New standalone modules:analytics submodule with two Spark jobs:
- OpeningBookJob: reads game_records.pgn, extracts first N plies using
pure Catalyst SQL expressions (no UDFs), aggregates win/draw/loss rates
per opening sequence, writes Parquet + CSV top-1000 summary.
- PlayerStatsJob: unions each game into a player-centric view, aggregates
total_games/wins/losses/draws/avg_move_count/win_rate per player_id,
writes Parquet.
Module uses Scala 3 calling spark-sql_2.13 via JVM binary compatibility
(DataFrame API only; no spark.implicits._ / typed Datasets). Spark is
compileOnly; the fat jar bundles only scala3-library + postgresql driver.
Submit via spark-submit; see build.gradle.kts header for invocation.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
- Implements the full tournament lifecycle (create, join, withdraw, start,
round progression, finish) as a standalone Quarkus module
- All 11 endpoints from the OpenAPI spec (`docs/tournament-openapi.yaml`) are covered
- Swiss pairing algorithm with Buchholz tiebreak and bye support
- Per-bot NDJSON event stream with targeted `gameStart` events carrying
the correct `color` field
- Game results ingested via Redis writeback stream (`GameResultStreamListener`)
## Known gaps (deferred)
- `GET /results` `nb` param defaults to 100 instead of all
- `PairingDto` exposes an internal `id` field not in the spec
- `GameExport.moves` emits PGN instead of UCI (upstream `GameWritebackEventDto`
does not carry UCI moves)
- `Pairing.white` can be `null` for bye rounds (spec has no bye concept)
## Test plan
- [x] 23 `TournamentResourceTest` integration tests (H2, mocked core client) — all pass
- [x] 5 `SwissPairingServiceTest` unit tests — all pass
- [x] Redis listener excluded in test/dev profiles; no Docker required to run tests
---------
Co-authored-by: LQ63 <lkhermann@web.de>
Co-authored-by: Lala, Shahd <Shahd.Lala@sybit.de>
Reviewed-on: #55
Janis Eccarius
lq64
Janis
shosho996