ParquetFileFormat

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def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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final def isInstanceOf[T0]: Boolean

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def log: Logger

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def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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def logDebug(msg: ⇒ String): Unit

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def logError(msg: ⇒ String, throwable: Throwable): Unit

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def logError(msg: ⇒ String): Unit

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def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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def logInfo(msg: ⇒ String): Unit

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def logName: String

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def logTrace(msg: ⇒ String): Unit

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def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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def logWarning(msg: ⇒ String): Unit

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def mergeSchemasInParallel(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).
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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def readSchemaFromFooter(footer: Footer, converter: ParquetSchemaConverter): StructType

Reads Spark SQL schema from a Parquet footer.
Reads Spark SQL schema from a Parquet footer. If a valid serialized Spark SQL schema string can be found in the file metadata, returns the deserialized StructType, otherwise, returns a StructType converted from the MessageType stored in this footer.
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Related Docs: class ParquetFileFormat | package parquet

object ParquetFileFormat extends internal.Logging with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

def mergeSchemasInParallel(filesToTouch: Seq[FileStatus], sparkSession: SparkSession): Option[StructType]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def readSchemaFromFooter(footer: Footer, converter: ParquetSchemaConverter): StructType

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from internal.Logging

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