feat(analytics): feed Lichess PGN dumps into Spark batch jobs

Add GameSource: normalises game records into a shared schema and
selects backend via NOWCHESS_PGN_PATH. Unset = PostgreSQL game_records
(unchanged); set = a Lichess PGN dump (file or http(s) URL).

- Parse Lichess PGN with Spark SQL string functions only (no UDFs).
- URLs fetched once via SparkContext.addFile, distributed to executors.
- .pgn.zst decompressed in-process via zstd-jni, plain .pgn redistributed.
- All four batch jobs read through GameSource and skip JDBC write-back
  in PGN mode (Parquet/CSV output only).

Enables driving the analytics demo straight from
https://database.lichess.org standard dumps.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

modules/analytics/build.gradle.kts

@@ -22,6 +22,9 @@
 //   NOWCHESS_JDBC_URL  (default: jdbc:postgresql://localhost:5432/nowchess)
 //   NOWCHESS_DB_USER   (default: nowchess)
 //   NOWCHESS_DB_PASS   (default: nowchess)
+//   NOWCHESS_PGN_PATH  (optional) — file or http(s) URL of a Lichess PGN dump (.pgn or .pgn.zst).
+//                      When set, all batch jobs read games from the dump instead of PostgreSQL and
+//                      skip JDBC write-back (Parquet/CSV output only). Demo data source.
 
 plugins {
     id("scala")
@@ -71,6 +74,10 @@ dependencies {
 
     // PostgreSQL JDBC driver bundled so it is available on executor classpath.
     implementation("org.postgresql:postgresql:42.7.4")
+
+    // zstd-jni: decompress Lichess .pgn.zst dumps in-process. Provided at runtime by Spark
+    // (it uses zstd-jni internally for shuffle/event-log compression), so compile-only here.
+    compileOnly("com.github.luben:zstd-jni:1.5.6-9")
 }
 
 application {

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

@@ -0,0 +1,119 @@
+package de.nowchess.analytics
+
+import org.apache.spark.SparkFiles
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.functions as F
+
+/** Normalised game-record source for the batch jobs.
+  *
+  * Every batch job consumes the same five-column shape:
+  *   - white_id, black_id : player identifiers
+  *   - result             : one of "white", "black", "draw"
+  *   - move_count         : number of plies
+  *   - pgn                : full PGN ("[Event …]…\n\n1. e4 …"), header and movetext separated by a blank line
+  *
+  * Two backends, selected by the `NOWCHESS_PGN_PATH` environment variable:
+  *   - unset → PostgreSQL `game_records` table (production)
+  *   - set   → a Lichess PGN dump file/URL (demo). Point it at a `lichess_db_standard_rated_*.pgn[.zst]`
+  *             to drive every batch job from real Lichess games.
+  *
+  * Lichess parsing uses only Spark SQL string functions — no UDFs — so Catalyst can push predicates,
+  * matching the no-UDF approach already used in OpeningBookJob.
+  */
+object GameSource:
+
+  private val PgnPathEnv = "NOWCHESS_PGN_PATH"
+
+  /** True when a Lichess PGN dump is configured; jobs use this to skip JDBC write-back. */
+  def isPgnMode: Boolean = sys.env.contains(PgnPathEnv)
+
+  def load(spark: SparkSession, jdbcUrl: String, dbUser: String, dbPass: String): DataFrame =
+    sys.env.get(PgnPathEnv) match
+      case Some(path) => fromLichessPgn(spark, path)
+      case None       => fromJdbc(spark, jdbcUrl, dbUser, dbPass)
+
+  def fromJdbc(spark: SparkSession, jdbcUrl: String, dbUser: String, dbPass: String): DataFrame =
+    spark.read
+      .format("jdbc")
+      .option("url", jdbcUrl)
+      .option("dbtable", "game_records")
+      .option("user", dbUser)
+      .option("password", dbPass)
+      .option("driver", "org.postgresql.Driver")
+      .option("fetchsize", "10000")
+      .load()
+      .select("white_id", "black_id", "result", "move_count", "pgn")
+
+  /** Parses a Lichess PGN dump into the normalised game shape.
+    *
+    * `path` may be:
+    *   - an http(s)/ftp URL — fetched once via SparkContext.addFile and distributed to executors, then read
+    *     from the local replica (no S3/PVC needed; handy for a staging demo)
+    *   - any Hadoop-readable path (file://, hdfs://, s3a://, …)
+    *
+    * `.zst` dumps (Lichess' native format) are decompressed in-process via zstd-jni; `.gz`/`.bz2` are
+    * handled by Spark's text reader codecs.
+    *
+    * Records are split on the "[Event " tag that opens every game, so each row holds one complete game
+    * (the empty fragment before the first game is filtered out). Header tags are read with regexp_extract;
+    * the movetext (after the blank line) is cleaned of clock/eval comments and move numbers to count plies.
+    */
+  def fromLichessPgn(spark: SparkSession, path: String): DataFrame =
+    val resolved = resolvePath(spark, path)
+    val record   = F.col("value")
+
+    val resultTag = F.regexp_extract(record, "Result \"([^\"]*)\"", 1)
+    val result = F
+      .when(resultTag === "1-0", "white")
+      .when(resultTag === "0-1", "black")
+      .when(resultTag === "1/2-1/2", "draw")
+      .otherwise(F.lit(null).cast("string"))
+
+    val moveText  = F.coalesce(F.split(record, "\n\n").getItem(1), F.lit(""))
+    val noComment = F.regexp_replace(moveText, "\\{[^}]*\\}", "")
+    val noResult  = F.regexp_replace(noComment, "(1-0|0-1|1/2-1/2|\\*)", "")
+    val noNumbers = F.regexp_replace(noResult, "\\d+\\.+", " ")
+    val plies     = F.size(F.filter(F.split(F.trim(noNumbers), "\\s+"), tok => F.length(tok) > 0))
+
+    spark.read
+      .option("lineSep", "[Event ")
+      .text(resolved)
+      .filter(F.length(F.trim(record)) > 0)
+      .select(
+        F.regexp_extract(record, "White \"([^\"]*)\"", 1).as("white_id"),
+        F.regexp_extract(record, "Black \"([^\"]*)\"", 1).as("black_id"),
+        result.as("result"),
+        plies.as("move_count"),
+        F.concat(F.lit("[Event "), record).as("pgn"),
+      )
+      .filter((F.col("white_id") =!= "").and(F.col("black_id") =!= ""))
+
+  /** Turns an http(s)/ftp URL into a cluster-local path by fetching it once with SparkContext.addFile,
+    * which distributes the file to every executor. `.zst` is decompressed in-process and the plain `.pgn`
+    * is redistributed. Non-URL paths are returned unchanged.
+    */
+  private def resolvePath(spark: SparkSession, path: String): String =
+    if !path.matches("^(https?|ftp)://.*") then path
+    else
+      spark.sparkContext.addFile(path)
+      val local = SparkFiles.get(baseName(path))
+      if !local.endsWith(".zst") then "file://" + local
+      else distribute(spark, decompressZstd(local))
+
+  private def baseName(path: String): String = path.substring(path.lastIndexOf('/') + 1)
+
+  private def distribute(spark: SparkSession, localPath: String): String =
+    spark.sparkContext.addFile("file://" + localPath)
+    "file://" + SparkFiles.get(baseName(localPath))
+
+  /** Decompresses a `.zst` file to a temp `.pgn` using zstd-jni (bundled with Spark at runtime). */
+  private def decompressZstd(srcPath: String): String =
+    val out = java.io.File.createTempFile("lichess-", ".pgn")
+    out.deleteOnExit()
+    val in = com.github.luben.zstd.ZstdInputStream(
+      java.io.BufferedInputStream(java.io.FileInputStream(srcPath)),
+    )
+    try java.nio.file.Files.copy(in, out.toPath, java.nio.file.StandardCopyOption.REPLACE_EXISTING)
+    finally in.close()
+    out.getAbsolutePath

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

@@ -37,15 +37,8 @@ object OpeningBookJob:
       outputDir: String,
       maxPlies: Int,
   ): Unit =
-    val games = spark.read
-      .format("jdbc")
-      .option("url", jdbcUrl)
-      .option("dbtable", "game_records")
-      .option("user", dbUser)
-      .option("password", dbPass)
-      .option("driver", "org.postgresql.Driver")
-      .option("fetchsize", "10000")
-      .load()
+    val games = GameSource
+      .load(spark, jdbcUrl, dbUser, dbPass)
       .select("pgn", "result")
       .filter(F.col("result").isNotNull.and(F.col("pgn").isNotNull))
 
@@ -79,15 +72,16 @@ object OpeningBookJob:
       .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()
+    if !GameSource.isPgnMode then
+      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.
     *

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

@@ -50,15 +50,8 @@ object PlayerClusteringJob:
       outputDir: String,
       k: Int,
   ): Unit =
-    val games = spark.read
-      .format("jdbc")
-      .option("url", jdbcUrl)
-      .option("dbtable", "game_records")
-      .option("user", dbUser)
-      .option("password", dbPass)
-      .option("driver", "org.postgresql.Driver")
-      .option("fetchsize", "10000")
-      .load()
+    val games = GameSource
+      .load(spark, jdbcUrl, dbUser, dbPass)
       .select("white_id", "black_id", "result", "move_count")
       .filter(F.col("result").isNotNull)
 
@@ -126,25 +119,26 @@ 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()
+    if !GameSource.isPgnMode then
+      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()
+      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(

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

@@ -53,15 +53,8 @@ object PlayerGraphJob:
       dbPass: String,
       outputDir: String,
   ): Unit =
-    val gamesRdd: RDD[Row] = spark.read
-      .format("jdbc")
-      .option("url", jdbcUrl)
-      .option("dbtable", "game_records")
-      .option("user", dbUser)
-      .option("password", dbPass)
-      .option("driver", "org.postgresql.Driver")
-      .option("fetchsize", "10000")
-      .load()
+    val gamesRdd: RDD[Row] = GameSource
+      .load(spark, jdbcUrl, dbUser, dbPass)
       .select("white_id", "black_id", "result")
       .filter(F.col("result").isNotNull)
       .rdd
@@ -116,15 +109,16 @@ 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()
+    if !GameSource.isPgnMode then
+      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.

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

@@ -34,15 +34,8 @@ object PlayerStatsJob:
       dbPass: String,
       outputDir: String,
   ): Unit =
-    val games = spark.read
-      .format("jdbc")
-      .option("url", jdbcUrl)
-      .option("dbtable", "game_records")
-      .option("user", dbUser)
-      .option("password", dbPass)
-      .option("driver", "org.postgresql.Driver")
-      .option("fetchsize", "10000")
-      .load()
+    val games = GameSource
+      .load(spark, jdbcUrl, dbUser, dbPass)
       .select("white_id", "black_id", "result", "move_count")
       .filter(F.col("result").isNotNull)
 
@@ -84,12 +77,13 @@ object PlayerStatsJob:
       .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()
+    if !GameSource.isPgnMode then
+      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()

modules/official-bots/src/main/scala/de/nowchess/bot/service/TournamentBotConfig.scala

@@ -20,7 +20,7 @@ object TournamentBotConfig:
       tournamentId <- env.get("TOURNAMENT_ID").filter(_.nonEmpty)
       token        <- env.get("TOURNAMENT_BOT_TOKEN").filter(_.nonEmpty)
       botId        <- jwtSubject(token)
-      serverUrl  = env.getOrElse("TOURNAMENT_SERVER_URL", "http://localhost:8089")
+      serverUrl  = env.getOrElse("TOURNAMENT_SERVER_URL", "http://141.37.123.132:8086")
       difficulty = env.getOrElse("TOURNAMENT_BOT_DIFFICULTY", "medium")
     yield TournamentBotConfig(serverUrl, tournamentId, token, botId, difficulty)
 

modules/official-bots/src/main/scala/de/nowchess/bot/service/TournamentBotGamePlayer.scala

@@ -39,10 +39,11 @@ class TournamentBotGamePlayer:
   // scalafix:on DisableSyntax.var
 
   val defaultServerUrl: String =
-    System.getenv().asScala.getOrElse("TOURNAMENT_SERVER_URL", "http://localhost:8089")
+    System.getenv().asScala.getOrElse("TOURNAMENT_SERVER_URL", "http://141.37.123.132:8086")
 
   @PostConstruct
   def initialize(): Unit =
+    parkOnStartup()
     config match
       case None =>
         log.info("Tournament bot disabled — set TOURNAMENT_ID and TOURNAMENT_BOT_TOKEN to enable")
@@ -50,6 +51,32 @@ class TournamentBotGamePlayer:
         log.infof("Tournament bot enabled — server=%s tournament=%s bot=%s", cfg.serverUrl, cfg.tournamentId, cfg.botId)
         startAsync(cfg)
 
+  private def parkOnStartup(): Unit =
+    park(defaultServerUrl, "expert") match
+      case Some(id) => log.infof("Parked expert bot on %s as id %s", defaultServerUrl, id)
+      case None     => log.warnf("Failed to park expert bot on %s", defaultServerUrl)
+
+  private def park(serverUrl: String, difficulty: String): Option[String] =
+    System.getenv().asScala.get("TOURNAMENT_BOT_TOKEN").filter(_.nonEmpty).flatMap { token =>
+      Try {
+        val body = s"""{"name":"${botName(difficulty)}"}"""
+        val response = client
+          .target(serverUrl)
+          .path("api")
+          .path("bots")
+          .request(MediaType.APPLICATION_JSON)
+          .header("Authorization", s"Bearer $token")
+          .post(Entity.entity(body, MediaType.APPLICATION_JSON))
+        if response.getStatus == 201 || response.getStatus == 200 then
+          val id = objectMapper.readTree(response.readEntity(classOf[String])).path("id").asText()
+          response.close()
+          Option(id).filter(_.nonEmpty)
+        else { log.warnf("Parking bot %s returned status %d", botName(difficulty), response.getStatus); response.close(); None }
+      }.getOrElse(None)
+    }
+
+  private def botName(difficulty: String): String = s"NowChess ${difficulty.capitalize}"
+
   def joinTournament(
       tournamentId: String,
       botToken: String,