[{"data":1,"prerenderedAt":156},["ShallowReactive",2],{"content-svc-lambda-guides-serverless-stream-processing-with-lambda-4-5-KDS-monitoring":3},{"markdown":4,"frontMatterAttributes":5,"bodyRaw":10,"menu":11,"menuType":148,"isCollapseableMenu":14,"nextDocItem":149,"previousDocItem":135,"contributorPaths":150,"contentName":155,"slug":147,"readingTime":120},"\u003Cp>There are 2 monitoring levels for Kinesis data streams.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Basic (stream-level)\u003C\u002Fstrong>: Stream-level data is sent automatically every minute at no charge.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Enhanced (shard-level)\u003C\u002Fstrong>: Shard-level data is sent every minute for an additional cost. To get this level of data, you must specifically enable it for the stream using the \u003Ccode>EnableEnhancedMonitoring\u003C\u002Fcode> operation.\nThis allows you to monitor the hot and cold shards, example the over utilized or underutilized shards.\nYou can view the output of additional metrics in CloudWatch when Enhanced shard level metric is enabled.\u003C\u002Fp>\n\u003Cp>\u003Cimg src=\"\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fkds\u002Fassets\u002Fkds-4-5-cloudwatch.png\" alt=\"KDS CW Metrics\">\u003C\u002Fp>\n\u003Ch4>Monitor IteratorAgeMilliseconds value\u003C\u002Fh4>\n\u003Cp>When this value increases, it could be caused by one of these reasons:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Slow record processing\u003C\u002Fli>\n\u003Cli>Read throttles\u003C\u002Fli>\n\u003Cli>AWS Lambda function error\u003C\u002Fli>\n\u003Cli>Connection timeout\u003C\u002Fli>\n\u003Cli>Uneven data distribution among shards\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Frepost.aws\u002Fknowledge-center\u002Fkinesis-data-streams-iteratorage-metric\">IteratorAgeMilliseconds value in Kinesis Data Streams\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch4>Kinesis Data Streams API calls in CloudTrail\u003C\u002Fh4>\n\u003Cp>Amazon KDS is integrated with AWS CloudTrail which shows\u002Flogs record of actions taken by a user, role, or an AWS service in Kinesis Data Streams. This includes code and console related calls\u002Factions.\nCloudTrail captures all API calls for Kinesis Data Streams as events.\nIf you created\u002Fconfigured a trail it will record it to Amazon S3 Bucket that you have specified and if not, you can still see it in the event history via console\u003C\u002Fp>\n\u003Cp>You can configure other AWS services to further analyze and act upon the event data collected in CloudTrail logs.\u003C\u002Fp>\n\u003Ch4>CloudTrail Supported Services and Integrations\u003C\u002Fh4>\n\u003Cp>CloudTrail supports logging events for many AWS services. You can find the specifics for each supported service in that service's guide. Links to those service-specific topics are provided below. In addition, some AWS services can be used to analyze and act upon data collected in CloudTrail logs. You can browse an overview of those service integrations here.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Fawscloudtrail\u002Flatest\u002Fuserguide\u002Fcloudtrail-aws-service-specific-topics.html#cloudtrail-aws-service-specific-topics-integrations\">CloudTrail - Services and Integrations\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Fstreams\u002Flatest\u002Fdev\u002Flogging-using-cloudtrail.html\">Logging API Calls with CloudTrail\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch4>Configuring Amazon SNS notifications for CloudTrail\u003C\u002Fh4>\n\u003Cp>You can enable notification by CloudTrail when new log files published to your Amazon S3 bucket. You can manage notifications using Amazon Simple Notification Service (Amazon SNS) and notifications are optional.\nIf notifications are needed, configure CloudTrail to send update information to an Amazon SNS topic whenever a new log file has been sent. You can subscribe to the topic to receive the messages.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Fawscloudtrail\u002Flatest\u002Fuserguide\u002Fconfigure-sns-notifications-for-cloudtrail.html\">CloudTrail + SNS\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch4>Monitor using Kinesis Client Library (KCL)\u003C\u002Fh4>\n\u003Cp>KCL publishes custom Amazon CloudWatch metrics on your behalf and can be viewed from CloudWatch console.\nThe metrics will be published using the name of your KCL application as the namespace. You can view these metrics in Custom Metrics under CloudWatch.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Fstreams\u002Flatest\u002Fdev\u002Fmonitoring-with-kcl.html\">Monitoring the KCL\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch4>Lambda Monitoring\u003C\u002Fh4>\n\u003Ch5>Monitor lambda concurrency metrics\u003C\u002Fh5>\n\u003Cp>The Lambda function emits concurrency metrics as an aggregate count of the number of instances processing events. This will help you to monitor if you're close to hitting concurrency limits.\u003C\u002Fp>\n\u003Cp>The granularity for each metric is 1 minute for both following metrics:\u003C\u002Fp>\n\u003Cp>\u003Cstrong>General concurrency metrics:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Ccode>ConcurrentExecutions\u003C\u002Fcode> – The number of active concurrent invocations at a given point in time.\u003C\u002Fli>\n\u003Cli>\u003Ccode>UnreservedConcurrentExecutions\u003C\u002Fcode> – The number of active concurrent invocations that are using unreserved concurrency.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>Provisioned concurrency metrics:\u003C\u002Fstrong>\nUsually, you want to track this if you have enabled provisioned concurrency and want to track the usage.\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Ccode>ProvisionedConcurrentExecutions\u003C\u002Fcode> – The number of execution environment instances that are actively processing an invocation on provisioned concurrency.\u003C\u002Fli>\n\u003Cli>\u003Ccode>ProvisionedConcurrentInvocations\u003C\u002Fcode> – The number of times Lambda invokes your function code using provisioned concurrency.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Fmonitoring-metrics.html\">Lambda function metrics\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch5>Monitor lambda using CloudWatch\u003C\u002Fh5>\n\u003Cp>You can monitor lambda using the below options:\u003C\u002Fp>\n\u003Ch6>CloudWatch Log Insights\u003C\u002Fh6>\n\u003Cp>You can interactively search and analyze your log data in Amazon CloudWatch Logs. You can perform queries to help you more efficiently and effectively respond to operational issues.\nYou can search and analyze log data using a specialized query syntax over multiple log groups and provides filtering using glob and regular expressions pattern matching.\u003C\u002Fp>\n\u003Cp>CloudWatch Logs Insights provides sample queries, command descriptions, query autocompletion, and log field discovery to help you get started. Sample queries are included for several types of AWS service logs.\nIt automatically discovers fields in logs from AWS services AWS Lambda, Amazon VPC, and any application or custom log that emits log events as JSON.\u003C\u002Fp>\n\u003Cp>A single request can query up to 50 log groups. Note that queries time out after 60 minutes, query results are available for 7 days.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fcompute\u002Foperating-lambda-using-cloudwatch-logs-insights\u002F\">CloudWatch Insights\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch6>Lambda Insights\u003C\u002Fh6>\n\u003Cp>Lambda Insights collects, aggregates and summarizes system level metrics and also collects, aggregates and summarizes diagnostic information such as cold starts.\nYou can use the multi-function overview on the Lambda Insights dashboard to identify and detect compute memory anomalies with your function. If the multi-function overview indicates that a function is using a large amount of memory, you can view detailed memory utilization metrics in the Memory Usage pane. You can then go to the Metrics dashboard to enable anomaly detection or create an alarm.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Fmonitoring-insights.html\">Lambda Insights\u003C\u002Fa> to learn more.\u003C\u002Fp>\n\u003Ch5>AWS X-Ray\u003C\u002Fh5>\n\u003Cp>You can use AWS X-Ray to visualize application components, identify performance issues, and troubleshoot requests that resulted in an error.\nYour Lambda functions sends trace data to X-Ray, and X-Ray processes the data to generate a service map and searchable trace summaries.\nYou can track requests made to your applications; X-Ray can create a map of services used by your application and you can view connections among your services in your application and you can create a dependency tree and detect latency or errors.\u003C\u002Fp>\n\u003Cp>Please refer to \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fcompute\u002Fdebugging-snapstart-enabled-lambda-functions-made-easy-with-aws-x-ray\u002F\">Debug with AWS X-Ray\u003C\u002Fa> to learn more.\u003C\u002Fp>\n",{"title":6,"weight":7,"learningGoals":8},"Kinesis Data Streams Monitoring",68,[9],"Learn Amazon KDS & Lambda monitoring methodologies","There are 2 monitoring levels for Kinesis data streams.\n\n**Basic (stream-level)**: Stream-level data is sent automatically every minute at no charge.\n\n**Enhanced (shard-level)**: Shard-level data is sent every minute for an additional cost. To get this level of data, you must specifically enable it for the stream using the `EnableEnhancedMonitoring` operation.\nThis allows you to monitor the hot and cold shards, example the over utilized or underutilized shards.\nYou can view the output of additional metrics in CloudWatch when Enhanced shard level metric is enabled.\n\n![KDS CW Metrics](\u002Fassets\u002Fexternal\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fkds\u002Fassets\u002Fkds-4-5-cloudwatch.png)\n\n#### Monitor IteratorAgeMilliseconds value\n\nWhen this value increases, it could be caused by one of these reasons:\n\n- Slow record processing\n- Read throttles\n- AWS Lambda function error\n- Connection timeout\n- Uneven data distribution among shards\n\nPlease refer to [IteratorAgeMilliseconds value in Kinesis Data Streams](https:\u002F\u002Frepost.aws\u002Fknowledge-center\u002Fkinesis-data-streams-iteratorage-metric) to learn more.\n\n#### Kinesis Data Streams API calls in CloudTrail\n\nAmazon KDS is integrated with AWS CloudTrail which shows\u002Flogs record of actions taken by a user, role, or an AWS service in Kinesis Data Streams. This includes code and console related calls\u002Factions.\nCloudTrail captures all API calls for Kinesis Data Streams as events.\nIf you created\u002Fconfigured a trail it will record it to Amazon S3 Bucket that you have specified and if not, you can still see it in the event history via console\n\nYou can configure other AWS services to further analyze and act upon the event data collected in CloudTrail logs.\n\n#### CloudTrail Supported Services and Integrations\n\nCloudTrail supports logging events for many AWS services. You can find the specifics for each supported service in that service's guide. Links to those service-specific topics are provided below. In addition, some AWS services can be used to analyze and act upon data collected in CloudTrail logs. You can browse an overview of those service integrations here.\n\nPlease refer to [CloudTrail - Services and Integrations](https:\u002F\u002Fdocs.aws.amazon.com\u002Fawscloudtrail\u002Flatest\u002Fuserguide\u002Fcloudtrail-aws-service-specific-topics.html#cloudtrail-aws-service-specific-topics-integrations) to learn more.\n\nPlease refer to [Logging API Calls with CloudTrail](https:\u002F\u002Fdocs.aws.amazon.com\u002Fstreams\u002Flatest\u002Fdev\u002Flogging-using-cloudtrail.html) to learn more.\n\n#### Configuring Amazon SNS notifications for CloudTrail\n\nYou can enable notification by CloudTrail when new log files published to your Amazon S3 bucket. You can manage notifications using Amazon Simple Notification Service (Amazon SNS) and notifications are optional.\nIf notifications are needed, configure CloudTrail to send update information to an Amazon SNS topic whenever a new log file has been sent. You can subscribe to the topic to receive the messages.\n\nPlease refer to [CloudTrail + SNS](https:\u002F\u002Fdocs.aws.amazon.com\u002Fawscloudtrail\u002Flatest\u002Fuserguide\u002Fconfigure-sns-notifications-for-cloudtrail.html) to learn more.\n\n#### Monitor using Kinesis Client Library (KCL)\n\nKCL publishes custom Amazon CloudWatch metrics on your behalf and can be viewed from CloudWatch console.\nThe metrics will be published using the name of your KCL application as the namespace. You can view these metrics in Custom Metrics under CloudWatch.\n\nPlease refer to [Monitoring the KCL](https:\u002F\u002Fdocs.aws.amazon.com\u002Fstreams\u002Flatest\u002Fdev\u002Fmonitoring-with-kcl.html) to learn more.\n\n#### Lambda Monitoring\n\n##### Monitor lambda concurrency metrics\n\nThe Lambda function emits concurrency metrics as an aggregate count of the number of instances processing events. This will help you to monitor if you're close to hitting concurrency limits.\n\nThe granularity for each metric is 1 minute for both following metrics:\n\n**General concurrency metrics:**\n\n- `ConcurrentExecutions` – The number of active concurrent invocations at a given point in time.\n- `UnreservedConcurrentExecutions` – The number of active concurrent invocations that are using unreserved concurrency.\n\n**Provisioned concurrency metrics:**\nUsually, you want to track this if you have enabled provisioned concurrency and want to track the usage.\n\n- `ProvisionedConcurrentExecutions` – The number of execution environment instances that are actively processing an invocation on provisioned concurrency.\n- `ProvisionedConcurrentInvocations` – The number of times Lambda invokes your function code using provisioned concurrency.\n\nPlease refer to [Lambda function metrics](https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Fmonitoring-metrics.html) to learn more.\n\n##### Monitor lambda using CloudWatch\n\nYou can monitor lambda using the below options:\n\n###### CloudWatch Log Insights\n\nYou can interactively search and analyze your log data in Amazon CloudWatch Logs. You can perform queries to help you more efficiently and effectively respond to operational issues.\nYou can search and analyze log data using a specialized query syntax over multiple log groups and provides filtering using glob and regular expressions pattern matching.\n\nCloudWatch Logs Insights provides sample queries, command descriptions, query autocompletion, and log field discovery to help you get started. Sample queries are included for several types of AWS service logs.\nIt automatically discovers fields in logs from AWS services AWS Lambda, Amazon VPC, and any application or custom log that emits log events as JSON.\n\nA single request can query up to 50 log groups. Note that queries time out after 60 minutes, query results are available for 7 days.\n\nPlease refer to [CloudWatch Insights](https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fcompute\u002Foperating-lambda-using-cloudwatch-logs-insights\u002F) to learn more.\n\n###### Lambda Insights\n\nLambda Insights collects, aggregates and summarizes system level metrics and also collects, aggregates and summarizes diagnostic information such as cold starts.\nYou can use the multi-function overview on the Lambda Insights dashboard to identify and detect compute memory anomalies with your function. If the multi-function overview indicates that a function is using a large amount of memory, you can view detailed memory utilization metrics in the Memory Usage pane. You can then go to the Metrics dashboard to enable anomaly detection or create an alarm.\n\nPlease refer to [Lambda Insights](https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Fmonitoring-insights.html) to learn more.\n\n##### AWS X-Ray\n\nYou can use AWS X-Ray to visualize application components, identify performance issues, and troubleshoot requests that resulted in an error.\nYour Lambda functions sends trace data to X-Ray, and X-Ray processes the data to generate a service map and searchable trace summaries.\nYou can track requests made to your applications; X-Ray can create a map of services used by your application and you can view connections among your services in your application and you can create a dependency tree and detect latency or errors.\n\nPlease refer to [Debug with AWS X-Ray](https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fcompute\u002Fdebugging-snapstart-enabled-lambda-functions-made-easy-with-aws-x-ray\u002F) to learn more.\n",[12,43,100],{"title":13,"collapsible":14,"isCollapsed":15,"content":16},"Introduction",false,true,[17,23,33],{"title":13,"order":18,"time":19,"path":20,"id":21,"link":22},1,"2 min","1-introduction","1-introduction.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F1-introduction",{"title":24,"order":25,"callout":26,"time":19,"path":30,"id":31,"link":32},"Getting started with streaming data",2,{"title":27,"description":28,"link":29},"Watch the video","This video will help you get started with Amazon Kinesis Data Streams, a massively scalable, durable, and low-cost streaming data service that enables you to continuously capture gigabytes of data per second from hundreds of thousands of sources, such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events.","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1I1DcJvmd4w","2-getting-started","2-getting-started.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F2-getting-started",{"title":34,"order":35,"callout":36,"time":19,"path":40,"id":41,"link":42},"Event source mapping for Lambda",3,{"title":37,"description":38,"link":39},"Read the docs","Refer to the AWS Lambda documentation for detailed explanations of Lambda event source mappings","https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Finvocation-eventsourcemapping.html","3-event-source-mapping","3-event-source-mapping.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F3-event-source-mapping",{"title":44,"collapsible":14,"isCollapsed":15,"content":45},"Streaming processing with MSK",[46,65,73,80,87,93],{"title":47,"order":48,"youtubeVideoID":49,"resources":50,"callout":58,"time":19,"path":62,"id":63,"link":64},"MSK as event source for Lambda",51,"JKvIypfEiok",[51,55],{"link":52,"type":53,"text":54},"https:\u002F\u002Faws.amazon.com\u002Flambda\u002F","Text","AWS Lambda",{"link":56,"type":53,"text":57},"https:\u002F\u002Faws.amazon.com\u002Fserverless\u002F","Serverless on AWS",{"title":59,"description":60,"link":61},"Follow the Kafka with Lambda learning guide","Refer to the deep dive learning guide for using Kafka with Lambda","https:\u002F\u002Fserverlessland.com\u002Fcontent\u002Fguides\u002Flambda-kafka\u002Fintroduction","msk-stream-processing","msk-stream-processing.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fmsk-stream-processing",{"title":66,"order":67,"callout":68,"time":69,"path":70,"id":71,"link":72},"Deep dive into Lambda ESM for MSK",50,{"title":59,"description":60,"link":61},"1 min","msk-esm-deep-dive","msk-esm-deep-dive.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fmsk-esm-deep-dive",{"title":74,"order":75,"callout":76,"time":19,"path":77,"id":78,"link":79},"Ordered processing of messages",52,{"title":59,"description":60,"link":61},"msk-ordered-processing","msk-ordered-processing.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fmsk-ordered-processing",{"title":81,"order":82,"callout":83,"time":69,"path":84,"id":85,"link":86},"Scaling of consumers",54,{"title":59,"description":60,"link":61},"msk-scaling-consumers","msk-scaling-consumers.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fmsk-scaling-consumers",{"title":88,"order":89,"time":69,"path":90,"id":91,"link":92},"Authentication options for Lambda consumers",56,"msk-lambda-auth-options","msk-lambda-auth-options.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fmsk-lambda-auth-options",{"title":94,"order":95,"callout":96,"time":69,"path":97,"id":98,"link":99},"Error handling in Lambda functions",58,{"title":59,"description":60,"link":61},"msk-lambda-error-handling","msk-lambda-error-handling.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002Fmsk-lambda-error-handling",{"title":101,"collapsible":14,"isCollapsed":14,"content":102},"Streaming processing with KDS",[103,112,124,135,143],{"title":104,"weight":105,"learningGoals":106,"time":108,"path":109,"id":110,"link":111},"Kinesis Data Streams (KDS) - Key Concepts and Terminologies",60,[107],"Learn key concepts and terminologies of Amazon KDS","4 min","4-1-KDS-key-concepts-terms","4-1-KDS-key-concepts-terms.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F4-1-KDS-key-concepts-terms",{"title":113,"weight":114,"learningGoals":115,"resources":117,"time":120,"path":121,"id":122,"link":123},"Amazon Kinesis Data Streams - Integration with AWS Lambda",62,[116],"Amazon KDS integration with AWS Lambda for Serverless stream processing",[118,119],"https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Finvocation-eventsourcemapping.html#invocation-eventsourcemapping-batching","https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fcompute\u002Fnew-aws-lambda-scaling-controls-for-kinesis-and-dynamodb-event-sources\u002F","5 min","4-2-KDS-lambda-intg","4-2-KDS-lambda-intg.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F4-2-KDS-lambda-intg",{"title":125,"weight":126,"learningGoals":127,"resources":129,"time":131,"path":132,"id":133,"link":134},"Amazon KDS - Error handling with Lambda ESM",64,[128],"How to handle errors when configuring Amazon KDS as Lambda ESM",[130],"https:\u002F\u002Fdocs.aws.amazon.com\u002Flambda\u002Flatest\u002Fdg\u002Fwith-kinesis.html","3 min","4-3-KDS-lambda-error-handling","4-3-KDS-lambda-error-handling.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F4-3-KDS-lambda-error-handling",{"title":136,"weight":137,"learningGoals":138,"time":120,"path":140,"id":141,"link":142},"Kinesis Data Streams Scaling- Key Concepts and Terminologies",66,[139],"Learn key concepts and terminologies of Amazon KDS Scaling","4-4-KDS-scaling","4-4-KDS-scaling.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F4-4-KDS-scaling",{"title":6,"weight":7,"learningGoals":144,"time":120,"path":145,"id":146,"link":147},[9],"4-5-KDS-monitoring","4-5-KDS-monitoring.md","\u002Fcontent\u002Fservice\u002Flambda\u002Fguides\u002Fserverless-stream-processing-with-lambda\u002F4-5-KDS-monitoring","LIST",null,[151,152,153,154],"content\u002Fcontributors\u002Fmithun-mallick.json","content\u002Fcontributors\u002Fkevin-schwarz.json","content\u002Fcontributors\u002Fnaren-sithambaram.json","content\u002Fcontributors\u002Fprasanna-s-krishnan.json","Serverless Stream Processing with Lambda",1792665273675]