Google Inc.

Marian Dvorsky

Yonggang Zhao

Alexander Gourkov Balikov

Grzegorz Malewicz

Joshua L Levenberg

Christopher B Colohan

Derek P Thomson

Xiaozhou Li

Liang Lin

Biswapesh Chattopadhyay

Weiran Liu

Ovidiu Gheorghioiu

A method includes receiving a request to perform a shuffle operation on a data stream; receiving at least a portion of the data stream including a plurality of records, each including a key; storing each of the plurality of records in a persistent storage location assigned to a key range corresponding to keys included in the plurality of records; receiving a request from a consumer for a subset of the plurality of records including a range of keys; and upon receiving the request from the consumer, providing the subset of the plurality of records including the range of keys from the one or more persistent storage locations.

Persistent shuffle system

A method includes receiving a request to perform a shuffle operation on a data stream, the request including a set of initial key ranges: generating a shuffler configuration that assigns a shuffler from a set of shufflers to each of the initial key ranges; initiating the set of shufflers to perform the shuffle operation on the data stream; analyzing metadata statistics to determine whether a shuffler configuration update event occurs, the metadata statistics produced by the set of shufflers during the shuffle operation and indicating load statistics for each shuffler in the set of shufflers; and upon occurrence of the shuffler configuration update event and during the shuffle operation, altering the shuffler configuration based at least in part on the metadata statistics to produce an assignment of shufflers to key ranges that is different from the assignment of shufflers to the initial key ranges.

Dynamic shuffle reconfiguration

A large-scale data processing system and method including a plurality of processes, wherein a master process assigns input data blocks to respective map processes and partitions of intermediate data are assigned to respective reduce processes. In each of the plurality of map processes an application-independent map program retrieves a sequence of input data blocks assigned thereto by the master process and applies an application-specific map function to each input data block in the sequence to produce the intermediate data and stores the intermediate data in high speed memory of the interconnected processors. Each of the plurality of reduce processes receives a respective partition of the intermediate data from the high speed memory of the interconnected processors while the map processes continue to process input data blocks an application-specific reduce function is applied to the respective partition of the intermediate data to produce output values.

System and method for limiting the impact of stragglers in large-scale parallel data processing

A method for shard assignment in a large-scale data processing job is provided. Datasets are divided into a plurality of shards and the shards are indexed and aggregated into one or more groups. A worker process is initially assigned an indexed shard from a group. The initial assignment can assigned based on a simple algorithm. The worker's subsequent shard assignment is based on the index of the initially assigned shard.

Method for shard assignment in a large-scale data processing job

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Sunnyvale, CA, United States of America

Marian Dvorsky