Avoid skewed filter trees to speed up `createFilter` in ORC

// Create and read 1 row table with 1000 columns
sql("set spark.sql.orc.filterPushdown=true")
val selectExpr = (1 to 1000).map(i => s"id c$i")
spark.range(1).selectExpr(selectExpr: _*).write.mode("overwrite").orc("/tmp/orc")
print(s"With 0 filters, ")
spark.time(spark.read.orc("/tmp/orc").count)

// Increase the number of filters
(20 to 30).foreach { width =>
  val whereExpr = (1 to width).map(i => s"c$i is not null").mkString(" and ")
  print(s"With $width filters, ")
  spark.time(spark.read.orc("/tmp/orc").where(whereExpr).count)
}

With 0 filters, Time taken: 653 ms                                              
With 20 filters, Time taken: 962 ms
With 21 filters, Time taken: 1282 ms
With 22 filters, Time taken: 1982 ms
With 23 filters, Time taken: 3855 ms
With 24 filters, Time taken: 6719 ms
With 25 filters, Time taken: 12669 ms
With 26 filters, Time taken: 25032 ms
With 27 filters, Time taken: 49585 ms
With 28 filters, Time taken: 98980 ms    // over 1 min 38 seconds
With 29 filters, Time taken: 198368 ms   // over 3 mins
With 30 filters, Time taken: 393744 ms   // over 6 mins