[{"data":1,"prerenderedAt":566},["ShallowReactive",2],{"content-svc-lambda-guides-aws-lambda-operator-guide-computing-power":3},{"markdown":4,"frontMatterAttributes":5,"bodyRaw":8,"menu":9,"menuType":563,"isCollapseableMenu":12,"nextDocItem":507,"previousDocItem":496,"contributorPaths":564,"contentName":565,"slug":506,"readingTime":33},"\u003Cp>Memory is the principal lever available to Lambda developers for controlling the performance of a function. You can configure the amount of memory allocated to a Lambda function, between 128 MB and 10,240 MB. The Lambda console defaults new functions to the smallest setting and many developers also choose 128 MB for their functions.\u003C\u002Fp>\n\u003Cp>However, 128 MB should usually only be used for the simplest of Lambda functions, such as those that transform and route events to other AWS services. If the function imports libraries or Lambda layers, or interacts with data loaded from S3 or EFS, it’s likely to be more performant with a higher memory allocation.\u003C\u002Fp>\n\u003Cp>The amount of memory also determines the amount of virtual CPU available to a function. Adding more memory proportionally increases the amount of CPU, increasing the overall computational power available. If a function is CPU-, network- or memory-bound, then changing the memory setting can dramatically improve its performance.\u003C\u002Fp>\n\u003Cp>Since the Lambda service charges for the total amount of gigabyte-seconds consumed by a function, increasing the memory has an impact on overall cost if the total duration stays constant. Gigabyte-seconds are the product of total memory (in gigabytes) and duration (in seconds). However, in many cases, increasing the memory available causes a decrease in the duration. As a result, the overall cost increase may be negligible or may even decrease.\u003C\u002Fp>\n\u003Cp>For example, 1000 invocations of a function that computes prime numbers may have the following average durations at different memory levels:\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Memory\u003C\u002Fth>\n\u003Cth>Duration\u003C\u002Fth>\n\u003Cth>Cost\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>128 MB\u003C\u002Ftd>\n\u003Ctd>11.722 s\u003C\u002Ftd>\n\u003Ctd>$0.024628\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>512 MB\u003C\u002Ftd>\n\u003Ctd>6.678 s\u003C\u002Ftd>\n\u003Ctd>$0.028035\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>1024 MB\u003C\u002Ftd>\n\u003Ctd>3.194 s\u003C\u002Ftd>\n\u003Ctd>$0.026830\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>1536 MB\u003C\u002Ftd>\n\u003Ctd>1.465 s\u003C\u002Ftd>\n\u003Ctd>$0.024638\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Cp>In this case, at 128 MB, the function takes 11.722 seconds on average to complete, at a cost of $0.024628 for 1,000 invocations. When the memory is increased to 1536 MB, the duration average drops to 1.465 seconds, so the cost is $0.024638. For a one-thousandth of a cent cost difference, the function has a 10-fold improvement in performance.\u003C\u002Fp>\n\u003Cp>As discussed in \u003Ca href=\"monitoring-observability\">Monitoring and observability\u003C\u002Fa>, you can monitor functions with CloudWatch and set alarms if memory consumption is approaching the configured maximum. This can help identify memory-bound functions. For CPU-bound and IO-bound functions, monitoring the duration can often provide more insight. In these cases, increasing the memory can help resolve the compute or network bottlenecks.\u003C\u002Fp>\n",{"title":6,"order":7},"Memory and computing power",80,"Memory is the principal lever available to Lambda developers for controlling the performance of a function. You can configure the amount of memory allocated to a Lambda function, between 128 MB and 10,240 MB. The Lambda console defaults new functions to the smallest setting and many developers also choose 128 MB for their functions.\n\nHowever, 128 MB should usually only be used for the simplest of Lambda functions, such as those that transform and route events to other AWS services. If the function imports libraries or Lambda layers, or interacts with data loaded from S3 or EFS, it’s likely to be more performant with a higher memory allocation.\n\nThe amount of memory also determines the amount of virtual CPU available to a function. Adding more memory proportionally increases the amount of CPU, increasing the overall computational power available. If a function is CPU-, network- or memory-bound, then changing the memory setting can dramatically improve its performance.\n\nSince the Lambda service charges for the total amount of gigabyte-seconds consumed by a function, increasing the memory has an impact on overall cost if the total duration stays constant. Gigabyte-seconds are the product of total memory (in gigabytes) and duration (in seconds). However, in many cases, increasing the memory available causes a decrease in the duration. As a result, the overall cost increase may be negligible or may even decrease.\n\nFor example, 1000 invocations of a function that computes prime numbers may have the following average durations at different memory levels:\n\n|Memory |Duration |Cost|\n|--|--|--|\n|128 MB |11.722 s |$0.024628|\n|512 MB |6.678 s |$0.028035|\n|1024 MB |3.194 s |$0.026830|\n|1536 MB |1.465 s |$0.024638|\n\nIn this case, at 128 MB, the function takes 11.722 seconds on average to complete, at a cost of $0.024628 for 1,000 invocations. When the memory is increased to 1536 MB, the duration average drops to 1.465 seconds, so the cost is $0.024638. For a one-thousandth of a cent cost difference, the function has a 10-fold improvement in performance.\n\nAs discussed in [Monitoring and observability](monitoring-observability), you can monitor functions with CloudWatch and set alarms if memory consumption is approaching the configured maximum. This can help identify memory-bound functions. For CPU-bound and IO-bound functions, monitoring the duration can often provide more insight. In these cases, increasing the memory can help resolve the compute or network bottlenecks.",[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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