![]() ![]() That way you won't need to fetch it from S3 during every invocation. As an example you may want to fetch a machine learning model, store it in /tmp and use it in your Lambda function. In short - /tmp works well for ephemeral storage which should be shared between invocations with an added benefit of fast I/O throughput. Each new execution environment starts with an empty /tmp directory.Because of the way Lambda is designed, the same execution environment will be reused by multiple invocations to optimize performance. ![]() EDIT: Justin Plock pointed out that you can get up to 10GB of ephemeral storage in /tmp There are multiple factors to consider before using /tmp as a storage option: Temporary storage, also known as /tmpĪnother interesting storage option available for AWS Lambda functions is its execution environment file system, available at /tmp. To create ad hoc visualizations and business analysis reports, Amazon QuickSight can connect to your S3 buckets and produce interactive dashboards.Ĭheck out S3 FAQ to learn more. Additionally you can use AWS Glue to perform extract, transform, and loan (ETL) operations. For instance, you can use Amazon Athena to query your S3 data, or Amazon Rekognition to analyze it. Storing data in S3 has an additional benefit, given how well it integrates with other AWS services. Should you need a larger dataset, you can consider fetching that from S3. This is often useful because Lambda can be invoked with 6MB payload in a synchronous manner and 256kB in an asynchronous manner. Not only can you invoke a Lambda function whenever an object is placed into an S3 bucket, but you can also both retrieve and send data to/from S3 in your Lambda function invocation. This can provide a scalable way to trigger application workflows when objects are created or deleted in S3. It has a native integration with Lambda, which allows you to invoke a function in response to an S3 event. S3 has important event integrations for serverless developers. ![]() ![]() S3 is a common element of serverless architecture diagrams, to quote AWS docs: It's a great choice for storing unstructured static assets, such as images, videos, documents, etc. Amazon S3Īmazon S3 is a widely popular object storage service, offering high availability and 11 9's of durability. The goal of this post is to give you an overview of the different storage options available to you when building serverless applications with AWS Lambda, their differences and common use-cases. Other potential use cases include machine learning models, image processing, the output of your business-specific compute operation and more. Good luck storing node_modules in DynamoDB, by the way. For instance, storing third-party libraries in DynamoDB would surely be an interesting idea, but not exactly practical. The seemingly obvious answer to this question is "use a database".Īs with most obvious answers, this one is not entirely correct. Which presents an interesting challenge: What if I need access to storage in my Lambda function? Lambda functions are (by design) emphemeral, which means that their execution environments exist briefly when the function is invoked. If you're building a serverless app, you're most likely using AWS Lambda. ![]()
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