RDFGraphLoader

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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12. def loadFromDisk(session: SparkSession, path: URI, minPartitions: Int): RDFGraph

    

    Load an RDF graph from a single file or directory.

    Load an RDF graph from a single file or directory.

    session

    the Spark session

    path

    the path to the file or directory

    minPartitions

    min number of partitions for Hadoop RDDs (SparkContext.defaultMinPartitions)

    returns

    an RDF graph

13. def loadFromDisk(session: SparkSession, paths: Seq[URI], minPartitions: Int): RDFGraph

    

    Load an RDF graph from multiple files or directories.

    Load an RDF graph from multiple files or directories.

    session

    the Spark session

    paths

    the files or directories

    minPartitions

    min number of partitions for Hadoop RDDs (SparkContext.defaultMinPartitions)

    returns

    an RDF graph

14. def loadFromDisk(session: SparkSession, path: String, minPartitions: Int = 2): RDFGraph

    

    Load an RDF graph from a file or directory.

    Load an RDF graph from a file or directory. The path can also contain multiple paths and even wildcards, e.g. "/my/dir1,/my/paths/part-00[0-5]*,/another/dir,/a/specific/file"

    session

    the Spark session

    path

    the absolute path of the file

    minPartitions

    min number of partitions for Hadoop RDDs (SparkContext.defaultMinPartitions)

    returns

    an RDF graph

15. def loadFromDiskAsDataFrame(session: SparkSession, path: String, minPartitions: Int, sqlSchema: SQLSchema = SQLSchemaDefault): RDFGraphDataFrame

    

    Load an RDF graph from a file or directory with a Spark DataFrame as underlying datastructure.

    Load an RDF graph from a file or directory with a Spark DataFrame as underlying datastructure. The path can also contain multiple paths and even wildcards, e.g. "/my/dir1,/my/paths/part-00[0-5]*,/another/dir,/a/specific/file"

    session

    the Spark session

    path

    the absolute path of the file

    minPartitions

    min number of partitions for Hadoop RDDs (SparkContext.defaultMinPartitions)

    returns

    an RDF graph based on a org.apache.spark.sql.DataFrame

16. def loadFromDiskAsDataset(session: SparkSession, paths: Seq[URI]): RDFGraphDataset

    

    Load an RDF graph from from from a file or directory with a Spark Dataset as underlying datastructure.

    Load an RDF graph from from from a file or directory with a Spark Dataset as underlying datastructure. The path can also contain multiple paths and even wildcards, e.g. "/my/dir1,/my/paths/part-00[0-5]*,/another/dir,/a/specific/file"

    session

    the Spark session

    paths

    the absolute path of the file

    returns

    an RDF graph based on a Dataset

17. def loadFromDiskAsDataset(session: SparkSession, path: String): RDFGraphDataset

    

    Load an RDF graph from from multiple files or directories with a Spark Dataset as underlying datastructure.

    Load an RDF graph from from multiple files or directories with a Spark Dataset as underlying datastructure. The path can also contain multiple paths and even wildcards, e.g. "/my/dir1,/my/paths/part-00[0-5]*,/another/dir,/a/specific/file"

    session

    the Spark session

    path

    the absolute path of the file

    returns

    an RDF graph based on a Dataset

18. def loadFromDiskAsRDD(session: SparkSession, path: String, minPartitions: Int): RDFGraphNative

    

    Load an RDF graph from a file or directory with a Spark RDD as underlying datastructure.

    Load an RDF graph from a file or directory with a Spark RDD as underlying datastructure. The path can also contain multiple paths and even wildcards, e.g. "/my/dir1,/my/paths/part-00[0-5]*,/another/dir,/a/specific/file"

    session

    the Spark session

    path

    the files

    minPartitions

    min number of partitions for Hadoop RDDs (SparkContext.defaultMinPartitions)

    returns

    an RDF graph

19. def main(args: Array[String]): Unit

    

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21. final def notify(): Unit

    

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

    

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25. final def wait(): Unit

    

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

    

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27. final def wait(arg0: Long): Unit

    

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