ParquetFileFormat

Instance Constructors

new ParquetFileFormat()

Type Members

case class FileTypes(data: Seq[FileStatus], metadata: Seq[FileStatus], commonMetadata: Seq[FileStatus]) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def buildReader(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

Returns a function that can be used to read a single file in as an Iterator of InternalRow.
Returns a function that can be used to read a single file in as an Iterator of InternalRow.
dataSchema
The global data schema. It can be either specified by the user, or reconciled/merged from all underlying data files. If any partition columns are contained in the files, they are preserved in this schema.
partitionSchema
The schema of the partition column row that will be present in each PartitionedFile. These columns should be appended to the rows that are produced by the iterator.
requiredSchema
The schema of the data that should be output for each row. This may be a subset of the columns that are present in the file if column pruning has occurred.
filters
A set of filters than can optionally be used to reduce the number of rows output
options
A set of string -> string configuration options.

Definition Classes
ParquetFileFormat → FileFormat
def buildReaderWithPartitionValues(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

Exactly the same as buildReader except that the reader function returned by this method appends partition values to InternalRows produced by the reader function buildReader returns.
Exactly the same as buildReader except that the reader function returned by this method appends partition values to InternalRows produced by the reader function buildReader returns.

Definition Classes
ParquetFileFormat → FileFormat
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(other: Any): Boolean

Definition Classes
ParquetFileFormat → AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
ParquetFileFormat → AnyRef → Any
def inferSchema(sparkSession: SparkSession, parameters: Map[String, String], files: Seq[FileStatus]): Option[StructType]

When possible, this method should return the schema of the given files.
When possible, this method should return the schema of the given files. When the format does not support inference, or no valid files are given should return None. In these cases Spark will require that user specify the schema manually.

Definition Classes
ParquetFileFormat → FileFormat
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isSplitable(sparkSession: SparkSession, options: Map[String, String], path: Path): Boolean

Returns whether a file with path could be splitted or not.
Returns whether a file with path could be splitted or not.

Definition Classes
ParquetFileFormat → FileFormat
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def prepareWrite(sparkSession: SparkSession, job: Job, options: Map[String, String], dataSchema: StructType): OutputWriterFactory

Prepares a write job and returns an OutputWriterFactory.
Prepares a write job and returns an OutputWriterFactory. Client side job preparation can be put here. For example, user defined output committer can be configured here by setting the output committer class in the conf of spark.sql.sources.outputCommitterClass.

Definition Classes
ParquetFileFormat → FileFormat
def shortName(): String

The string that represents the format that this data source provider uses.
The string that represents the format that this data source provider uses. This is overridden by children to provide a nice alias for the data source. For example:
```
override def shortName(): String = "parquet"
```
Definition Classes
ParquetFileFormat → DataSourceRegister
Since
1.5.0
def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean

Returns whether the reader will return the rows as batch or not.
Returns whether the reader will return the rows as batch or not.

Definition Classes
ParquetFileFormat → FileFormat
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
ParquetFileFormat → AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object ParquetFileFormat | package parquet

class ParquetFileFormat extends FileFormat with DataSourceRegister with Logging with Serializable

Instance Constructors

new ParquetFileFormat()

Type Members

case class FileTypes(data: Seq[FileStatus], metadata: Seq[FileStatus], commonMetadata: Seq[FileStatus]) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def buildReader(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

def buildReaderWithPartitionValues(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(other: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def inferSchema(sparkSession: SparkSession, parameters: Map[String, String], files: Seq[FileStatus]): Option[StructType]

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isSplitable(sparkSession: SparkSession, options: Map[String, String], path: Path): 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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def prepareWrite(sparkSession: SparkSession, job: Job, options: Map[String, String], dataSchema: StructType): OutputWriterFactory

def shortName(): String

def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean

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 Logging

Inherited from DataSourceRegister

Inherited from FileFormat

Inherited from AnyRef

Inherited from Any

Ungrouped