feat(analytics): add PostgreSQL JDBC write-back to all four batch jobs

Each batch job now writes its results to a Postgres table in addition to
the existing Parquet/CSV output. OpeningBookJob → analytics_opening_stats,
PlayerStatsJob → analytics_player_stats, PlayerClusteringJob →
analytics_player_clusters + analytics_cluster_archetypes, PlayerGraphJob
→ analytics_player_graph. MLlib Vector columns are excluded from the JDBC
write by reusing the already-selected scalar DataFrame in
PlayerClusteringJob.

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

modules/analytics/src/main/scala/de/nowchess/analytics/OpeningBookJob.scala

@@ -72,13 +72,23 @@ object OpeningBookJob:
       .mode("overwrite")
       .parquet(s"$outputDir/opening_book")
 
-    stats
+    val top1000 = stats.limit(1000)
-      .limit(1000)
+
-      .write
+    top1000.write
       .mode("overwrite")
       .option("header", "true")
       .csv(s"$outputDir/opening_book_top1000")
 
+    top1000.write
+      .mode("overwrite")
+      .format("jdbc")
+      .option("url", jdbcUrl)
+      .option("dbtable", "analytics_opening_stats")
+      .option("user", dbUser)
+      .option("password", dbPass)
+      .option("driver", "org.postgresql.Driver")
+      .save()
+
   /** Extracts the first `maxPlies` moves from a PGN column as a space-separated string.
     *
     * PGN format produced by PgnExporter: [Event "?"]\n[White "?"]\n...\n\n1. e4 e5 2. Nf3 Nc6 *

modules/analytics/src/main/scala/de/nowchess/analytics/PlayerClusteringJob.scala

@@ -115,9 +115,9 @@ object PlayerClusteringJob:
 
     archetypes.show(20, false)
 
-    predictions
+    val clustersDf = predictions.select("player_id", "total_games", "win_rate", "avg_move_count", "cluster")
-      .select("player_id", "total_games", "win_rate", "avg_move_count", "cluster")
+
-      .write
+    clustersDf.write
       .mode("overwrite")
       .parquet(s"$outputDir/player_clusters")
 
@@ -126,6 +126,26 @@ object PlayerClusteringJob:
       .option("header", "true")
       .csv(s"$outputDir/cluster_archetypes")
 
+    clustersDf.write
+      .mode("overwrite")
+      .format("jdbc")
+      .option("url", jdbcUrl)
+      .option("dbtable", "analytics_player_clusters")
+      .option("user", dbUser)
+      .option("password", dbPass)
+      .option("driver", "org.postgresql.Driver")
+      .save()
+
+    archetypes.write
+      .mode("overwrite")
+      .format("jdbc")
+      .option("url", jdbcUrl)
+      .option("dbtable", "analytics_cluster_archetypes")
+      .option("user", dbUser)
+      .option("password", dbPass)
+      .option("driver", "org.postgresql.Driver")
+      .save()
+
   private def buildPlayerStats(games: org.apache.spark.sql.DataFrame): org.apache.spark.sql.DataFrame =
     val asWhite = games.select(
       F.col("white_id").as("player_id"),

modules/analytics/src/main/scala/de/nowchess/analytics/PlayerGraphJob.scala

@@ -116,6 +116,16 @@ object PlayerGraphJob:
       .mode("overwrite")
       .parquet(s"$outputDir/player_graph")
 
+    result.write
+      .mode("overwrite")
+      .format("jdbc")
+      .option("url", jdbcUrl)
+      .option("dbtable", "analytics_player_graph")
+      .option("user", dbUser)
+      .option("password", dbPass)
+      .option("driver", "org.postgresql.Driver")
+      .save()
+
     // How many players belong to each connected component?
     // A large dominant component + many singletons is the expected shape.
     val componentSizes = result

modules/analytics/src/main/scala/de/nowchess/analytics/PlayerStatsJob.scala

@@ -83,3 +83,13 @@ object PlayerStatsJob:
     stats.write
       .mode("overwrite")
       .parquet(s"$outputDir/player_stats")
+
+    stats.write
+      .mode("overwrite")
+      .format("jdbc")
+      .option("url", jdbcUrl)
+      .option("dbtable", "analytics_player_stats")
+      .option("user", dbUser)
+      .option("password", dbPass)
+      .option("driver", "org.postgresql.Driver")
+      .save()