[{"data":1,"prerenderedAt":566},["ShallowReactive",2],{"content-svc-lambda-guides-aws-lambda-operator-guide-retries-failure":3},{"markdown":4,"frontMatterAttributes":5,"bodyRaw":8,"menu":9,"menuType":563,"isCollapseableMenu":12,"nextDocItem":96,"previousDocItem":86,"contributorPaths":564,"contentName":565,"slug":95,"readingTime":58},"\u003Cp>AWS serverless services, including Lambda, are fault-tolerant and designed to handle failures. In the case of Lambda, if a service invokes a Lambda function and there is a service disruption, Lambda invokes your function in a different Availability Zone. If your function throws an error, the Lambda service retries your function.\u003C\u002Fp>\n\u003Cp>Since the same event may be received more than once, functions should be designed to be \u003Ca href=\"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FIdempotence\">idempotent\u003C\u002Fa>. This means that receiving the same event multiple times does not change the result beyond the first time the event was received.\u003C\u002Fp>\n\u003Cp>For example, if a credit card transaction is attempted twice due to a retry, the Lambda function should process the payment on the first receipt. On the second retry, either the Lambda function should discard the event or the downstream service it uses should be idempotent.\u003C\u002Fp>\n\u003Cp>A Lambda function implements idempotency typically by using a DynamoDB table to track recently processed identifiers to determine if the transaction has been handled previously. The DynamoDB table usually implements a \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Famazondynamodb\u002Flatest\u002Fdeveloperguide\u002FTTL.html\">Time To Live (TTL)\u003C\u002Fa> value to expire items to limit the storage space used.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fassets\u002Fimages\u002Fevent-driven-architectures-figure-12.png\" alt=\"\">\u003C\u002Fp>\n\u003Cp>For failures within the custom code of a Lambda function, the service offers a number of features to help preserve and retry the event, and provide monitoring to capture that the failure has occurred. Using these approaches can help you develop workloads that are resilient to failure and improve the durability of events as they are processed by Lambda functions. These are explored further in \u003Ca href=\"debugging-ops\">Debugging\u003C\u002Fa> and \u003Ca href=\"monitoring-observability\">Monitoring and observability\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>To learn more, read \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Fwellarchitected\u002Flatest\u002Fserverless-applications-lens\u002Fgeneral-design-principles.html\">General Design Principles\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww2.deloitte.com\u002Fcontent\u002Fdam\u002FDeloitte\u002Ftr\u002FDocuments\u002Ftechnology-media-telecommunications\u002FServerless%20Computing.pdf\">Serverless Computing – Architectural Considerations & Principles\u003C\u002Fa> by Deloitte Consulting.\u003C\u002Fp>\n",{"title":6,"order":7},"Developing for retries and failures",13,"AWS serverless services, including Lambda, are fault-tolerant and designed to handle failures. In the case of Lambda, if a service invokes a Lambda function and there is a service disruption, Lambda invokes your function in a different Availability Zone. If your function throws an error, the Lambda service retries your function.\n\nSince the same event may be received more than once, functions should be designed to be [idempotent](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FIdempotence). This means that receiving the same event multiple times does not change the result beyond the first time the event was received.\n\nFor example, if a credit card transaction is attempted twice due to a retry, the Lambda function should process the payment on the first receipt. On the second retry, either the Lambda function should discard the event or the downstream service it uses should be idempotent.\n\nA Lambda function implements idempotency typically by using a DynamoDB table to track recently processed identifiers to determine if the transaction has been handled previously. The DynamoDB table usually implements a [Time To Live (TTL)](https:\u002F\u002Fdocs.aws.amazon.com\u002Famazondynamodb\u002Flatest\u002Fdeveloperguide\u002FTTL.html) value to expire items to limit the storage space used.\n\n![](\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fassets\u002Fimages\u002Fevent-driven-architectures-figure-12.png)\n\nFor failures within the custom code of a Lambda function, the service offers a number of features to help preserve and retry the event, and provide monitoring to capture that the failure has occurred. Using these approaches can help you develop workloads that are resilient to failure and improve the durability of events as they are processed by Lambda functions. These are explored further in [Debugging](debugging-ops) and [Monitoring and observability](monitoring-observability).\n\nTo learn more, read [General Design Principles](https:\u002F\u002Fdocs.aws.amazon.com\u002Fwellarchitected\u002Flatest\u002Fserverless-applications-lens\u002Fgeneral-design-principles.html) and [Serverless Computing – Architectural Considerations & Principles](https:\u002F\u002Fwww2.deloitte.com\u002Fcontent\u002Fdam\u002FDeloitte\u002Ftr\u002FDocuments\u002Ftechnology-media-telecommunications\u002FServerless%20Computing.pdf) by Deloitte Consulting.\n",[10,21,137,245,312,382,486,555],{"title":-1,"collapsible":11,"isCollapsed":12,"content":13},false,true,[14],{"title":15,"order":16,"time":17,"path":18,"id":19,"link":20},"Introduction",1,"4 min","intro","intro.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fintro",{"title":22,"collapsible":12,"isCollapsed":11,"content":23},"Event-driven 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