tiennm99
dbc23c25c5
The browser downloaded 45 MB of gzipped SQLite before it could answer anything. Now sql.js-httpvfs asks for the pages a query touches and the databases ship uncompressed as <id>.sqlite3 — a byte range of a gzip stream is not a byte range of a database. That only works if every query the site issues is index-driven, and measured against the real 2016 file, most were not: so_bao_danh = ? SEARCH via PK ~20 KB ho_ten_ascii LIKE '%x%' SCAN 127 MB ho_ten_ascii LIKE 'x%' SCAN 127 MB COUNT(*) covering index scan 20 MB ORDER BY toan DESC LIMIT 10 SCAN + temp b-tree 127 MB Prefix LIKE scans because SQLite's LIKE optimisation needs a NOCASE index; a range comparison does use the index. So the schema changed to suit the access pattern rather than the search changing to suit the schema. name_word holds one row per word of each name, WITHOUT ROWID so the table is the index, carrying ho_ten_ascii so a multi-word query is resolved inside a single b-tree. name_word_freq says which word of a query is rarest — the vocabulary is 4,397 words across 2.87M entries, so "buu loc" seeks on 287 entries rather than walking the 300,000 that "thi" would. Searching by any word of a name survives, at a few hundred KB a query. idx_ho_ten and idx_ho_ten_ascii are gone: no plan could use either. Partial indexes on toan, khtn and khxh cost 12 MB and keep the SQL presets off a full scan. The footer's candidate count now comes from datasets.json instead of COUNT(*). 2016 grows 223.5 MB to 288.6 MB, 2017 162.7 MB to 237.7 MB, and the site is 528 MB against the 1 GB GitHub Pages limit. Row counts are unchanged. The SQL tab is the one place a user can still write a query that reads the whole table, so it asks before it opens, runs under a byte budget that stops a runaway query, and shows what each query actually fetched. Verified: row counts through the assembler guards, every app query index-driven under EXPLAIN QUERY PLAN, and GitHub Pages returning 206 with a correct Content-Range. Not verified in a browser — this machine has none — and the library refuses to open a file the host compresses, so the deployed response headers need a look.
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.ts, 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, 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.ts
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.