This project contains a sample of an AWS Lambda function that performs a simple transfomration of MARC21-encoded files to MARC-In-JSON.

The purpose of this project is to provide a means of estimating the cost of using AWS services to perform "burst-style" processing in order to get a sense of the cost-effectiveness of the approach.

An AWS Lambda function is a single function call that can be invoked by a number of different triggers. When the trigger is fired, AWS instantiates a server, sets up the environment, and invokes the function on that server, then shuts down after the function call is complete. In principle, this allows users to pay for only the amount of processing they actually use.

In this partciular instance, the Lambda function edu.ncsu.lib.aws.handlers.MARCHandler.handleRequest is designed to receive notifications that files have been deposited in a particular Amazon S3 bucket, transform them, and deposit the results into a different bucket. This is proof-of-concept code, and so for simplicity's sake the inputs are MARC21 encoded files and the outputs are those inputs converted to MARC-in-JSON format. To a certain extent, this mimics one part of the workflow of maintaining a shared index:

• an institution uploads files in "Format X"
• without further intervention from the uploader, processing is done on those files to transform the records into files that can be ingested by Solr.

A complete description of the setup in AWS for all of this is beyond the scope of this README.

The only requirement on the build system is a Java 8 JDK. To build, invoke ./gradlew shadowJar (or ./gradlew.bat shadowJar on Windows); this will download a suitable version of gradle and build the output. The result will be stored in build/libs/aws-lambda-test-all.jar -- this is a complete jar file containing all dependencies for the function, and can be uploaded directly to AWS via the console. I have not taken the time to set up uploading via the tools provided by AWS.

N.B. As of initial commit, the unit tests do not successfully complete, but the function has been run successfully on AWS multiple times.