死信队列与失败处理
消息可能处理失败。了解死信队列如何捕获有害消息，以及如何为韧性事件驱动系统设计重试和重新处理机制。
死信队列与失败处理 是 CoddyKit 上的免费 Serverless Backend with AWS Lambda & API Gateway 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Serverless Backend with AWS Lambda & API Gateway 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Serverless Backend with AWS Lambda & API Gateway 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
The Problem of Failing Messages
In an event-driven system a message may fail repeatedly — bad data, a downstream outage, or a bug. Without a safety net, it can block the queue or be lost forever.
What is a Dead-Letter Queue?
A dead-letter queue (DLQ) is a separate queue that captures messages that could not be processed after a set number of attempts. This isolates poison messages without losing them.
Redrive Policy
An SQS queue routes to a DLQ via a redrive policy that names the DLQ and a maxReceiveCount. After that many failed receives, the message moves to the DLQ.
{
"deadLetterTargetArn": "arn:aws:sqs:us-east-1:123:orders-dlq",
"maxReceiveCount": 5
}Configuring a DLQ for Lambda
Async Lambda invocations (S3, SNS) can send failures to a DLQ or to an on-failure destination via the event invoke config.
aws lambda put-function-event-invoke-config \
--function-name processOrder \
--destination-config '{"OnFailure":{"Destination":"arn:aws:sqs:...:orders-dlq"}}'Setting maxReceiveCount Wisely
Too low and transient blips dead-letter good messages; too high and a poison message wastes many retries. A value of 3 to 5 is a common starting point.
Inspecting Dead-Lettered Messages
Messages in the DLQ keep the original body plus attributes showing why they failed. Read them to diagnose the root cause before reprocessing.
aws sqs receive-message \
--queue-url https://sqs.../orders-dlq \
--attribute-names AllRedriving Messages Back
Once the bug is fixed, SQS can redrive messages from the DLQ back to the source queue for reprocessing — no manual copying needed.
Idempotency Matters
Because retries and redrives can deliver the same message more than once, your handler must be idempotent: processing the same message twice should not double-charge or duplicate records.
Alerting on the DLQ
A growing DLQ is a signal something is broken. Set a CloudWatch alarm on the DLQ ApproximateNumberOfMessagesVisible metric to get notified.
Partial Batch Failures
When Lambda reads a batch from SQS, reporting batchItemFailures lets only the failed messages return to the queue, instead of reprocessing the whole batch.
{
"batchItemFailures": [
{ "itemIdentifier": "msg-id-3" }
]
}Designing for Resilience
A resilient pipeline combines:
- Retries for transient errors
- A DLQ for poison messages
- Idempotent handlers
- Alarms and a redrive plan
Quick Check
Test your failure-handling knowledge.
Recap
You learned to handle failing messages:
- A DLQ isolates poison messages after maxReceiveCount
- Configure redrive policies and Lambda on-failure destinations
- Inspect, fix, then redrive messages back
- Make handlers idempotent and alarm on DLQ depth
- Use batchItemFailures for partial batch retries
常见问题解答
「死信队列与失败处理」课时是免费的吗？
是的 — 「死信队列与失败处理」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Serverless Backend with AWS Lambda & API Gateway 课程的其余内容，请升级到 CoddyKit PRO。 Serverless Backend with AWS Lambda & API Gateway 课程共包含 4 节课。
「死信队列与失败处理」这节课中我会学到什么？
消息可能处理失败。了解死信队列如何捕获有害消息，以及如何为韧性事件驱动系统设计重试和重新处理机制。 你通过在浏览器中直接运行的动手代码来练习 Serverless Backend with AWS Lambda & API Gateway，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Serverless Backend with AWS Lambda & API Gateway 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Serverless Backend with AWS Lambda & API Gateway 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「死信队列与失败处理」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Serverless Backend with AWS Lambda & API Gateway 课中编写并运行代码吗？
能。每节 Serverless Backend with AWS Lambda & API Gateway 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。