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object GeoParquetFileFormat extends Logging with Serializable

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  1. GeoParquetFileFormat
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  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. def isTraceEnabled(): Boolean
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  15. def log: Logger
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  16. def logDebug(msg: => String, throwable: Throwable): Unit
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  17. def logDebug(msg: => String): Unit
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  18. def logError(msg: => String, throwable: Throwable): Unit
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  19. def logError(msg: => String): Unit
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  20. def logInfo(msg: => String, throwable: Throwable): Unit
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  21. def logInfo(msg: => String): Unit
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  22. def logName: String
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  23. def logTrace(msg: => String, throwable: Throwable): Unit
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  24. def logTrace(msg: => String): Unit
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  25. def logWarning(msg: => String, throwable: Throwable): Unit
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  26. def logWarning(msg: => String): Unit
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  27. def mergeSchemasInParallel(parameters: Map[String, String], filesToTouch: Seq[FileStatus], sparkSession: SparkSession): Option[StructType]

    Figures out a merged Parquet schema with a distributed Spark job.

    Figures out a merged Parquet schema with a distributed Spark job.

    Note that locality is not taken into consideration here because:

    1. For a single Parquet part-file, in most cases the footer only resides in the last block of that file. Thus we only need to retrieve the location of the last block. However, Hadoop FileSystem only provides API to retrieve locations of all blocks, which can be potentially expensive.

    2. This optimization is mainly useful for S3, where file metadata operations can be pretty slow. And basically locality is not available when using S3 (you can't run computation on S3 nodes).

  28. final def ne(arg0: AnyRef): Boolean
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  29. final def notify(): Unit
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  30. final def notifyAll(): Unit
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  31. final def synchronized[T0](arg0: => T0): T0
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  32. def toString(): String
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  34. final def wait(arg0: Long, arg1: Int): Unit
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  35. final def wait(arg0: Long): Unit
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