tiennm99
f180c662a2
The previous attempt passed fileLength in the inline config, and the
worker discarded it. sqlite.worker.ts builds the lazy file's config
itself and hardcodes:
fileLength: config.serverMode === "chunked"
? config.databaseLengthBytes
: undefined
So in full mode there is no way to supply a length, and the library
falls back to sizing the file with a HEAD request — which GitHub Pages
answers with the gzipped length, and then refuses to use.
Chunked mode is the only mode that takes a length. One chunk holds the
whole database, so the chunk index is always 0 and every request goes to
urlPrefix + "0"; the assembler therefore publishes <id>.sqlite30. The
length still comes from the range probe, which also checks the bytes are
a SQLite header.
dbPrefixOf and dbOf derive one form from the other and are handed to
RemoteDatabase together, so the prefix the library appends an index to
and the file the assembler writes cannot drift apart. A test pins that;
nothing else would catch it, because the symptom is a 404 per query.
The stray-artifact guard now also rejects a leftover <id>.sqlite3, which
after this change is a stale artifact rather than the published one.
sqlite-wasm-http was checked as an alternative and does not help: its
worker takes the size from a HEAD Content-Length too, and its options
expose no way to override it, so on Pages it would silently use the
compressed size. Its shared-cache backend wants COOP/COEP, but it ships
a fallback that does not, so isolation was never the blocker — the
architecture note claiming otherwise is corrected.
3.9 KiB
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 read in place by
HTTP range request, 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/ → .db
assembler/ Go — verifies, compresses, builds, assembles .db + 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.sqlite30 → /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, compresses it, 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.