tiennm99
2c24943cc6
Three reversals had landed without the documentation following them, so the docs described a pipeline that compresses its output, a schema with three secondary indexes, and a browser that re-downloads the file on every visit. None of those are true any more. - Compression: the assembler stopped producing .gz when the databases began shipping as .sqlite3. The deployment guide's "why no uncompressed database can ship" section explained a guard that now exists for the opposite reason — to keep .db, .gz and journals out, so .sqlite3 stays the only name. - Indexes: the architecture printed a DDL with three CREATE INDEX statements and a paragraph on the partial one. schema.go carries none. - Persistence: "the download is repeated every visit ... has not been done" was listed as an open risk after db-cache.js closed it. Replaced with the ETag flow, the offline fallback, and the risks that did replace it. Measured both transfers rather than scaling one from the other, which would have been wrong: 2016 is 142 MB stored and 31 MB delivered, 2017 is 119 MB and 36 MB. The smaller database is the larger download, so neither figure follows from the stored size. Also corrects a CHUNK_BYTES reference to a module that no longer exists, the 238-289 MB per-dataset figure, two paths to web/src/lib/datasets.js, and the CI step list, which omitted npm test and the post-deploy header check. The two code comments that said the same outdated things go with them.
thptqg
Tra cứu điểm thi THPT Quốc gia — exam-score lookup for Vietnam's national high
school graduation exam. Client-side SQL over a SQLite database the browser
downloads once, keeps, and queries in memory, built
from the published .xls/.xlsx score files by the Go parser module. Where
those files come from: data pipeline.
Live at tiennm99.github.io/thptqg.
| Dataset | Exam | Candidates | Site |
|---|---|---|---|
2016 |
2016 | 877,460 | /2016/ |
2017 |
2017 | 861,068 | /2017/ |
Two earlier 2017 publications (2017-old, 2017-old2) were kept for a while
because they disagreed with the current one. They have been removed; they remain
in git history.
Layout
The repository is one directory per pipeline stage, plus the two stores they pass between them.
crawler/ Go — re-fetches the source spreadsheets → data/
parser/ Go — Excel to SQLite data/ → .sqlite3
assembler/ Go — verifies, builds and assembles .sqlite3 + web/ → _site/
web/ npm — the frontend, one SvelteKit app for every dataset
data/<id>/ raw Excel files, one directory per dataset
datasets.json the registry: which datasets exist, and their expected size
docs/ architecture, data pipeline, deployment
Each stage runs on its own and hands its output to the next through the stores.
web/ is the only npm project; the three stages are independent Go modules.
datasets.json is the contract between them. It is JSON because Go and the web
app both read it and neither needs a dependency to do so; presentation stays in
web/src/lib/datasets.js, keyed by id, which fails loudly if the two disagree.
The dataset id is one identifier end to end:
data/2017/ → parser/configs/2017.yml → db/2017.sqlite3 → /thptqg/2017/
Build
(cd web && npm ci)
go -C assembler run ./cmd/assemble # databases, then the site, into _site/
npx serve _site
That one command compiles the parser, builds and verifies each database against
its registry row count and size, builds the web app and assembles _site —
refusing to continue if a database is short, an artifact looks truncated, or one
is missing altogether. Sub-steps when iterating:
go -C assembler run ./cmd/assemble db 2017 # one database
go -C assembler run ./cmd/assemble site # web build and _site only
go -C assembler run ./cmd/assemble verify A B # compare two sets of databases
(cd web && npm run dev) # the app against staged databases
The source spreadsheets are committed, so a crawl is only needed to refresh them:
go -C crawler run ./cmd/crawl 2016
go -C crawler run ./cmd/crawl 2017
Each reads the download links out of the article that published the dataset, so no link list is kept in the repository. Crawling is idempotent — files already present are skipped — and is never part of the build.
Pushing to main runs the same steps in
.github/workflows/deploy-pages.yml and publishes to GitHub Pages.
Adding a dataset
- Put the Excel files in
data/<id>/ - Add
parser/configs/<id>.yml— sheet mode, column indices, validation guards. No SQL; the schema is canonical. - Add an entry to
datasets.jsonwith its expected row count and size - Add the matching presentation to
CONTENTinweb/src/lib/datasets.js
Everything else follows: the assembler, the router and the hub all read the registry, and the UI adapts to whichever columns the dataset fills. Steps 3 and 4 check each other, so forgetting either one fails rather than half-working.
Docs
See docs/ — overview,
architecture,
data pipeline,
deployment.