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org.apache.spark.sql.rapids.execution

InternalColumnarRddConverter

object InternalColumnarRddConverter extends Logging

Please don't use this class directly use com.nvidia.spark.rapids.ColumnarRdd instead. We had to place the implementation in a spark specific package to poke at the internals of spark more than anyone should know about.

This provides a way to get back out GPU Columnar data RDD[Table]. Each Table will have the same schema as the dataframe passed in. If the schema of the dataframe is something that Rapids does not currently support an IllegalArgumentException will be thrown.

The size of each table will be determined by what is producing that table but typically will be about the number of bytes set by RapidsConf.GPU_BATCH_SIZE_BYTES.

Table is not a typical thing in an RDD so special care needs to be taken when working with it. By default it is not serializable so repartitioning the RDD or any other operator that involves a shuffle will not work. This is because it is very expensive to serialize and deserialize a GPU Table using a conventional spark shuffle. Also most of the memory associated with the Table is on the GPU itself, so each table must be closed when it is no longer needed to avoid running out of GPU memory. By convention it is the responsibility of the one consuming the data to close it when they no longer need it.

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  4. def apply(df: DataFrame): RDD[Table]
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  7. def convert(df: DataFrame): RDD[Table]
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  10. def extractRDDColumnarBatch(df: DataFrame): (Option[RDD[ColumnarBatch]], RDD[Row])
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