feat(analytics): add accuracy and blunder analysis job for Lichess data

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

@@ -0,0 +1,191 @@
+package de.nowchess.analytics
+
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions as F
+
+/** Per-move accuracy & blunder analysis mined from Lichess `[%eval ...]` move annotations.
+  *
+  * Unlike the flat single-`groupBy` summaries (opening rates, colour advantage), this job reconstructs the *quality of
+  * every move* from the engine evaluations Lichess embeds in the movetext (`{ [%eval 0.24] }`, mate scores `[%eval
+  * #-3]`) and turns them into the same accuracy signals lichess.com surfaces: average centipawn loss (ACPL), and counts
+  * of inaccuracies / mistakes / blunders.
+  *
+  * Pipeline (all Spark SQL string/array functions + window funcs — no UDFs, Catalyst-friendly):
+  *   1. Keep only games carrying `[%eval` comments.
+  *   2. `regexp_extract_all` pulls every eval in ply order; mate scores collapse to ±10 pawns, normal evals are clamped
+  *      to ±10 so a single huge swing cannot dominate the mean. All evals are White-POV pawns.
+  *   3. `posexplode` → one row per ply; a per-game window `lag` gives the eval *before* the move.
+  *   4. Centipawn loss for the side that moved = how much the eval moved against them (white wants it up, black down),
+  *      floored at 0 and scaled to centipawns.
+  *   5. Roll up to (game, side): ACPL + inaccuracy(≥50cp) / mistake(≥100cp) / blunder(≥200cp) counts, tagged with that
+  *      side's Elo and whether they won.
+  *
+  * Outputs (Parquet + CSV + JDBC):
+  *   - `accuracy_by_rating` — ACPL, avg blunders/mistakes/inaccuracies per game and win-rate, per Elo band. Shows how
+  *     move quality scales with rating.
+  *   - `blunder_outcome` — win-rate bucketed by number of blunders in the game. Quantifies "one blunder costs you the
+  *     game".
+  *
+  * Requires the eval-annotated Lichess dump (`NOWCHESS_PGN_PATH` → an evals dump); JDBC games carry no per-move evals.
+  */
+object AccuracyBlunderJob:
+
+  def main(args: Array[String]): Unit =
+    val jdbcUrl   = sys.env.getOrElse("NOWCHESS_JDBC_URL", "jdbc:postgresql://localhost:5432/nowchess")
+    val dbUser    = sys.env.getOrElse("NOWCHESS_DB_USER", "nowchess")
+    val dbPass    = sys.env.getOrElse("NOWCHESS_DB_PASS", "nowchess")
+    val outputDir = if args.length > 0 then args(0) else "/tmp/nowchess-accuracy"
+
+    val spark = SparkSession
+      .builder()
+      .appName("NowChess Accuracy & Blunders")
+      .getOrCreate()
+
+    run(spark, jdbcUrl, dbUser, dbPass, outputDir)
+    spark.stop()
+
+  def run(spark: SparkSession, jdbcUrl: String, dbUser: String, dbPass: String, outputDir: String): Unit =
+    val games = GameSource
+      .loadExtended(spark, jdbcUrl, dbUser, dbPass)
+      .select("pgn", "result", "white_elo", "black_elo")
+      .filter(F.col("result").isNotNull.and(F.col("pgn").contains("[%eval")))
+      .withColumn("game_id", F.monotonically_increasing_id())
+
+    // White-POV pawn evals in ply order; mate → ±10, normal evals clamped to ±10.
+    val evalStrs = F.expr("""regexp_extract_all(pgn, '\\[%eval ([^\\]]+)\\]', 1)""")
+    val evalCps = F.expr(
+      "transform(eval_strs, x -> CASE " +
+        "WHEN x LIKE '#-%' THEN -10.0 " +
+        "WHEN x LIKE '#%' THEN 10.0 " +
+        "ELSE greatest(-10.0, least(10.0, cast(x as double))) END)",
+    )
+
+    val withEvals = games
+      .withColumn("eval_strs", evalStrs)
+      .withColumn("eval_cp", evalCps)
+      .filter(F.size(F.col("eval_cp")) >= 2)
+
+    val plies = withEvals.select(
+      F.col("game_id"),
+      F.col("result"),
+      F.col("white_elo"),
+      F.col("black_elo"),
+      F.posexplode(F.col("eval_cp")).as(Seq("ply", "eval_after")),
+    )
+
+    val byGame     = Window.partitionBy("game_id").orderBy("ply")
+    val mover      = F.when(F.col("ply") % 2 === 0, "white").otherwise("black")
+    val evalBefore = F.coalesce(F.lag("eval_after", 1).over(byGame), F.lit(0.15))
+    val cpl = F.greatest(
+      F.lit(0.0),
+      F.when(F.col("mover") === "white", evalBefore - F.col("eval_after"))
+        .otherwise(F.col("eval_after") - evalBefore),
+    ) * 100
+
+    val moves = plies
+      .withColumn("mover", mover)
+      .withColumn("cpl", cpl)
+
+    val perSide = moves
+      .groupBy("game_id", "mover", "result", "white_elo", "black_elo")
+      .agg(
+        F.round(F.avg("cpl"), 1).as("acpl"),
+        F.sum(F.when(F.col("cpl") >= 200, 1).otherwise(0)).as("blunders"),
+        F.sum(F.when(F.col("cpl") >= 100 && F.col("cpl") < 200, 1).otherwise(0)).as("mistakes"),
+        F.sum(F.when(F.col("cpl") >= 50 && F.col("cpl") < 100, 1).otherwise(0)).as("inaccuracies"),
+      )
+      .withColumn(
+        "self_elo",
+        F.when(F.col("mover") === "white", F.col("white_elo")).otherwise(F.col("black_elo")),
+      )
+      .withColumn("won", F.when(F.col("mover") === F.col("result"), 1).otherwise(0))
+
+    writeAccuracyByRating(perSide, jdbcUrl, dbUser, dbPass, outputDir)
+    writeBlunderOutcome(perSide, jdbcUrl, dbUser, dbPass, outputDir)
+
+  private def writeAccuracyByRating(
+      perSide: org.apache.spark.sql.DataFrame,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      outputDir: String,
+  ): Unit =
+    val elo = F.col("self_elo")
+    val band = F
+      .when(elo < 1200, "<1200")
+      .when(elo < 1500, "1200–1499")
+      .when(elo < 1800, "1500–1799")
+      .when(elo < 2100, "1800–2099")
+      .otherwise("2100+")
+    val bandOrder = F
+      .when(elo < 1200, 1)
+      .when(elo < 1500, 2)
+      .when(elo < 1800, 3)
+      .when(elo < 2100, 4)
+      .otherwise(5)
+
+    val stats = perSide
+      .filter(elo.isNotNull)
+      .withColumn("band", band)
+      .withColumn("band_order", bandOrder)
+      .groupBy("band", "band_order")
+      .agg(
+        F.count("*").as("player_games"),
+        F.round(F.avg("acpl"), 1).as("avg_acpl"),
+        F.round(F.avg("blunders"), 2).as("avg_blunders"),
+        F.round(F.avg("mistakes"), 2).as("avg_mistakes"),
+        F.round(F.avg("inaccuracies"), 2).as("avg_inaccuracies"),
+        F.round(F.avg("won"), 3).as("win_rate"),
+      )
+      .orderBy(F.asc("band_order"))
+      .drop("band_order")
+
+    write(stats, outputDir, "accuracy_by_rating", jdbcUrl, dbUser, dbPass, "analytics_accuracy_by_rating")
+
+  private def writeBlunderOutcome(
+      perSide: org.apache.spark.sql.DataFrame,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      outputDir: String,
+  ): Unit =
+    val b      = F.col("blunders")
+    val bucket = F.when(b === 0, "0").when(b === 1, "1").when(b === 2, "2").otherwise("3+")
+    val order  = F.when(b === 0, 0).when(b === 1, 1).when(b === 2, 2).otherwise(3)
+
+    val stats = perSide
+      .withColumn("blunder_bucket", bucket)
+      .withColumn("bucket_order", order)
+      .groupBy("blunder_bucket", "bucket_order")
+      .agg(
+        F.count("*").as("player_games"),
+        F.round(F.avg("won"), 3).as("win_rate"),
+        F.round(F.avg("acpl"), 1).as("avg_acpl"),
+      )
+      .orderBy(F.asc("bucket_order"))
+      .drop("bucket_order")
+
+    write(stats, outputDir, "blunder_outcome", jdbcUrl, dbUser, dbPass, "analytics_blunder_outcome")
+
+  private def write(
+      df: org.apache.spark.sql.DataFrame,
+      outputDir: String,
+      name: String,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      table: String,
+  ): Unit =
+    df.write.mode("overwrite").parquet(s"$outputDir/$name")
+    df.write.mode("overwrite").option("header", "true").csv(s"$outputDir/${name}_csv")
+    if !GameSource.isPgnMode then
+      df.write
+        .mode("overwrite")
+        .format("jdbc")
+        .option("url", jdbcUrl)
+        .option("dbtable", table)
+        .option("user", dbUser)
+        .option("password", dbPass)
+        .option("driver", "org.postgresql.Driver")
+        .save()

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

@@ -0,0 +1,199 @@
+package de.nowchess.analytics
+
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions as F
+
+/** Time-management & clock-pressure analysis mined from Lichess `[%clk ...]` move annotations.
+  *
+  * Lichess records each player's remaining clock after every move (`{ [%clk 0:02:31] }`). This job reconstructs
+  * per-move thinking time and remaining-time from those stamps to answer questions the existing time-control summary
+  * cannot: how long do players actually think, how often do they fall into time scrambles (<10 s left), how often do
+  * they flag (lose on time), and does burning the clock correlate with winning?
+  *
+  * Pipeline (Spark SQL string/array funcs + window funcs — no UDFs):
+  *   1. `regexp_extract_all` pulls every `h:mm:ss` clock in ply order, converted to seconds.
+  *   2. `posexplode` → one row per ply; even plies are White's clock, odd plies Black's.
+  *   3. A per-(game,side) window `lag` gives the same side's previous clock; the difference is that move's thinking time.
+  *      Remaining clock <10 s marks a time-scramble move.
+  *   4. Roll up to (game, side): avg move time, scramble fraction, min clock, Elo, win flag, and whether the side lost on
+  *      time (`Termination "Time forfeit"`).
+  *
+  * Outputs (Parquet + CSV + JDBC):
+  *   - `clock_by_rating` — avg move time, scramble fraction, flag-loss rate and win-rate per Elo band.
+  *   - `scramble_outcome` — win-rate bucketed by how much of the game was played in time-scramble. Quantifies the cost of
+  *     time trouble.
+  *
+  * Requires a clock-annotated Lichess dump (`NOWCHESS_PGN_PATH`).
+  */
+object ClockPressureJob:
+
+  def main(args: Array[String]): Unit =
+    val jdbcUrl   = sys.env.getOrElse("NOWCHESS_JDBC_URL", "jdbc:postgresql://localhost:5432/nowchess")
+    val dbUser    = sys.env.getOrElse("NOWCHESS_DB_USER", "nowchess")
+    val dbPass    = sys.env.getOrElse("NOWCHESS_DB_PASS", "nowchess")
+    val outputDir = if args.length > 0 then args(0) else "/tmp/nowchess-clock-pressure"
+
+    val spark = SparkSession
+      .builder()
+      .appName("NowChess Clock Pressure")
+      .getOrCreate()
+
+    run(spark, jdbcUrl, dbUser, dbPass, outputDir)
+    spark.stop()
+
+  def run(spark: SparkSession, jdbcUrl: String, dbUser: String, dbPass: String, outputDir: String): Unit =
+    val games = GameSource
+      .loadExtended(spark, jdbcUrl, dbUser, dbPass)
+      .select("pgn", "result", "white_elo", "black_elo", "termination")
+      .filter(F.col("result").isNotNull.and(F.col("pgn").contains("[%clk")))
+      .withColumn("game_id", F.monotonically_increasing_id())
+
+    val clkStrs = F.expr("""regexp_extract_all(pgn, '\\[%clk ([^\\]]+)\\]', 1)""")
+    // "h:mm:ss" → seconds.
+    val clkSecs = F.expr(
+      "transform(clk_strs, x -> " +
+        "cast(split(x, ':')[0] as double) * 3600 + " +
+        "cast(split(x, ':')[1] as double) * 60 + " +
+        "cast(split(x, ':')[2] as double))",
+    )
+
+    val withClk = games
+      .withColumn("clk_strs", clkStrs)
+      .withColumn("clk_sec", clkSecs)
+      .filter(F.size(F.col("clk_sec")) >= 4)
+
+    val plies = withClk.select(
+      F.col("game_id"),
+      F.col("result"),
+      F.col("white_elo"),
+      F.col("black_elo"),
+      F.col("termination"),
+      F.posexplode(F.col("clk_sec")).as(Seq("ply", "clk_after")),
+    )
+
+    val mover    = F.when(F.col("ply") % 2 === 0, "white").otherwise("black")
+    val bySide   = Window.partitionBy("game_id", "mover").orderBy("ply")
+    val moveTime = F.lag("clk_after", 1).over(bySide) - F.col("clk_after")
+
+    val moves = plies
+      .withColumn("mover", mover)
+      .withColumn("move_time", moveTime)
+
+    val perSide = moves
+      .groupBy("game_id", "mover", "result", "white_elo", "black_elo", "termination")
+      .agg(
+        F.round(F.avg("move_time"), 1).as("avg_move_time"),
+        F.count("*").as("moves"),
+        F.round(F.min("clk_after"), 1).as("min_clk"),
+        F.sum(F.when(F.col("clk_after") < 10, 1).otherwise(0)).as("scramble_moves"),
+      )
+      .withColumn("scramble_fraction", F.round(F.col("scramble_moves") / F.col("moves"), 3))
+      .withColumn(
+        "self_elo",
+        F.when(F.col("mover") === "white", F.col("white_elo")).otherwise(F.col("black_elo")),
+      )
+      .withColumn("won", F.when(F.col("mover") === F.col("result"), 1).otherwise(0))
+      .withColumn(
+        "flag_loss",
+        F.when(
+          F.coalesce(F.col("termination"), F.lit("")).contains("Time forfeit") && F.col("won") === 0,
+          1,
+        ).otherwise(0),
+      )
+
+    writeClockByRating(perSide, jdbcUrl, dbUser, dbPass, outputDir)
+    writeScrambleOutcome(perSide, jdbcUrl, dbUser, dbPass, outputDir)
+
+  private def writeClockByRating(
+      perSide: org.apache.spark.sql.DataFrame,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      outputDir: String,
+  ): Unit =
+    val elo = F.col("self_elo")
+    val band = F
+      .when(elo < 1200, "<1200")
+      .when(elo < 1500, "1200–1499")
+      .when(elo < 1800, "1500–1799")
+      .when(elo < 2100, "1800–2099")
+      .otherwise("2100+")
+    val bandOrder = F
+      .when(elo < 1200, 1)
+      .when(elo < 1500, 2)
+      .when(elo < 1800, 3)
+      .when(elo < 2100, 4)
+      .otherwise(5)
+
+    val stats = perSide
+      .filter(elo.isNotNull)
+      .withColumn("band", band)
+      .withColumn("band_order", bandOrder)
+      .groupBy("band", "band_order")
+      .agg(
+        F.count("*").as("player_games"),
+        F.round(F.avg("avg_move_time"), 1).as("avg_move_time_s"),
+        F.round(F.avg("scramble_fraction"), 3).as("avg_scramble_fraction"),
+        F.round(F.avg("flag_loss"), 3).as("flag_loss_rate"),
+        F.round(F.avg("won"), 3).as("win_rate"),
+      )
+      .orderBy(F.asc("band_order"))
+      .drop("band_order")
+
+    write(stats, outputDir, "clock_by_rating", jdbcUrl, dbUser, dbPass, "analytics_clock_by_rating")
+
+  private def writeScrambleOutcome(
+      perSide: org.apache.spark.sql.DataFrame,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      outputDir: String,
+  ): Unit =
+    val sf = F.col("scramble_fraction")
+    val bucket = F
+      .when(sf === 0, "none")
+      .when(sf < 0.05, "<5%")
+      .when(sf < 0.20, "5–20%")
+      .otherwise(">20%")
+    val order = F
+      .when(sf === 0, 0)
+      .when(sf < 0.05, 1)
+      .when(sf < 0.20, 2)
+      .otherwise(3)
+
+    val stats = perSide
+      .withColumn("scramble_bucket", bucket)
+      .withColumn("bucket_order", order)
+      .groupBy("scramble_bucket", "bucket_order")
+      .agg(
+        F.count("*").as("player_games"),
+        F.round(F.avg("won"), 3).as("win_rate"),
+        F.round(F.avg("flag_loss"), 3).as("flag_loss_rate"),
+      )
+      .orderBy(F.asc("bucket_order"))
+      .drop("bucket_order")
+
+    write(stats, outputDir, "scramble_outcome", jdbcUrl, dbUser, dbPass, "analytics_scramble_outcome")
+
+  private def write(
+      df: org.apache.spark.sql.DataFrame,
+      outputDir: String,
+      name: String,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      table: String,
+  ): Unit =
+    df.write.mode("overwrite").parquet(s"$outputDir/$name")
+    df.write.mode("overwrite").option("header", "true").csv(s"$outputDir/${name}_csv")
+    if !GameSource.isPgnMode then
+      df.write
+        .mode("overwrite")
+        .format("jdbc")
+        .option("url", jdbcUrl)
+        .option("dbtable", table)
+        .option("user", dbUser)
+        .option("password", dbPass)
+        .option("driver", "org.postgresql.Driver")
+        .save()

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

@@ -0,0 +1,154 @@
+package de.nowchess.analytics
+
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions as F
+
+/** Smurf / sandbagging anomaly detection via population z-scores.
+  *
+  * Smurfs (strong players on fresh accounts) and sandbaggers leave a statistical signature: a win-rate, an upset-rate
+  * (beating higher-rated opponents) and a self-Elo climb that sit far above the population norm. This job builds those
+  * three features per player, standardises each against the whole player base, and flags the players whose combined
+  * deviation is extreme.
+  *
+  * Features per player (from each game's own/opponent Elo):
+  *   - win_rate — fraction of decisive results won
+  *   - upset_rate — wins vs higher-rated opponents / games vs higher-rated opponents
+  *   - elo_climb — max self-Elo − min self-Elo across their games (rapid rating gain)
+  *
+  * Standardisation uses a single unbounded window (`Window.partitionBy()`), i.e. mean/stddev over every qualifying
+  * player, so z = (x − μ) / σ. The composite anomaly score sums the three z-scores. No UDFs — pure SQL aggregates +
+  * window functions, so Catalyst plans the whole job.
+  *
+  * Outputs (Parquet + CSV + JDBC):
+  *   - `anomaly_scores` — every qualifying player with features, z-scores and composite, ranked most-anomalous first.
+  *   - `flagged_smurfs` — the suspicious subset (high composite, or the classic high-winrate / few-games / steep-climb
+  *     profile).
+  *
+  * Meaningful only when Elo is present (Lichess dump); requires `minGames` (arg 1, default 15) to avoid small-sample
+  * noise.
+  */
+object SmurfAnomalyJob:
+
+  def main(args: Array[String]): Unit =
+    val jdbcUrl   = sys.env.getOrElse("NOWCHESS_JDBC_URL", "jdbc:postgresql://localhost:5432/nowchess")
+    val dbUser    = sys.env.getOrElse("NOWCHESS_DB_USER", "nowchess")
+    val dbPass    = sys.env.getOrElse("NOWCHESS_DB_PASS", "nowchess")
+    val outputDir = if args.length > 0 then args(0) else "/tmp/nowchess-smurf-anomaly"
+    val minGames  = if args.length > 1 then args(1).toInt else 15
+
+    val spark = SparkSession
+      .builder()
+      .appName("NowChess Smurf Anomaly Detection")
+      .getOrCreate()
+
+    run(spark, jdbcUrl, dbUser, dbPass, outputDir, minGames)
+    spark.stop()
+
+  def run(
+      spark: SparkSession,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      outputDir: String,
+      minGames: Int,
+  ): Unit =
+    val games = GameSource
+      .loadExtended(spark, jdbcUrl, dbUser, dbPass)
+      .select("white_id", "black_id", "result", "white_elo", "black_elo")
+      .filter(F.col("result").isNotNull)
+
+    val asWhite = games.select(
+      F.col("white_id").as("player_id"),
+      F.col("white_elo").as("self_elo"),
+      F.col("black_elo").as("opp_elo"),
+      F.when(F.col("result") === "white", 1).otherwise(0).as("won"),
+    )
+    val asBlack = games.select(
+      F.col("black_id").as("player_id"),
+      F.col("black_elo").as("self_elo"),
+      F.col("white_elo").as("opp_elo"),
+      F.when(F.col("result") === "black", 1).otherwise(0).as("won"),
+    )
+
+    val playerGames = asWhite
+      .union(asBlack)
+      .filter(F.col("self_elo").isNotNull.and(F.col("opp_elo").isNotNull))
+
+    val higher = F.col("opp_elo") > F.col("self_elo")
+
+    val features = playerGames
+      .groupBy("player_id")
+      .agg(
+        F.count("*").as("total_games"),
+        F.round(F.avg("won"), 3).as("win_rate"),
+        F.round(F.avg("self_elo"), 0).as("avg_self_elo"),
+        (F.max("self_elo") - F.min("self_elo")).as("elo_climb"),
+        F.sum(F.when(higher, 1).otherwise(0)).as("vs_higher"),
+        F.sum(F.when(higher && F.col("won") === 1, 1).otherwise(0)).as("upsets"),
+      )
+      .filter(F.col("total_games") >= minGames)
+      .withColumn("upset_rate", F.round(F.col("upsets") / F.greatest(F.col("vs_higher"), F.lit(1)), 3))
+
+    val all = Window.partitionBy()
+    def z(col: String): org.apache.spark.sql.Column =
+      val mean = F.avg(col).over(all)
+      val std  = F.stddev(col).over(all)
+      F.round((F.col(col) - mean) / F.when(std === 0 || std.isNull, F.lit(1.0)).otherwise(std), 2)
+
+    val scored = features
+      .withColumn("z_win_rate", z("win_rate"))
+      .withColumn("z_upset_rate", z("upset_rate"))
+      .withColumn("z_elo_climb", z("elo_climb"))
+      .withColumn(
+        "anomaly_score",
+        F.round(F.col("z_win_rate") + F.col("z_upset_rate") + F.col("z_elo_climb"), 2),
+      )
+      .withColumn(
+        "flagged",
+        (F.col("anomaly_score") >= 4.0)
+          .or(F.col("win_rate") >= 0.8 && F.col("total_games") < 50 && F.col("elo_climb") >= 300),
+      )
+
+    val ordered = scored
+      .select(
+        "player_id",
+        "total_games",
+        "win_rate",
+        "avg_self_elo",
+        "elo_climb",
+        "upset_rate",
+        "z_win_rate",
+        "z_upset_rate",
+        "z_elo_climb",
+        "anomaly_score",
+        "flagged",
+      )
+      .orderBy(F.desc("anomaly_score"))
+
+    write(ordered, outputDir, "anomaly_scores", jdbcUrl, dbUser, dbPass, "analytics_smurf_anomaly")
+
+    val flagged = ordered.filter(F.col("flagged") === true)
+    write(flagged, outputDir, "flagged_smurfs", jdbcUrl, dbUser, dbPass, "analytics_flagged_smurfs")
+
+  private def write(
+      df: org.apache.spark.sql.DataFrame,
+      outputDir: String,
+      name: String,
+      jdbcUrl: String,
+      dbUser: String,
+      dbPass: String,
+      table: String,
+  ): Unit =
+    df.write.mode("overwrite").parquet(s"$outputDir/$name")
+    df.write.mode("overwrite").option("header", "true").csv(s"$outputDir/${name}_csv")
+    if !GameSource.isPgnMode then
+      df.write
+        .mode("overwrite")
+        .format("jdbc")
+        .option("url", jdbcUrl)
+        .option("dbtable", table)
+        .option("user", dbUser)
+        .option("password", dbPass)
+        .option("driver", "org.postgresql.Driver")
+        .save()