Verify what type of instance store volume (HDD, SSD, or NVMe SSD), if any, that your instance supports. If the instance supports instance store volumes, then check the type of instance store volumes that are supported and review the volume's information from the operating system (OS). Support is also available through many AWS Lambda Partners such as Datadog, HashiCorp (Terraform), Lumigo, Thundra, Slalom, and Contino.To identify instance store volumes on Amazon EC2 Linux instances, first check if the instance type supports instance store volumes. With 10 GB container image support, 10 GB function memory, and now 10 GB of ephemeral function storage, you can support workloads such as using large temporal files, data and media processing, machine learning inference, and financial analysis. You can now configure up to 10 GB of ephemeral storage per Lambda function instance in all Regions where AWS Lambda is available. 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.
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