learning-website-nextjs1-9  Exercise 2: Prove It Against the Django Search, and Two Findings
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The Django project searches with SQLite's full-text search. This one searches with its own index. They should find the same pages, and
they can be compared on the real content, in two steps.

STEP 1: is the TEXT the same? The Django index stores each page's plain text; dumptext.py (in the Django project) writes a fingerprint of
each, and compare-text.mjs makes the same text here and compares every page byte for byte.

Save as compare-text.mjs:

// Is the plain text we index the same text the Django project indexed? Compare every page's text, byte for byte.
//   node compare-text.mjs <content folder> <dump json>
import { createHash } from "node:crypto";
import { readFileSync } from "node:fs";
import { loadPages, prepareFragment, readContentFile } from "./packages/content/src/index.ts";
import { htmlToText } from "./packages/search/src/index.ts";

const [root, dumpPath] = process.argv.slice(2);
const django = JSON.parse(readFileSync(dumpPath, "utf8"));
const { pages } = loadPages(root);
let same = 0;
let missing = 0;
const different = [];
for (const page of pages) {
  const expected = django[page.path];
  if (!expected) { missing++; continue; }
  const text = htmlToText(prepareFragment(readContentFile(root, page.path), page.path, page.site));
  if (createHash("sha256").update(text, "utf8").digest("hex") === expected[0]) same++;
  else different.push({ path: page.path, mine: text.length, django: expected[1] });
}
console.log(`${pages.length} pages: ${same} identical text, ${different.length} different, ${missing} not in the Django index`);
for (const d of different.slice(0, 8)) console.log("  ", JSON.stringify(d));

FIRST RUN: 4,413 of 4,420 identical, 7 different. The seven pages came from three causes (a fourth difference showed up later, in a unit test):

  1. "</>" (a React fragment written in a code sample) was kept as text here; a parser ignores it. Dropped now.
  2. "&frac25;" (the fraction two fifths) was not a name my decoder knew. A real HTML name, added.
  3. In TWO pages (a Romaji course chapter and the Build Tooling combined course) the text contains a raw "<style> block" and "<script src>" that
     should have been written "&lt;style&gt;" and "&lt;script src&gt;". An unclosed <style> or <script> swallows the REST of the page, in a browser
     and in Python's parser. The Django index therefore holds only 524 of 54,617 characters of the Build Tooling page, and a browser hides the rest
     of both pages. Here an unclosed one now swallows the rest too (so the two agree), but the real fix is in the two content files (a finding for
     you: website_checks.md).
  4. (Found later, by a unit test, and not in the seven:) I replaced comments with a space, Python with nothing ("a<!-- x -->b" is "ab").
  (Also: before this, the shared entity decoder did not know 57 names that pages use; it was filled in from a survey of every page, and a
  lower-case fallback was removed because &Delta; and &delta; are different letters.)

SECOND RUN:  4420 pages: 4420 identical text, 0 different, 0 not in the Django index

STEP 2: do the same QUERIES give the same PAGES? dumpsearch.py asks the Django search 35 questions on the real database (words, accents,
prefixes, Japanese of 1, 2, 3 and 5 characters, queries in every site, and injection-like text); compare-search.mjs asks the new one:

Save as compare-search.mjs:

// Build the static search index of every site from the real content and compare its answers with the Django project's.
//   node compare-search.mjs <content folder> <dump json>
import { readFileSync } from "node:fs";
import { gzipSync } from "node:zlib";
import { loadPages, prepareFragment, readContentFile } from "./packages/content/src/index.ts";
import { buildSearchIndex, htmlToText, prepare, search } from "./packages/search/src/index.ts";
import { SITE_NAMES } from "./packages/sites/src/index.ts";

const [root, dumpPath] = process.argv.slice(2);
const django = JSON.parse(readFileSync(dumpPath, "utf8"));
const { pages } = loadPages(root);

const indexes = new Map();
console.log("site             pages   build ms   index KB   gzip KB   text MB");
for (const site of SITE_NAMES) {
  const started = performance.now();
  let textBytes = 0;
  const docs = pages.filter((p) => p.site === site).map((page) => {
    const text = htmlToText(prepareFragment(readContentFile(root, page.path), page.path, site));
    textBytes += Buffer.byteLength(text);
    return { path: page.path, title: page.title, summary: page.summary || Array.from(text).slice(0, 160).join(""), text };
  });
  const index = buildSearchIndex(docs);
  const ms = Math.round(performance.now() - started);
  const json = JSON.stringify(index);
  indexes.set(site, prepare(index));
  console.log(`${site.padEnd(16)} ${String(docs.length).padStart(5)} ${String(ms).padStart(10)} ${String(Math.round(json.length / 1024)).padStart(10)} ${String(Math.round(gzipSync(json).length / 1024)).padStart(9)} ${(textBytes / 1048576).toFixed(1).padStart(9)}`);
}

console.log("\nquery".padEnd(33), "Django".padStart(7), "here".padStart(6), " same set  top-3 same");
let sameSets = 0;
let sameTop3 = 0;
const rows = [];
for (const q of django) {
  const found = search(indexes.get(q.site), q.q, { limit: 100000 });
  const mine = found.results.map((r) => r.path);
  const mineSet = new Set(mine);
  const theirSet = new Set(q.paths);
  const sameSet = mine.length === q.paths.length && q.paths.every((p) => mineSet.has(p));
  const top3 = JSON.stringify(mine.slice(0, 3)) === JSON.stringify(q.paths.slice(0, 3));
  if (sameSet) sameSets++;
  if (top3) sameTop3++;
  const extra = mine.filter((p) => !theirSet.has(p)).length;
  const missing = q.paths.filter((p) => !mineSet.has(p)).length;
  rows.push(`${(q.site + ": " + q.q).slice(0, 31).padEnd(32)} ${String(q.total).padStart(7)} ${String(found.total).padStart(6)}  ${sameSet ? "yes" : `no (+${extra} -${missing})`}`.padEnd(66) + (q.total === 0 && found.total === 0 ? "(none)" : top3 ? "yes" : "no"));
}
console.log(rows.join("\n"));
console.log(`\nsame set of pages: ${sameSets} of ${django.length}; same first three: ${sameTop3} of ${django.length}`);

const timings = [];
for (const q of django) { const t = performance.now(); search(indexes.get(q.site), q.q); timings.push(performance.now() - t); }
timings.sort((a, b) => a - b);
console.log(`search time over ${timings.length} queries: median ${timings[timings.length >> 1].toFixed(2)} ms, slowest ${timings.at(-1).toFixed(1)} ms (first use of each term decodes its list)`);

site             pages   build ms   index KB   gzip KB   text MB
languages          561        593        937       320       2.1
webdevelopment     514        696        966       338       4.6
programming       1388       2309       2684       937      13.1
systems            653       1334       1465       513       7.7
ai                 187        281        422       146       1.6
humanities         707        907       1538       561       4.5
lifeskills         253        257        471       165       1.2
creative           157        170        299       102       0.9

query                             Django   here  same set  top-3 same
languages: szeretnek                  18     18  yes              yes
languages: Szeretnék                  18     18  yes              yes
languages: brotchen                    1      1  yes              yes
languages: polite conditional          7      7  yes              no
languages: polite cond                10     10  yes              no
languages: hotel                      43     43  yes              yes
languages: ありがとう                      25     25  yes              no
languages: ありが                        25     25  yes              no
languages: 水                           5      5  yes              yes
languages: 水を                          0      0  yes              (none)
languages: ください                       13     13  yes              no
languages: ö                         559    559  yes              no
languages: the                       348    348  yes              no
languages: zzzqqq                      0      0  yes              (none)
languages: hiragana                  226    226  yes              no
languages: kanji                      32     32  yes              yes
languages: Frau Weber                  5      5  yes              yes
languages: o                         559    559  yes              no
systems: partition                    44     44  yes              yes
systems: systemd unit                 39     39  yes              no
systems: grub                         32     32  yes              yes
systems: ssh key                      89     89  yes              yes
programming: decorat                  65     65  yes              yes
programming: list comprehension       21     21  yes              yes
programming: async await             148    148  yes              yes
webdevelopment: grid                  63     63  yes              yes
webdevelopment: flexbox               31     31  yes              yes
webdevelopment: css variables         27     27  yes              yes
ai: prompt                            85     85  yes              yes
humanities: empire                   177    177  yes              no
lifeskills: recipe                    27     27  yes              yes
creative: layer                       49     49  yes              yes
languages: "; DROP TABLE x; --         0      0  yes              (none)
languages: title:hotel                 2      2  yes              yes
languages: NEAR(a b)                  25     25  yes              no

same set of pages: 35 of 35; same first three: 23 of 35
search time over 35 queries: median 0.04 ms, slowest 1.0 ms (first use of each term decodes its list)

RESULT: the SAME set of pages for 35 of 35 queries. It was 31 of 35 at first: "ありが" found one page too many, because with PAIRS of characters a
page that has あり in one place and りが in another counts. Adding groups of three characters to the index (the index grew from 757 KB to 821 KB
for the languages site, before ranking was added) made a 3-character query exact. The other three differences were the unclosed <style> in the
Build Tooling page.

RANKING: "same first three" went from 5 of 35 (a simple "title match first" rule) to 23 of 35 once BM25 was used. Three of the 35 queries have no
results at all, so that is 20 of 32 real ones: it is NOT identical, because the two systems compute BM25 a little differently (SQLite weights
columns and lengths its own way). I stopped there on purpose: matching another engine's order to the last result is not the goal; the same set of
pages, in a sensible order, is.

SIZE AND SPEED (the index of each site, built from its real pages):
  the languages index is 937 KB, 320 KB gzipped (273 KB with brotli); the largest, programming, is 2.7 MB (937 KB gzipped).
  A search takes a median of 0.04 ms and at worst 1.7 ms once the index is loaded.

WHY THIS WORKS AS AN ANSWER
---------------------------
It compares everything it can (text for 4,420 pages, 35 queries), reports what it did NOT match (the order of results), and two of the seven
text differences turned out to be errors in the CONTENT, not in the code.