ABP VNext + Apache Flink 实时流计算：打造高可用“交易风控”系统

ABP VNext + Apache Flink 实时流计算：打造高可用“交易风控”系统 🌐

📚 目录

• ABP VNext + Apache Flink 实时流计算：打造高可用“交易风控”系统 🌐
• • 一、背景🚀
  • 二、系统整体架构 🏗️
  • 三、实战展示 🛠️：交易行为告警系统
  • • 3.1 ABP 采集交易事件 📝
    • • CAP + Outbox 配置示例 💼
      • 3.2 Flink CEP 模式与 Exactly-Once ⚡
      • 3.3 Redis Stream + SignalR 实时推送 🔔
      • 四、生产级部署和监控 📈
      • 五、自动化测试 🧪

        ---

        一、背景🚀

        在金融 💰、电商 🛒、IoT 🌐 等高频交互系统中，越来越多的场景需要“实时发现问题并响应”。

        ---

        二、系统整体架构 🏗️

        💡 图示展示了各组件之间的数据流向，实现消息解耦和高可用。

        ---

        三、实战展示 🛠️：交易行为告警系统

        3.1 ABP 采集交易事件 📝

        using System;
        using System.Threading.Tasks;
        using Microsoft.Extensions.Logging;
        using Volo.Abp.EventBus;
        using Volo.Abp.EventBus.Distributed;
        public class TransactionCreatedDomainEvent : DomainEvent
        {
            public Guid UserId { get; set; }
            public decimal Amount { get; set; }
            public string Location { get; set; }
        }
        public class TransactionCreatedHandler : IDistributedEventHandler
        {
            private readonly IDistributedEventBus _eventBus;
            private readonly ILogger _logger;
            public TransactionCreatedHandler(IDistributedEventBus eventBus,
                                             ILogger logger)
            {
                _eventBus = eventBus;
                _logger = logger;
            }
            public async Task HandleEventAsync(TransactionCreatedDomainEvent eventData)
            {
                var eto = new TransactionCreatedEto
                {
                    UserId = eventData.UserId,
                    Amount = eventData.Amount,
                    Location = eventData.Location,
                    OccurredAt = Clock.Now
                };
                try
                {
                    await _eventBus.PublishAsync(eto);
                }
                catch (Exception ex)
                {
                    _logger.LogError(ex, "发布交易事件失败：{UserId}", eventData.UserId);
                    throw;
                }
            }
        }
        

        CAP + Outbox 配置示例 💼

        // appsettings.json
        "Cap": {
          "UseEntityFramework": true,
          "UseDashboard": true,
          "Producer": {
            "Kafka": { "Servers": "localhost:9092" }
          },
          "Outbox": { "TableName": "CapOutboxMessages" }
        }
        

        ---

        3.2 Flink CEP 模式与 Exactly-Once ⚡

        import org.apache.flink.streaming.api.scala._
        import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment
        import org.apache.flink.streaming.api.CheckpointingMode
        import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
        import org.apache.flink.api.common.eventtime._
        import org.apache.flink.contrib.streaming.state.RocksDBStateBackend
        import org.apache.flink.cep.scala.pattern.Pattern
        import org.apache.flink.cep.scala.CEP
        import java.time.Duration
        val env = StreamExecutionEnvironment.getExecutionEnvironment
        env.enableCheckpointing(10000)
        env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
        env.setStateBackend(new RocksDBStateBackend("file:///flink-checkpoints"))
        env.getCheckpointConfig.setExternalizedCheckpointCleanup(
          ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
        val watermarkStrategy = WatermarkStrategy
          .forBoundedOutOfOrderness[Transaction](Duration.ofSeconds(5))
          .withTimestampAssigner((event, _) => event.timestamp.toEpochMilli)
        val stream = env
          .addSource(new FlinkKafkaConsumer[Transaction]("transactions", deserializer, props))
          .assignTimestampsAndWatermarks(watermarkStrategy)
        val pattern = Pattern.begin[Transaction]("first")
          .where(_.amount > 10000)
          .next("second")
          .where(new IterativeCondition[Transaction] {
            override def filter(event: Transaction, ctx: IterativeCondition.Context[Transaction]) = {
              val first = ctx.getEventsForPattern("first").iterator().next()
              event.location != first.location
            }
          })
          .within(Time.minutes(5))
        pattern.handleTimeout(new PatternTimeoutFunction[Transaction, Unit] {
          override def timeout(map: java.util.Map[String, java.util.List[Transaction]], timestamp: Long, out: Collector[Unit]): Unit = {
            // 超时清理逻辑
          }
        }, Time.minutes(5))
        

        💡 建议全链路使用 Schema Registry 管理消息格式，防止兼容性问题。

        ---

        3.3 Redis Stream + SignalR 实时推送 🔔

        using System;
        using System.Text.Json;
        using System.Threading;
        using System.Threading.Tasks;
        using Microsoft.Extensions.Hosting;
        using Microsoft.Extensions.Logging;
        using StackExchange.Redis;
        using Microsoft.AspNetCore.Authorization;
        using Microsoft.AspNetCore.SignalR;
        public class RiskAlertWorker : BackgroundService
        {
            private readonly IConnectionMultiplexer _redis;
            private readonly IHubContext _hubContext;
            private readonly ILogger _logger;
            public RiskAlertWorker(IConnectionMultiplexer redis,
                                   IHubContext hubContext,
                                   ILogger logger)
            {
                _redis = redis;
                _hubContext = hubContext;
                _logger = logger;
            }
            protected override async Task ExecuteAsync(CancellationToken stoppingToken)
            {
                var db = _redis.GetDatabase();
                try { await db.StreamCreateConsumerGroupAsync("risk-alerts", "alert-group", "$", true); }
                catch { /* 忽略 BUSYGROUP */ }
                int backoff = 1000;
                while (!stoppingToken.IsCancellationRequested)
                {
                    try
                    {
                        var entries = await db.StreamReadGroupAsync(
                            "risk-alerts", "alert-group", "consumer-1",
                            count: 10, flags: CommandFlags.Block(5000));
                        foreach (var entry in entries)
                        {
                            var alert = JsonSerializer.Deserialize(entry["data"]!);
                            await _hubContext.Clients.Group(alert.UserId.ToString())
                                       .SendAsync("ReceiveAlert", alert, stoppingToken);
                            await db.StreamAcknowledgeAsync("risk-alerts", "alert-group", entry.Id);
                        }
                        backoff = 1000;
                    }
                    catch (Exception ex)
                    {
                        _logger.LogError(ex, "处理 Redis 告警失败");
                        await Task.Delay(backoff, stoppingToken);
                        backoff = Math.Min(backoff * 2, 16000);
                    }
                }
            }
        }
        [Authorize]
        public class RiskAlertHub : Hub { }
        

        ---

        四、生产级部署和监控 📈

        组件	推荐配置
        ABP 后端	Pod 存活/就绪探针 ✅ + HTTPS 🔒 + Serilog→Elasticsearch Sink 📝 + CAP Outbox
        Kafka	enable.idempotence=true 🔁, acks=all ✅, TLS/SASL 🔐
        Flink	RocksDBStateBackend ⚙️ + EXACTLY_ONCE ⚡ + State TTL 🕒 + HA 🌟
        Redis	Redis Cluster 🔄 + AOF 📝 + ACL 🔑 + 阻塞消费 ⏳
        PostgreSQL	主从流复制 🛠️ + WAL 日志 📜 + TimescaleDB 插件 📊
        SignalR	Azure SignalR ☁️ / Redis Backplane 🔄 + JWT 鉴权 🔏

        # Flink YAML 示例
        state.backend: rocksdb
        checkpointing:
          interval: 10s
          mode: EXACTLY_ONCE
          externalized-checkpoint-retention: RETAIN_ON_CANCELLATION
        

        # Flink Prometheus Reporter
        metrics.reporters: prom
        metrics.reporter.prom.class: org.apache.flink.metrics.prometheus.PrometheusReporter
        metrics.reporter.prom.port: 9250
        

        📊 在 Grafana 中可视化：Kafka TPS、Flink 延迟分位、Redis 消费速率、ABP 请求成功率/错误率。

        ---

        五、自动化测试 🧪

        // Testcontainers 启动依赖
        var kafka = new KafkaContainer().StartAsync().GetAwaiter().GetResult();
        var redis = new RedisContainer().StartAsync().GetAwaiter().GetResult();
        var postgres = new PostgreSqlContainer().StartAsync().GetAwaiter().GetResult();
        // 注入到 ABP 测试模块
        context.Services.Configure(opts => {
            opts.ProducerConnectionString = kafka.GetBootstrapAddress();
            opts.OutboxTableName = "CapOutboxMessages";
        });
        // Flink MiniCluster
        var flinkCluster = new MiniClusterWithClientResource(
            new MiniClusterResourceConfiguration.Builder().Build());
        flinkCluster.Start();
        

        ---

        
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