csv-parser VS schema-irve

Inside the API route, convert the CSV file to JSON with csv-parser

https://github.com/mafintosh/csv-parser/pull/121 https://github.com/mafintosh/csv-parser/pull/151 https://github.com/mafintosh/csv-parser/issues/218

The author of the library probably has learned, the hard way many many lessons (and probably also decided to prioritize some of the requested issues / feature requests along the way).

The above is not meant as a ding on the project itself and I am sure it is used successfully by many people. The point here is that your claim that you can easily write a csv parser in 200 lines of code does not hold water. It's anything but easy and you should use a battle tested library and not reinvent the wheel.

If this were me, I'd probably add a getStaticProps function to the page, and then use the csv-parser library within this function to convert the CSV file into JSON data, and then return that JSON data from the getStaticProps function so the page can access it as props.

I started with using the external library csv-parser. However, since it is basically a wrapper around the base Node.js technologies I listed above I has the same problems working with my data that I will list below. I eventually uninstalled it and wrote my own light-weight version.

I have found this CSV parser package and it almost works exactly like I need it. I've ran into 2 issues.

I was using the [csv-parser](https://github.com/mafintosh/csv-parser) library to handle csv parsing in node. The file can be huge ranging from 50,000 to 500,000 lines, maybe even larger. I had to perform some computations on the csv, after this is submitted to the server for that I was thinking of dividing the csv into chunks which I could then provide to worker threads for performing the computation. The worker thread would get the number of lines to skip and then start reading the lines after that up to a particular limit. I create a read stream and pass the csv-parser in with the option of number of lines to skip. I tried to perform some benchmarks on it but could find no visible benefits between skipping lines and not skipping lines. Even if I read the whole file it was sometimes faster than reading the ending 30,000 lines.

Agreed! People are surprised but CSV files (while I would use them for everything) work great in low-tech environment, or when files must be produced by many different actors with different technical levels.

One concrete example is the French standard to describe Electrical Vehicles Charge Points, which is made of 2 parts (static = position & overall description, dynamic = current state, occupancy etc). Both "files" are just CSV files:

https://github.com/etalab/schema-irve

Both sub-formats are specified via TableSchema (https://frictionlessdata.io/schemas/table-schema.json).

Files are produced directly by electrical charge points operators, which can have widely different sizes & technicality, so CSV works nicely in that case.