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com.nvidia.spark

Retryable

trait Retryable extends AnyRef

An interface that can be used to retry the processing on non-deterministic expressions on the GPU.

GPU memory is a limited resource. When it runs out the RAPIDS Accelerator for Apache Spark will use several different strategies to try and free more GPU memory to let the query complete. One of these strategies is to roll back the processioning for one task, pause the task thread, then retry the task when more memory is available. This works transparently for any stateless deterministic processing. But technically an expression/UDF can be non-deterministic and/or keep state in between calls. This interface provides a checkpoint method to save any needed state, and a restore method to reset the state in the case of a retry.

Please note that a retry is not isolated to a single expression, so a restore can be called even after the expression returned one or more batches of results. And each time checkpoint it called any previously saved state can be overwritten.

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AnyRef, Any
Known Subclasses
AllowSpillOnlyLazySpillableColumnarBatchImpl, BaseCrossJoinGatherMap, BatchedRunningWindowBinaryFixer, BatchedRunningWindowFixer, DenseRankFixer, JoinGatherer, JoinGathererImpl, LazySpillable, LazySpillableColumnarBatch, LazySpillableColumnarBatchImpl, LazySpillableGatherMap, LazySpillableGatherMapImpl, LeftCrossGatherMap, MultiJoinGather, RankFixer, RightCrossGatherMap, SumBinaryFixer, GpuRand
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Abstract Value Members

  1. abstract def checkpoint(): Unit

    Save the state, so it can be restored in the case of a retry.

    Save the state, so it can be restored in the case of a retry. (This is called inside a Spark task context on executors.)

  2. abstract def restore(): Unit

    Restore the state that was saved by calling to "checkpoint".

    Restore the state that was saved by calling to "checkpoint". (This is called inside a Spark task context on executors.)

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