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Uva Software’s scanii-lambda

A Sam-Packaged AWS Lambda client to the scanii.com content processing engine. For a detailed walk-through of deploying this application see: https://support.scanii.com/article/55-how-do-i-analyze-content-stored-on-amazon-s3.

How it works

This is, essentially, a series of lambda functions packaged in a one-click deployable application that configures everything needed so your S3 objects are submitted automatically to scanii’s content analysis API. Once the content is processed, you can choose from a couple of different actions:

  1. Tag the content - this is defaulted to on and adds the following tag to objects processed:
    1. ScaniiId -> the resource id of the processed content
    2. ScaniiFindings -> list of identified findings (content engine dependent)
    3. ScaniiContentType -> the identified content type of the file processed
  2. Delete the object with findings - this is defaulted to off and will delete S3 objects with findings (such as malware or NSFW content) - for a full list of available content identification see https://support.scanii.com/article/20-content-detection-engines

Working with the source code

The source code for this application is written using Javascript and requires, at least, nodejs 8 to run. Before getting started we strongly advise you to become familiar with the following technologies:

  1. Amazon S3
  2. Amazon Lambda
  3. AWS Serverless Application Model (SAM) specification

Building and running tests

Tests utilize Mocha and are triggered into NPM, we provide a makefile to tie everything together:

$ make test

Running the application locally

If you have the SAM CLI (https://github.com/awslabs/aws-sam-cli) installed locally you can run scanii-lambda locally for testing:

$ make run

Deploying it

You can deploy this application by clicking here. - please note that after deployment you must manually create a trigger event for the uvasoftware-scanii-lambda-submit function for your S3 bucket, under “Add Triggers/S3” and event type Object Created (All)