Abstract: Shuffling of into partitions by first grouping input vertices of a limited number. Each group of input vertices may then be simply shuffled into a corresponding group of intermediate vertices, such as by broadcasting. A second grouping occurs in which the intermediate vertices are grouped by partition. The intermediate vertices then shuffle into corresponding output vertices for the respective partitions of that group. If the intermediate vertices are still too large, then this shuffling may involve recursively performing the shuffling just described, until ultimately the number of intermediate vertices shuffling into the output vertices is likewise limited. Thus, the final shuffling into the output vertices might also be simply performed by broadcasting.

Abstract: Aspects extend to methods, systems, and computer program products for optimally pipelining result sets with fault tolerance in distributed query execution. Distributed computing jobs are optimized by dividing the distributed computing jobs into one or more bubbles for execution. Each bubble can be independently executed, potentially in parallel with other bubbles, when resources to handle the bubble are available. Intra-bubble communication can be streamed between vertices within a bubble. Inter-bubble communication can be stored to durable storage. Bubbles provide a failure boundary for a job graph and re-executing a bubble along with storage of intermediate results in durable storage can be used to recover from failures. When a vertex inside a bubble fails, computation can resume by rescheduling the execution of the failed bubble from the durable inputs for that bubble. Durable storage provides a light-weight failover to handle non-deterministic behavior.

Abstract: Shuffling of into partitions by first grouping input vertices of a limited number. Each group of input vertices may then be simply shuffled into a corresponding group of intermediate vertices, such as by broadcasting. A second grouping occurs in which the intermediate vertices are grouped by partition. The intermediate vertices then shuffle into corresponding output vertices for the respective partitions of that group. If the intermediate vertices are still too large, then this shuffling may involve recursively performing the shuffling just described, until ultimately the number of intermediate vertices shuffling into the output vertices is likewise limited. Thus, the final shuffling into the output vertices might also be simply performed by broadcasting.

Abstract: Aspects extend to methods, systems, and computer program products for optimally pipelining result sets with fault tolerance in distributed query execution. Distributed computing jobs are optimized by dividing the distributed computing jobs into one or more bubbles for execution. Each bubble can be independently executed, potentially in parallel with other bubbles, when resources to handle the bubble are available. Intra-bubble communication can be streamed between vertices within a bubble. Inter-bubble communication can be stored to durable storage. Bubbles provide a failure boundary for a job graph and re-executing a bubble along with storage of intermediate results in durable storage can be used to recover from failures. When a vertex inside a bubble fails, computation can resume by rescheduling the execution of the failed bubble from the durable inputs for that bubble. Durable storage provides a light-weight failover to handle non-deterministic behavior.