"Serverless" Functions

Because the library is pure JS, the hard work of reading and writing files can be performed in the client browser or on the server side. On the server side, the mechanical process is essentially independent from the data parsing or generation. As a result, it is sometimes sensible to organize applications so that the "last mile" conversion between JSON data and spreadsheet files is independent from the main application.

The straightforward architecture would split off the JSON data conversion as a separate microservice or application. Since it is only needed when an import or export is requested, and since the process itself is relatively independent from the rest of a typical service, a "Serverless" architecture makes a great fit. Since the "function" is separate from the rest of the application, it can be integrated into a platform built in Java or Go or Python or another language!

This demo discusses general architectures and provides examples for popular commercial systems and self-hosted alternatives. The examples are merely intended to demonstrate very basic functionality.

Simple Strategies

Data Normalization

Most programming languages and platforms can process CSV or JSON but can't use XLS or XLSX or XLSB directly. Form data from an HTTP POST request can be parsed and contained files can be converted to CSV or JSON. The XLSX.stream.to_csv utility can stream rows to a standard HTTP response. XLSX.utils.sheet_to_json can generate an array of objects that can be fed to another service.

At the simplest level, a file on the filesystem can be converted using the bin script that ships with the NodeJS package:

$ xlsx /path/to/uploads/file > /tmp/new_csv_file

From a utility script, workbooks can be converted in two lines:

var workbook = XLSX.readFile("path/to/file.xlsb");
XLSX.writeFile(workbook, "output/path/file.csv");

Report Generation

For an existing platform that already generates JSON or CSV or HTML output, the library can process the data and generate a new file with embellishments. The XLSX.utils.sheet_add_json and XLSX.utils.sheet_add_aoa functions can add data rows to an existing worksheet:

var ws = XLSX.utils.aoa_to_sheet([
	["Company Report"],
	[],
	["Item", "Cost"]
]);
XLSX.utils.sheet_add_json(ws, [
	{ item: "Coffee", cost: 5 },
	{ item: "Cake", cost: 20 }
], { skipHeader: true, origin: -1, header: ["item", "cost"] });

Deployment Targets

The library is supported in Node versions starting from 0.8 as well as a myriad of ES3 and ES5 compatible JS engines. All major services use Node versions beyond major release 4, so there should be no problem directly using the library in those environments.

Note that most cloud providers proactively convert form data to UTF8 strings. This is especially problematic when dealing with XLSX and XLSB files, as they naturally contain codes that are not valid UTF8 characters. As a result, these demos specifically handle Base64-encoded files only. To test on the command line, use the base64 tool to encode data before piping to curl:

base64 test.xlsb | curl -F "data=@-;filename=test.xlsb" http://localhost/

AWS Lambda

Through the AWS Gateway API, Lambda functions can be triggered on HTTP requests. The LambdaProxy example reads files from form data and converts to CSV.

When deploying on AWS, be sure to npm install locally and include the modules in the ZIP file.

When reading form data, be sure to include the necessary binary types on the AWS API Gateway console. To do this, navigate to the "Binary Media Types" section in the settings tab of the console. For reading a file, you may need to add "multipart/form-data". For downloading a file, you may need to add "application/vnd.ms-excel".

Azure Functions

Azure supports many types of triggers. The AzureHTTPTrigger shows an example HTTP trigger that converts the submitted file to CSV.

When deploying on Azure, be sure to install the module from the remote console, as described in the "Azure Functions JavaScript developer guide".

Firebase Functions

Firebase functions can be triggered via HTTP requests, similar to a REST API. In the Firebase directory, the example function reads files sent through HTTP and converts it to a CSV and sends the response in the form of a string.

To run this demo locally, run npm i -g firebase-tools to install the Firebase CLI and npm i to install the dependencies, then firebase use --add to connect to an existing Firebase project. Run firebase emulators:start to start the local server.

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