thptqg

Tra cứu điểm thi THPT Quốc gia — exam-score lookup for Vietnam's national high school graduation exam. Client-side SQL (sql.js) over a SQLite database built from the ministry's raw .xls score files by the Rust xlsxread parser.

Live at tiennm99.github.io/thptqg.

Dataset	Exam	Candidates	Site
2016	2016	877,461	/2016/
2017	2017	861,068	/2017/
2017-old	2017	847,348	/2017-old/
2017-old2	2017	679,764	/2017-old2/

The three 2017 datasets are successive publications of the same exam and they disagree; all three are kept so the differences stay inspectable.

Layout

index.html + src/     the frontend — one app serving all four datasets and the hub
  datasets.js           the four dataset ids and their per-dataset content
  router.js             pathname → dataset
data/<id>/            raw Excel files, one directory per dataset
parser/               the Rust parser
  src/schema.rs         canonical 22-column table: DDL, INSERT, subject regexes
  configs/<id>.toml     per-dataset parse rules only, no SQL
  scripts/              database build, crawler, parity verification
scripts/              site assembly
docs/                 architecture, data pipeline, deployment

The dataset id is one identifier end to end:

data/2017-old/ → parser/configs/2017-old.toml → db/2017-old.db.gz → /thptqg/2017-old/

Build

npm ci
npm run build:rust     # compile the parser
npm run build:db       # build + gzip all four databases (add an id for just one)
npm run build:site     # one Vite build, then assemble into _site/
npx serve _site

Pushing to main runs the same steps in .github/workflows/deploy-pages.yml and publishes to GitHub Pages.

Adding a dataset

1. Put the Excel files in data/<id>/
2. Add parser/configs/<id>.toml — sheet mode, column indices, validation guards. No SQL; the schema is canonical.
3. Add an entry to DATASETS in src/datasets.js

Everything else follows: the build script, the site assembly and the router all read that one list, and the UI adapts to whichever columns the dataset fills.

Docs

See docs/ — overview, architecture, data pipeline, deployment.