[{"data":1,"prerenderedAt":566},["ShallowReactive",2],{"content-svc-lambda-guides-aws-lambda-operator-guide-profile-functions":3},{"markdown":4,"frontMatterAttributes":5,"bodyRaw":8,"menu":9,"menuType":563,"isCollapseableMenu":12,"nextDocItem":513,"previousDocItem":503,"contributorPaths":564,"contentName":565,"slug":512,"readingTime":58},"\u003Cp>Choosing the memory allocated to Lambda functions is an optimization process that balances speed (duration) and cost. While you can manually run tests on functions by selecting different memory allocations and measuring the time taken to complete, the \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Falexcasalboni\u002Faws-lambda-power-tuning\">AWS Lambda Power Tuning\u003C\u002Fa> tool allows you to automate the process.\u003C\u002Fp>\n\u003Cp>This tool uses AWS Step Functions to run multiple concurrent versions of a Lambda function at different memory allocations and measure the performance. The input function is run in your AWS account, performing live HTTP calls and SDK interaction, to measure likely performance in a live production scenario. You can also implement a CI\u002FCD process to use this tool to automatically measure the performance of new functions you deploy.\u003C\u002Fp>\n\u003Cp>You can graph the results to visualize the performance and cost trade-off. In this example, you can see that a function has the lowest cost at 1024 MB and 1536 MB of memory, but the fastest execution at 3008 MB:\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fassets\u002Fimages\u002Fperf-optimize-figure-9.png\" alt=\"\">\u003C\u002Fp>\n\u003Cp>Generally, CPU-bound Lambda functions see the most benefit when memory increases, whereas network-bound see the least. This is because more memory provides greater computational capability, but it does not impact the response time of downstream services in network calls. Running the profiler on your functions provides insight into how your code performs at different memory allocations, allowing you to make better decisions about how to configure your functions.\u003C\u002Fp>\n\u003Cp>You can also use AWS Compute Optimizer to automate a cost-performance analysis for all the Lambda functions in an AWS account. This service evaluates functions that have run at least 50 times over the previous 14 days, and provides automatic recommendations for memory allocation. You can opt-in from the Cost Optimizer console to use this free recommendation engine.\u003C\u002Fp>\n",{"title":6,"order":7},"Profiling functions with AWS Lambda Power Tuning",81,"Choosing the memory allocated to Lambda functions is an optimization process that balances speed (duration) and cost. While you can manually run tests on functions by selecting different memory allocations and measuring the time taken to complete, the [AWS Lambda Power Tuning](https:\u002F\u002Fgithub.com\u002Falexcasalboni\u002Faws-lambda-power-tuning) tool allows you to automate the process.\n\nThis tool uses AWS Step Functions to run multiple concurrent versions of a Lambda function at different memory allocations and measure the performance. The input function is run in your AWS account, performing live HTTP calls and SDK interaction, to measure likely performance in a live production scenario. You can also implement a CI\u002FCD process to use this tool to automatically measure the performance of new functions you deploy.\n\nYou can graph the results to visualize the performance and cost trade-off. In this example, you can see that a function has the lowest cost at 1024 MB and 1536 MB of memory, but the fastest execution at 3008 MB:\n\n![](\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fassets\u002Fimages\u002Fperf-optimize-figure-9.png)\n\nGenerally, CPU-bound Lambda functions see the most benefit when memory increases, whereas network-bound see the least. This is because more memory provides greater computational capability, but it does not impact the response time of downstream services in network calls. Running the profiler on your functions provides insight into how your code performs at different memory allocations, allowing you to make better decisions about how to configure your functions.\n\nYou can also use AWS Compute Optimizer to automate a cost-performance analysis for all the Lambda functions in an AWS account. This service evaluates functions that have run at least 50 times over the previous 14 days, and provides automatic recommendations for memory allocation. You can opt-in from the Cost Optimizer console to use this free recommendation engine.",[10,21,139,247,314,384,488,555],{"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":12,"content":23},"Event-driven architectures",[24,30,37,43,49,55,62,68,74,80,86,92,98,104,110,116,122,128,133],{"title":22,"order":25,"time":26,"path":27,"id":28,"link":29},2,"1 min","event-driven-architectures","event-driven-architectures.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fevent-driven-architectures",{"title":31,"order":32,"time":33,"path":34,"id":35,"link":36},"How Lambda fits into the event-driven paradigm",3,"3 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