When using Lambda, I try to follow best practices for retries and dead-letter-queues (DLQs) or error destinations, but there are so many ways to do it I often find myself needing to look them up. The primary compute component of serverless in AWS is AWS Lambda, so as you might imagine, I use it a lot. Improve startup performance for Java runtimes by up to 10x at no extra cost, typically with no changes to your function code.As you may know, I’m a big fan of serverless in AWS. File system accessĬonfigure a function to mount an Amazon Elastic File System (Amazon EFS) to a local directory, so that your function code can access and modify shared resources safely and at high concurrency. Verify that only approved developers publish unaltered, trusted code in your Lambda functions Private networkingĬreate a private network for resources such as databases, cache instances, or internal services. Concurrency and scaling controlsĪpply fine-grained control over the scaling and responsiveness of your production applications. Response streamingĬonfigure your Lambda function URLs to stream response payloads back to clients from Node.js functions, to improve time to first byte (TTFB) performance or to return larger payloads. Function URLsĪdd a dedicated HTTP(S) endpoint to your Lambda function. Lambda extensionsĪugment your Lambda functions with tools for monitoring, observability, security, and governance. Package libraries and other dependencies to reduce the size of deployment archives and makes it faster to deploy your code. Image so that you can reuse your existing container tooling or deploy larger workloads that rely on sizable dependencies, such as machine learning. Container imagesĬreate a container image for a Lambda function by using an AWS provided base image or an alternative base Manage the deployment of your functions with versions, so that, for example, a new function can be used for beta testing without affecting users of the stable production version. Use environment variables to adjust your function's behavior without updating code. If you do need to manage your compute resources, AWS has other compute services to consider, suchĬonfigure your Lambda function using the console or AWS CLI. Lambda performs operational and administrative activities on your behalf, including managingĬapacity, monitoring, and logging your Lambda functions. Because Lambda manages these resources, youĬannot log in to compute instances or customize the operating system on provided Lambda manages the compute fleet that offers aīalance of memory, CPU, network, and other resources to run your code. When using Lambda, you are responsible only for your code. Use AWS Amplify to easily integrate with your iOS, Android, Web, and React Native frontends. Mobile backends: Build backends using Lambda and Amazon API Gateway to authenticate and process API requests. IoT backends: Build serverless backends using Lambda to handle web, mobile, IoT, and third-party API requests. Web applications: Combine Lambda with other AWS services to build powerful web applications that automatically scale up and down and run in a highly available configuration across multiple data centers. Stream processing: Use Lambda and Amazon Kinesis to process real-time streaming data for application activity tracking, transaction order processing, clickstream analysis, data cleansing, log filtering, indexing, social media analysis, Internet of Things (IoT) device data telemetry, and metering. File processing: Use Amazon Simple Storage Service (Amazon S3) to trigger Lambda data processing in real time after an upload.
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