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). How should ephemermal storage be set for ad hoc task execution via the Ruby SDK? (I'm looking for the SDK equivalent to this blogpost by AWS, rather than an alternative solution e.g.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. I can't find anything in the docs for how to fix this.(the task runs fine, just then runs out of disk space). There are no errors or warnings in Cloudwatch that the property can't be set e.g.I am running with platform version 1.4.0.I know that the size_in_gi_b value is correct, as if I change it (e.g.I know that the ephermal_storage property is in the right place in my call, as if I move it the SDK gives an error.I've tried this: client = Aws::ECS::Client.new(region: region)īut it doesn't work (I'm running df -H within my entrypoint and can see that the storage doesn't increase). This all works fine with the defaults - I can kick off the task OK and can send along custom command parameters to my Docker container: client = Aws::ECS::Client.new(region: region)įor some runs, I need a large disk size than the default & want to set this via the ephemeral_storage parameter in the SDK. I'm using the Ruby SDK for AWS ECS to kick-off a task hosted in Fargate via run_task method.
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