolean handleDegradation(T data, ConditionContext context,
DistributedException e) {
switch (e.getType()) {
case TIMEOUT:
// 超时降级 - 使用本地缓存结果
return evaluateFromLocalCache(data);
case NODE_FAILURE:
// 节点故障 - 路由到备份节点
return evaluateWithBackup(data, context);
case NETWORK_ERROR:
// 网络错误 - 使用宽松条件
return evaluateWithLooseCondition(data);
default:
// 默认拒绝
return false;
}
}
}
完整实现示例
@Service
public class DistributedTransactionConditioner {
@Autowired
private DistributedCache cache;
@Autowired
private DistributedLock distributedLock;
@Autowired
private NodeRouter nodeRouter;
public EvaluationResult evaluateTransaction(Transaction tx) {
ConditionContext context = buildContext(tx);
// 1. 前置检查
if (!preCheck(tx, context)) {
return EvaluationResult.REJECTED;
}
// 2. 获取分布式锁
String lockKey = "tx:" + tx.getId();
boolean locked = distributedLock.acquire(lockKey, 5, TimeUnit.SECONDS);
if (!locked) {
return EvaluationResult.BUSY;
}
try {
// 3. 构建条件链
ConditionChain chain = buildConditionChain(tx.getType());
// 4. 分布式条件评估
boolean passed = chain.evaluate(tx, context);
// 5. 跨节点一致性检查
if (passed) {
passed = crossNodeConsistencyCheck(tx, context);
}
// 6. 执行结果处理
if (passed) {
processPassedTransaction(tx, context);
return EvaluationResult.PASSED;
} else {
processRejectedTransaction(tx, context);
return EvaluationResult.REJECTED;
}
} finally {
distributedLock.release(lockKey);
}
}
private ConditionChain buildConditionChain(TransactionType type) {
ConditionChain chain = new ConditionChain();
// 根据交易类型构建不同的条件链
switch (type) {
case PAYMENT:
chain.addCondition(new AmountCondition(50000))
.addCondition(new TimeWindowCondition())
.addCondition(new RiskAssessCondition());
break;
case WITHDRAW:
chain.addCondition(new BalanceCondition())
.addCondition(new DailyLimitCondition())
.addCondition(new FrequencyCondition());
break;
}
return chain;
}
private boolean crossNodeConsistencyCheck(Transaction tx,
ConditionContext context) {
// 对跨多个节点的数据进行一致性检查
List<String> relatedNodes = nodeRouter.getRelatedNodes(tx);
for (String node : relatedNodes) {
if (!isLocalNode(node)) {
boolean remoteResult = remoteCheck(node, tx, context);
if (!remoteResult) {
return false;
}
}
}
return true;
}
}
配置与监控
# application.yml
distributed:
conditioner:
timeout: 5000ms
retry:
max-attempts: 3
backoff: 200ms
cache:
ttl: 60s
max-size: 10000
circuit-breaker:
failure-threshold: 5
reset-timeout: 30000ms
关键设计要点
- 链式处理：使用责任链模式处理复杂条件
- 分布式协调：通过锁、缓存、路由确保一致性
- 容错机制：熔断、降级、重试保证系统稳定性
- 性能优化：缓存、异步处理、并行评估
- 可扩展性：插件化条件节点，支持动态扩展
这个架构能够处理高并发、跨节点的复杂条件判断场景。
