How do you monitor and troubleshoot event-driven architecture systems in production?

由人工智能和领英社区提供技术支持

Event-driven architecture (EDA) is a popular pattern for designing software systems that react to events from various sources, such as user actions, sensors, or other systems. EDA can improve scalability, performance, and flexibility, but it also introduces some challenges for monitoring and troubleshooting in production. In this article, you will learn some best practices and tools for keeping your event-driven systems healthy and reliable.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

1 The importance of observability

Observability is the ability to measure and understand the internal state of a system based on the external outputs. For event-driven systems, observability is crucial, because the events are often asynchronous, distributed, and complex. Without observability, you may not know what events are happening, where they are going, how they are processed, or what errors or failures occur. Observability helps you to detect and diagnose problems, optimize performance, and improve quality.

添加您的观点

Himani Borana

Actively seeking Full-time opportunities | SDE | SE | Backend Developer | Full Stack Developer | Open to Relocate | GHC’23
举报内容
To monitor and troubleshoot event-driven architecture systems, use tools like Prometheus and Grafana for real-time metrics and visualizations. For instance, in a microservices-based e-commerce platform, Prometheus tracks service performance, while Grafana displays it. The ELK Stack (Elasticsearch, Logstash, Kibana) centralizes logging to pinpoint issues by analyzing logs. If the order processing service fails, ELK helps identify the problem. Distributed tracing tools like Jaeger trace events across services, revealing latency or bottlenecks. For example, Jaeger can trace delays in payment services. CI/CD pipelines like Jenkins ensure continuous monitoring and integration, catching issues early and maintaining system reliability.

已翻译

赞

2 The key metrics and logs for event-driven systems

To achieve observability, you need to collect and analyze metrics and logs from your event-driven systems. Metrics are numerical measurements that indicate the health and performance of your system, such as throughput, latency, error rate, or queue size. Logs are textual records that provide more details about the events and their processing, such as event IDs, timestamps, sources, destinations, or payloads. You should define and collect metrics and logs that are relevant, consistent, and comprehensive for your event-driven systems.

添加您的观点

Prathamesh Narkhede

???? Software Development Engineer @ESRI | ?? C#, .Net, MAUI, AWS Expertise | ?? MS in CIS
举报内容
We heavily relied on CloudWatch and S3 Datalake for monitoring and troubleshooting our event-driven architecture systems. One thing I've found helpful is defining and collecting metrics that truly matter. Metrics like throughput, latency, and error rate were crucial for gauging system health and performance. Simultaneously, logs played a pivotal role in providing detailed insights into events, with CloudWatch serving as a valuable tool for aggregating and analyzing these logs. This approach, rooted in practical application rather than buzzwords, ensured we maintained a high level of observability, facilitating proactive issue identification and resolution in our event-driven systems.

已翻译

赞

3 The best practices for tracing events

Tracing is a technique that tracks the flow and history of events across different components and services in your system. Tracing can help you to understand the dependencies, bottlenecks, and failures in your event-driven systems. To enable tracing, you should assign unique identifiers to each event and propagate them throughout the system. You should also use standard formats and protocols, such as OpenTelemetry or Zipkin, to capture and transmit trace data. You should also use tools that can visualize and analyze trace data, such as Jaeger or Elastic APM.

添加您的观点

Pranay Prateek

Co-Founder at SigNoz | The future of Observability is Open Source | Hiring devrel engineers, open source community engineers, product marketeers - write to [email protected] | Y Combinator W21
举报内容
For example, if you have producer and consumer NodeJS applications which are communicating via Kafka as the message broker, you can use OpenTelemetry to instrument your NodeJS application and use KafkaJS library in OpenTelemetry to instrument Kafka clients as well. This will give you a client side view of delays happening because of Kafka via traces. Using the span data in traces, you can figure out the time taken between producer and consumer application to receive messages via Kafka

已翻译

赞
Mateusz Nowak

???? Software Craftsman | Domain-Driven Design | Event Sourcing | EventStorming | Event Modeling | Kotlin | Java | C# .NET | Ruby | Node.js
举报内容
Not only should you assign an unique identifier to the event, but you should also add metadata such as correlationId and causationId, also known as traceId and spanId. This way, you can see which events are part of the same process and what the cause (another event) of each of them was.

已翻译

赞

4 The common challenges and pitfalls for event-driven systems

Event-driven systems can face some common challenges and pitfalls that can affect their observability and reliability, such as event loss, duplication, and ordering. To prevent event loss, you should use reliable messaging protocols, such as AMQP or MQTT, with durable storage like Kafka or RabbitMQ. To address event duplication, you should use idempotent processing and deduplication techniques like sequence numbers or hashes. Finally, to order events correctly, you should use sequencing mechanisms like timestamps or sequence numbers, as well as event sourcing or sagas to maintain consistency and handle compensating actions.

添加您的观点

5 The tools and frameworks for event-driven systems

There are many tools and frameworks that can help you design, implement, monitor, and troubleshoot event-driven systems. Apache Kafka is a distributed streaming platform that can handle millions of events per second with high scalability, availability, and durability. It provides APIs for producing and consuming events, as well as connectors for integrating with various sources and sinks. AWS Lambda is a serverless computing service that lets you run code in response to events without managing servers or infrastructure. Azure Event Grid is a cloud service that enables event-driven communication between different Azure services and applications. It allows you to publish and subscribe to events, filter and route them based on rules, and use custom topics and webhooks for integrating with external sources and sinks.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Software Architectural Design

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How do you monitor and troubleshoot event-driven architecture systems in production?

1

2

3

4

5

6

1 The importance of observability

2 The key metrics and logs for event-driven systems

3 The best practices for tracing events

4 The common challenges and pitfalls for event-driven systems

5 The tools and frameworks for event-driven systems

6 Here’s what else to consider

Software Architectural Design

给文章评分

感谢您的反馈

更多Software Architectural Design相关文章

更多相关阅读内容