[{"data":1,"prerenderedAt":401},["ShallowReactive",2],{"content-svc-lambda-guides-effectively-running-java-on-serverless-graviton":3},{"markdown":4,"frontMatterAttributes":5,"bodyRaw":15,"menu":16,"menuType":392,"isCollapseableMenu":19,"nextDocItem":190,"previousDocItem":166,"contributorPaths":393,"contentName":400,"slug":186,"readingTime":33},"\u003Cp>Migrating to Graviton2 processors for AWS Lambda functions is easy as clicking a radio button in the AWS Management console.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Feffectively-running-java-on-serverless\u002Fassets\u002Fgraviton_console.png\" alt=\"GravitonConsole\">\u003C\u002Fp>\n\u003Cp>You edit the underlying architecture from \u003Ccode>x86_64\u003C\u002Fcode> to \u003Ccode>arm64\u003C\u002Fcode> and save. That's it. Future invocations of the Lambda \nfunctions will use Graviton2 processors that allow you to get up to 34% better price performance. You can use Lambda SnapStart\nwith Graviton2 processors too.\u003C\u002Fp>\n\u003Ch4>AWS SAM\u003C\u002Fh4>\n\u003Cp>You can enable Graviton2 in SAM template as following:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-yaml\">MyArmFunction:\n  Type: AWS::Lambda::Function\n  Properties:\n    Runtime: nodejs18.x\n    Code: src\u002F\n    Architectures:\n  \t  - arm64\n    Handler: lambda.handler\n    MemorySize: 512\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch4>Visualize Graviton2 savings in AWS Lambda Power Tuning tool\u003C\u002Fh4>\n\u003Cp>The \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Falexcasalboni\u002Faws-lambda-power-tuning\">AWS Lambda Power Tuning\u003C\u002Fa> open-source project runs your functions \nusing different settings to suggest a configuration to minimize costs and maximize performance. \nThe tool allows you to compare two results on the same chart and incorporate arm64-based pricing. \nThis is useful to compare two versions of the same function, one using x86 and the other arm64.\u003C\u002Fp>\n\u003Cp>For example, you can compare the performance of the x86_64 and arm64 Lambda functions and see that the arm64 Lambda function is 12% cheaper to run:\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Feffectively-running-java-on-serverless\u002Fassets\u002Fgraviton_power_tuning.png\" alt=\"GravitonSavings\">\u003C\u002Fp>\n\u003Ch4>Considerations\u003C\u002Fh4>\n\u003Cp>If your Java-based Lambda functions don’t use architecture-specific binaries, \nincluding in their dependencies, you can switch from one architecture to the other. \u003C\u002Fp>\n",{"title":6,"order":7,"authors":8,"resources":10},"Using Graviton2",6,[9],"Dhiraj Mahapatro",[11],{"link":12,"text":13,"type":14},"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fcompute\u002Fmigrating-aws-lambda-functions-to-arm-based-aws-graviton2-processors\u002F","Using Arm-based Graviton2 processors","blog","Migrating to Graviton2 processors for AWS Lambda functions is easy as clicking a radio button in the AWS Management console.\n\n![GravitonConsole](\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Feffectively-running-java-on-serverless\u002Fassets\u002Fgraviton_console.png)\n\nYou edit the underlying architecture from `x86_64` to `arm64` and save. That's it. Future invocations of the Lambda \nfunctions will use Graviton2 processors that allow you to get up to 34% better price performance. You can use Lambda SnapStart\nwith Graviton2 processors too.\n\n#### AWS SAM\nYou can enable Graviton2 in SAM template as following:\n\n```yaml\nMyArmFunction:\n  Type: AWS::Lambda::Function\n  Properties:\n    Runtime: nodejs18.x\n    Code: src\u002F\n    Architectures:\n  \t  - arm64\n    Handler: lambda.handler\n    MemorySize: 512\n```\n\n#### Visualize Graviton2 savings in AWS Lambda Power Tuning tool\n\nThe [AWS Lambda Power Tuning](https:\u002F\u002Fgithub.com\u002Falexcasalboni\u002Faws-lambda-power-tuning) open-source project runs your functions \nusing different settings to suggest a configuration to minimize costs and maximize performance. \nThe tool allows you to compare two results on the same chart and incorporate arm64-based pricing. \nThis is useful to compare two versions of the same function, one using x86 and the other arm64.\n\nFor example, you can compare the performance of the x86_64 and arm64 Lambda functions and see that the arm64 Lambda function is 12% cheaper 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