事件驱动流处理管道架构

Architecture

实时事件流管道架构图，IoT 传感器、应用日志和用户点击流数据通过 Kafka 流入 Apache Flink 进行窗口聚合、异常检测以及向数据湖和仪表板的多目标输出。该模板可视化从摄取到转换、存储和告警的端到端流处理。对于构建实时分析和监控平台的数据工程师至关重要。

下载 .fz 文件

完整 FlowZap 代码

Sources { # Data Sources
n1: rectangle label:"IoT Sensor Stream"
n2: rectangle label:"Application Logs"
n3: rectangle label:"User Clickstream"
n4: rectangle label:"Transaction Events"
n1.handle(bottom) -> Ingestion.n5.handle(top) [label="MQTT"]
n2.handle(bottom) -> Ingestion.n5.handle(top) [label="Fluentd"]
n3.handle(bottom) -> Ingestion.n6.handle(top) [label="Kafka Connect"]
n4.handle(bottom) -> Ingestion.n6.handle(top) [label="CDC"]
}
Ingestion { # Stream Ingestion (Kafka)
n5: rectangle label:"Kafka Topic: Raw Events"
n6: rectangle label:"Kafka Topic: Enriched Events"
n7: rectangle label:"Schema Registry Validate"
n8: rectangle label:"Partition by Key"
n5.handle(right) -> n7.handle(left) [label="Validate"]
n7.handle(right) -> n8.handle(left) [label="Schema OK"]
n8.handle(bottom) -> Processing.n9.handle(top) [label="Partitioned"]
n6.handle(bottom) -> Processing.n9.handle(top) [label="Stream"]
}
Processing { # Stream Processing (Flink/Spark)
n9: rectangle label:"Window Aggregation"
n10: rectangle label:"Filter and Transform"
n11: rectangle label:"Join Streams"
n12: diamond label:"Anomaly Detected?"
n13: rectangle label:"Emit Alert Event"
n14: rectangle label:"Write to Sink"
n9.handle(right) -> n10.handle(left) [label="5-min Window"]
n10.handle(right) -> n11.handle(left) [label="Cleaned"]
n11.handle(right) -> n12.handle(left) [label="Enriched"]
n12.handle(right) -> n14.handle(left) [label="Normal"]
n12.handle(bottom) -> n13.handle(top) [label="Anomaly"]
n13.handle(right) -> n14.handle(left) [label="Alert + Data"]
n14.handle(bottom) -> Sinks.n15.handle(top) [label="Output"]
}
Sinks { # Data Sinks
n15: rectangle label:"Data Lake (S3/HDFS)"
n16: rectangle label:"Real-Time Dashboard"
n17: rectangle label:"Alert Notification"
n18: rectangle label:"Search Index (Elasticsearch)"
n15.handle(right) -> n16.handle(left) [label="Batch Analytics"]
n17.handle(right) -> n18.handle(left)
}

为什么需要这个工作流？

Batch processing introduces hours of latency between data generation and insight. Real-time event streaming pipelines process data as it arrives, enabling immediate anomaly detection, live dashboards, and sub-second alerting—critical for IoT monitoring, fraud detection, and operational intelligence.

工作原理

Step 1: Data sources (IoT sensors, application logs, clickstreams) publish events to Kafka topics.
Step 2: Schema Registry validates event formats before ingestion.
Step 3: Events are partitioned by key for ordered, parallel processing.
Step 4: Apache Flink performs window aggregation, filtering, and stream joins.
Step 5: Anomaly detection triggers alert events for immediate notification.
Step 6: Processed data flows to multiple sinks: data lake, real-time dashboard, and search index.

替代方案

Batch ETL with Spark is simpler but adds hours of latency. Managed streaming services like AWS Kinesis or Google Dataflow reduce operational complexity. This template visualizes the end-to-end streaming pipeline architecture.

Key Facts

Template Name	事件驱动流处理管道架构
Category	Architecture
Steps	6 workflow steps
Format	FlowZap Code (.fz file)