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The two code comments that said the same outdated things go with them.
Reading the file where it lay never worked well enough. Two costs were
structural rather than bugs: reads are serial, because the worker uses
synchronous XHR, so a name search touching 390 pages waited 17 seconds
to move 608 KB — roughly one request per result row, which no page size
removes — and the first visitor after each deploy waited ~26 seconds for
the CDN to fill its cache with a 288 MB object.
The browser now downloads the whole database once and queries it in
memory with sql.js. A dataset page is gated behind that: the gate states
what it will cost, in transfer and in memory, and offers only the
download, because there is nothing to show without it.
Dropping the structures that existed to make range-request queries
index-driven halved the file. name_word carried one row per word of
every name, about 3.5 million of them, and with the partial score
indexes it was more than half of what every visitor would now download.
Measured on rebuilt databases: 2016 went 288.6 -> 142.5 MB (31 MB
gzipped on the wire), 2017 237.7 -> 119.3 MB, both with row counts and
audits unchanged. Queries on the result: an exam number is immediate, a
name scans all 877,460 rows in about 240 ms.
Alternatives were measured before choosing this. sqlite-wasm-http sizes
files from a HEAD Content-Length with no override, so on a host that
gzips it silently uses the compressed size. DuckDB-WASM ships 32-37 MB
of WebAssembly before its Parquet extension, more than this whole
download. Static pre-generated shards are the most robust option but
cannot answer arbitrary SQL, and cannot stop early the way LIMIT does.
The published name loses its chunk index, the byte budgets and the SQL
consent modal go with the range reads that made them necessary, and the
docs no longer describe a design the site does not use.
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.
Four of the 119 files in data/2016 publish one score column per subject
instead of a DIEM_THI sentence, and none of them was being read correctly.
The ĐH Công nghiệp Thực phẩm file puts a three-row ministry title block
above its header, so no header was recognised and the positional fallback
shifted every column by one: the serial number became so_bao_danh, the
exam number became ho_ten, the name became ngay_sinh, and the national ID
became the score cell. All 7,833 rows were unusable. The three ĐH Cần Thơ
files name an SBD column but no DIEM_THI, so they fell to the same
fallback: surname into ngay_sinh, given name into ten_cum_thi, birth date
into the score cell, and 12,152 candidates with no scores at all.
Both are now read by FormatSubjectColumns, which resolves identity and one
column per subject from the header. The header is searched for in the
first five rows, so a title block no longer hides it.
The Cần Thơ score columns are numbered rather than named. They follow the
order the exam was sat — each morning an essay paper, each afternoon a
multiple-choice one — which is what identifies them: columns 1/3/5/7
quantise to 0.25 and 2/4/6/8 do not, and each column's mean lands within
0.5 of the same subject's mean across the rest of the dataset. The
foreign language is filed under the subject its N1..N6 code names.
Gender now accepts the 0/1 encoding those files use: of the rows marked
1, 53% carry "Thị" in the name against 1% of those marked 0. Birth dates
in the compact ddmmyy form are expanded so the column holds one format.
A score of 0 is stored rather than dropped, recovering 302 real scores
that a JavaScript falsy check had been turning into NULL.
Row count falls by one, to 877,460: the removed row is the title line
"ĐƠN VỊ: / TRƯỜNG ĐẠI HỌC CÔNG NGHIỆP THỰC PHẨM TP. HỒ CHÍ MINH", which
had been stored as a student. The dataset has no duplicate exam numbers;
the three rows previously described as collapsing were that same file's
title and header lines being counted and then rejected.
Also drops behaviour that existed only to match the parser this one
replaced: the inert "SINH " header token, the untrimmed diem_thi cell, an
unreachable blank-row branch, and a cross-check test against a database
that can no longer exist. None of them changes output.
Verified by rebuilding both datasets: 877,460 and 861,068 rows, both
artifacts through the assembler's row and size guards, and the reader
fidelity suite unchanged across all 182 files.
The site credited 2016 to Bộ GD&ĐT, which those files were never fetched
from. Both datasets come from published articles: 2016 from an aggregator
on dtnt.bacninh.edu.vn listing one spreadsheet per exam cluster, 2017 from
baotintuc.vn. The README, the architecture table and the web footer all
repeated the ministry claim.
The footer now shows each dataset's full article URL as a link rather than
a bare host, so the citation can be checked. That needs overflow-wrap on
the footer: the 2016 URL is 110 characters with no break opportunity and
would otherwise scroll the page sideways on a phone.
Also records that a full 2016 crawl has been run successfully. The host was
marked unconfirmed and data/2016/ described as the only recoverable copy;
both datasets are now rebuildable from source.
Move the frontend into web/, the repo's only npm workspace, and replace the
JS crawler with a Go module covering both remaining datasets. The crawler
writes to a .part file and renames on completion: writing straight to the
destination left truncated files that the skip-if-present check would then
skip forever.
Remove the 2017-old and 2017-old2 datasets. They were successive publications
of the same exam, kept side by side so the disagreement stayed inspectable;
the current 2017 supersedes them and they remain in git history.
Recover the 2016 crawler source from the Internet Archive's copy of the
aggregator article, whose original host no longer resolves. All 119 filenames
are verified against data/2016 in both directions, but no archive captured the
spreadsheets themselves, so the host still serving them is unconfirmed and
data/2016 remains the only confirmed copy.
Filenames are load-bearing throughout: go-parser sorts inputs bytewise and
inserts last-wins, so they decide which row survives a duplicate exam number.
The docs still described two standalone projects with separate frontends,
separate parsers and three Vite variants. Rewritten around what the repo now
is, merging both projects' copies rather than keeping one and discarding the
other — deployment-guide.md and system-architecture.md existed in both and
documented different pipelines.
project-overview.md goal, scope, constraints, the four datasets, history
system-architecture.md data flow, canonical schema, routing, how one
frontend serves both exam years without branching
data-pipeline.md per-dataset Excel formats, the three 2016 layouts,
overflow-sheet gotcha, expected row counts
deployment-guide.md the single-build workflow, adding a dataset,
why no uncompressed database can ship
Records the release gate in plans/reports/parser-parity-result.md: row counts,
all 18 pre-existing per-column non-NULL counts and the deterministic student
samples are identical across all four datasets, against databases decompressed
from the exact bytes the pipeline publishes. The 1,691 recovered
foreign-language scores are documented with the evidence they are real.
Also drops a machine-specific absolute path from a comment in
format_detect_2016.rs. The build-database.js citations there are kept: that
file no longer exists in this repo, but the references explain why several
parsing rules look arbitrary.
tiennm99