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Better parametric search for components available for JLC PCB assembly

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JLC PCB SMD Assembly Component Catalogue

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A better tool to browse the components offered by the JLC PCB SMT Assembly Service.

This repo is forked from the excellent work of jaqwsx (repo here), and contains a number of number of enhancements, such as ~10x speedup for full-text search and >100x speedup for database update, and some filtering by basic parts, by favorite parts, by any subcategory with a matching name, download CSV file, etc.

This forked repo was meant as a test site for the enhancements.

How To Use It?

This enhanced version: https://dougy83.github.io/jlcparts/

Jaqwsx's original version: https://yaqwsx.github.io/jlcparts/

Why?

(This description is from original repo)

Probably all of us love JLC PCB SMT assembly service. It is easy to use, cheap and fast. However, you can use only components from their catalogue. This is not as bad, since the library is quite broad. However, the library UI sucks. You can only browse the categories, do full-text search. You cannot do parametric search nor sort by property. That's why I created a simple page which presents the catalogue in much nicer form. You can:

  • do full-text search
  • browse categories
  • parametric search
  • sort by any component attribute
  • sort by price based on quantity
  • easily access datasheet and LCSC product page.

How Does It Work?

Please see original repo for base info. This repo differs in the following respects:

The database downloads as a single 20MB file, and takes less than a second to store in IndexedDB. This file is updated every day, using the large database generated by the events on jaqwsx's repo.

The frontend uses IndexedDB in the browser to store three component library tables as gzip-compressed binary strings. These tables are text files and have a single entry on each line, as a JSON string, or just an a plain string for the attribute lookup table.

Queries are performed on the compressed data by decompressing on the fly, which provides a much faster query than using IndexedDB queries. Keeping the tables compressed means the RAM usage is kept low.

Info

Please view original repo for info.

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