[{"data":1,"prerenderedAt":566},["ShallowReactive",2],{"content-svc-lambda-guides-aws-lambda-operator-guide-throttling":3},{"markdown":4,"frontMatterAttributes":5,"bodyRaw":8,"menu":9,"menuType":563,"isCollapseableMenu":12,"nextDocItem":364,"previousDocItem":354,"contributorPaths":564,"contentName":565,"slug":363,"readingTime":58},"\u003Cp>In this example, there is an SQS queue and a processing Lambda function. The unreserved account concurrency is 100. The processing function simulates 30-second invocations to process messages from a queue. The function has a batch size of 1, meaning that a single invocation processes only 1 message every 30 seconds:\u003C\u002Fp>\n\u003Cpre>\u003Ccode class=\"language-js\">const doWork = (ms) => new Promise(resolve => setTimeout(resolve, ms))\n\nexports.handler = async (event) => {\n    await doWork(30000)\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>Items are arriving in the queue more rapidly than messages are processed. The Lambda service scales up to consume the unreserved concurrency of 100, and then throttling occurs. You can see this pattern in the CloudWatch metrics for the function:\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fassets\u002Fimages\u002Fdebugging-ops-figure-10.png\" alt=\"\">\u003C\u002Fp>\n\u003Cp>CloudWatch metrics for the function show no errors, but the Concurrent executions chart shows that the maximum concurrency is reached. As a result, the Throttles chart shows the throttling in place, since SQS messages are still available.\u003C\u002Fp>\n\u003Cp>You can detect throttling by using CloudWatch alarms, and setting an alarm anytime the throttling metric for a function is greater than 0. There are several approaches to resolving this issue:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Request a concurrency increase from AWS Support in this Region.\u003C\u002Fli>\n\u003Cli>Identify performance issues in the function to improve the speed of processing and therefore improve throughput.\u003C\u002Fli>\n\u003Cli>Increase the batch size of the function, so more messages are processed by each function invocation.\u003C\u002Fli>\n\u003C\u002Ful>\n",{"title":6,"order":7},"Identifying and managing throttling",57,"In this example, there is an SQS queue and a processing Lambda function. The unreserved account concurrency is 100. The processing function simulates 30-second invocations to process messages from a queue. The function has a batch size of 1, meaning that a single invocation processes only 1 message every 30 seconds:\n\n```js\nconst doWork = (ms) => new Promise(resolve => setTimeout(resolve, ms))\n\nexports.handler = async (event) => {\n    await doWork(30000)\n}\n```\n\nItems are arriving in the queue more rapidly than messages are processed. The Lambda service scales up to consume the unreserved concurrency of 100, and then throttling occurs. You can see this pattern in the CloudWatch metrics for the function:\n\n![](\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Faws-lambda-operator-guide\u002Fassets\u002Fimages\u002Fdebugging-ops-figure-10.png)\n\nCloudWatch metrics for the function show no errors, but the Concurrent executions chart shows that the maximum concurrency is reached. As a result, the Throttles chart shows the throttling in place, since SQS messages are still available.\n\nYou can detect throttling by using CloudWatch alarms, and setting an alarm anytime the throttling metric for a function is greater than 0. 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