![]() ![]() Source for AWS Glue and Amazon Quicksight I want to quote the table to show the differences between these options and common use-cases to help you choose the right one for your own applications. To learn more, see a great blog post, Choosing between AWS Lambda data storage options in web apps, written by my colleague James Beswick. ![]() To learn more, see Configuring function options in the AWS Documentation.Īs a review, AWS Lambda provides a comprehensive range of storage options. You can configure ephemeral storage using Lambda API via AWS SDK and AWS CloudFormation. $ aws lambda update-function-configuration -function-name PDFGenerator \ With AWS Command Line Interface (AWS CLI), you can update your desired size of ephemeral storage using the update-function-configuration command. When you click the Edit button, you can configure the ephemeral storage from 512 MB to 10,240 MB in 1 MB increments for your Lambda functions. Since customers could not cache larger data locally in the Lambda execution environment, every function invoke had to read data in parallel, which made scaling out harder for customers. With the previous limit of 512 MB, customers had to selectively load data from Amazon Simple Storage Service (Amazon S3) and Amazon EFS, or increase the allocated function memory and thus increase their cost, just to handle large objects downloaded from Amazon S3. Data-intensive applications require large amounts of temporary data specific to the invocation or cached data that can be reused for all invocation in the same execution environment in a highly performant manner. ![]() However, extract, transform, and load (ETL) jobs and content generation workflows such as creating PDF files or media transcoding require fast, scalable local storage to process large amounts of data quickly. While AWS Lambda includes a 512 MB temporary file system ( /tmp) for your code, this is an ephemeral scratch resource not intended for durable storage such as Amazon Elastic File System (Amazon EFS). Serverless applications are event-driven, using ephemeral compute functions ranging from web APIs, mobile backends, and streaming analytics to data processing stages in machine learning (ML) and high-performance applications. For 'manual', see example in hello function below (syntax for both is identical) memorySize: 512 # optional, in MB, default is 1024 timeout: 10 # optional, in seconds, default is 6 versionFunctions: false # optional, default is true tracing: lambda: true # optional, enables tracing for all functions (can be true (true equals 'Active') 'Active' or 'PassThrough') functions: hello: handler: handler.hello # required, handler set in AWS Lambda name: $ cacheFrom: - my-image:latest platform: linux/amd64 anotherimage: uri: 000000000000. AWS default is auto this can either be 'auto' or 'onFunctionUpdate'. # serverless.yml service: myService provider: name: aws runtime: nodejs14.x runtimeManagement: auto # optional, set how Lambda controls all functions runtime. ConfigurationĪll of the Lambda functions in your serverless service can be found in serverless.yml under the functions property. If you are using AWS as a provider, all functions inside the service are AWS Lambda functions. ![]()
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