CSV Parsing Server

This server takes a POST request with CSV as it's body. It parses the CSV and returns a JSON response containing an array with each CSV record as an object within it.

Getting Started

Firstly, ensure you have the following installed

- Rust (https://www.rust-lang.org/tools/install)

Clone this repository and run cargo run. If you'd like to set any specific environment variables, you can run cargo run -- --help to see all ovailable options. You can either set the environment variable from the help text or set it in the command line, e.g. cargo run -- -l debug -p 5000.

The current config variables are as follows:

env variable	CLI flag	type	default	optional
PORT	-p || --port	u16	3000	yes
HOST	-h || --host	String	127.0.0.1	yes
LOG_LEVEL	-l || --log-level	String	info	yes

Currently there is only one route, which is /api/parse. It takes a POST request with a CSV body. The header Content-Type must be set to text/csv as well. The response is JSON, containing an array of each record.

Running locally

Generating some CSV

There is a basic Node script to generate a CSV.

Run this from the root directory:

cd scripts/csv_generator && npm i && npm run generate && cd -

For more info, please check here

Running the server

run cargo run

Making a request

Assuming your server is running, in another terminal, run cd scripts followed by ./http_req.sh This will make a cURL request to the server with the generated CSV from the step above

Architechture

This app is split into several crates

Bin

The bin crate is found at src/bin. It is the glue that calls all the other crates together to make the app :)

Config

The config crate is found at src/config. It contains any app level configs needed to ensure this is a 12 factor app Any and all configs should happen here so they are centrally located and not duplicated across code.

Server

The server the main crate, located at src/server. It contains the code to setup and start the server as well as all the controllers. Right now, there is only a few views (AoS for CSV records, and CSV parsing errors). It's mostly very straight forward.

One notable decision was to use an amortized vec crate. This was to ensure that as this service becomes more popular, and we start to parse larger and larger CSV files, it is important that we can have predictable performance. By using an amortized vec, we can spread the cost of increasing the vector memory allocation (as it fills up) across all pushes to the vector. While this may be a smidge slower for smaller CSV files (on the order or nanoseconds), for larger CSV files, this will prevent re-allocation pauses.

Current Issues

The biggest one is that currently, there is a panic due to a stack overflow somewhere in the csv or serde crate when parsing invalid CSV. Seeing as this is a coding challenge, and the issue lies outside of this code, I am going to let it go.

The others, in no particular order:

• Configurable max body size for CSV
• Currently only takes a structured CSV, dynamic CSV is not possible at the moment, but should be trivial to implement later
  • Likely would use a HashMap to store each record
• Should stream bytes from request body into CSV buf reader as bytes are ready
  • I think using the csv-core crate for incremental parsing would, potentially, make this endpoint a little bit faster
• Models should be separated out into a domain crate for testing/readability
• Testing is not particularly robust at the moment