docs(adr): ADR-008 SQL-level FTS pagination via window-function CTE

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docs/adr/008-fts-sql-pagination.md

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+# ADR-008: SQL-level pagination for full-text search via window-function CTE
+
+## Status
+
+Accepted
+
+## Context
+
+`DocumentRepository.findAllMatchingIdsByFts` (formerly `findRankedIdsByFts`) returns all matching document IDs for a FTS query. `DocumentService.searchDocuments` then paginates in memory on the RELEVANCE sort path.
+
+A pre-production audit against 1,520 documents measured:
+
+```
+rows_per_call: 911 / call  (query: "walter")
+```
+
+At current scale this is acceptable — 911 UUIDs ≈ 14 KB, ms-level DB time. At 100 K+ documents two failure modes emerge:
+
+1. **Memory**: a broad query returns ~60 K UUIDs ≈ 1 MB per request, multiplied by concurrent users.
+2. **Latency**: the `LATERAL` join does work proportional to match-set size; at 60 K matches the FTS step alone exceeds 100 ms per query.
+
+Tracked as finding **F-31 (High)** in the pre-production architectural review.
+
+## Decision
+
+Push pagination and rank ordering into SQL for the RELEVANCE sort path when no non-text filters are active (pure full-text search):
+
+```sql
+WITH q AS (
+  SELECT CASE WHEN websearch_to_tsquery('german', :query)::text <> ''
+              THEN to_tsquery('simple', regexp_replace(
+                       websearch_to_tsquery('german', :query)::text,
+                       '''([^'']+)''', '''\\1'':*', 'g'))
+         END AS pq
+), matches AS (
+  SELECT d.id, ts_rank(d.search_vector, q.pq) AS rank
+  FROM documents d, q
+  WHERE d.search_vector @@ q.pq
+)
+SELECT id, rank, COUNT(*) OVER () AS total
+FROM matches
+ORDER BY rank DESC, id
+OFFSET :offset LIMIT :limit
+```
+
+`COUNT(*) OVER ()` returns the full match count alongside each page row in a single round-trip — no separate count query needed.
+
+`rows_per_call` for the FTS query drops from match-set size (911) to page size (≤ 50).
+
+When non-text filters (date range, sender, receiver, tags, status) are also active, the existing path is preserved: `findAllMatchingIdsByFts` returns all ranked IDs, which are passed as an `IN` clause to the JPA Specification, and `totalElements` comes from the JPA `Page.getTotalElements()`. This keeps the count accurate across the combined filter set.
+
+## Alternatives Considered
+
+**1. Two-query approach (separate COUNT + paged SELECT)**
+Correct, but doubles round-trips. The window function achieves the same result in one query.
+
+**2. Capped result set with a user-visible warning**
+Return at most N results (e.g. 500) and show "showing top 500 of many results". Simpler, but degrades UX for broad queries and doesn't reduce latency proportionally (still scans N rows).
+
+**3. Full SQL rewrite combining FTS + JPA Specification filters**
+Possible via a native query that embeds all filter predicates. Eliminates the in-memory SENDER/RECEIVER sort paths and the two-phase approach. High complexity, tight coupling to schema details, loses type-safe JPA Specification composition. Deferred to a future refactor if scale demands it.
+
+## Consequences
+
+- **`rows_per_call` for pure-text FTS searches drops to ≤ page size** — the primary metric.
+- **SENDER and RECEIVER sort paths stay in-memory** for combined text+filter queries. For pure-text queries with SENDER/RECEIVER sort, the current approach (fetch all matched IDs, build spec, load all matched entities, sort in-memory) still runs. This is acceptable while the archive stays under ~10 K documents.
+- **RELEVANCE sort with text+filters still loads the full filtered entity set in-memory.** The filtered set is typically much smaller than the raw FTS match set, so the cost is bounded by filter selectivity, not total match count.
+- **`findAllMatchingIdsByFts` is retained** for: (a) the bulk-edit "select all" fast path (`findIdsForFilter`), (b) the document density chart (`getDensity`), and (c) the SENDER/RECEIVER in-memory sort paths.