Google Inc.

Interwoven, Inc.

Robert Bradshaw

Craig D Chambers

Frances J Perry

Ashish Raniwala

Robert R Henry

Stephen Robert Adams

Nathan Weizenbaum

Daniel Mills

Reuven Lax

Ezra Cooper

Tyler Arthur Akidau

Manuel Alfred Fahndrich

Jordan Tigani

Ben Chambers

Jack Jia

Mark Brian Shields

Samuel Green McVeety

Ahmet Altay

John Sully

Harris Samuel Nover

Rafael De Jesús Fernández Moctezuma

Andrei Maksimenka

Mikhail Shmulyan

Jeffrey Paul Gardner

Samuel Carl Whittle

Cosmin Ionel Arad

Britt Park

A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.

Parallel processing of data

A method includes receiving data corresponding one of streaming data or batch data and a content of the received data for computation. The method also includes determining an event time of the data for slicing the data, determining a processing time to output results of the received data, and emitting at least a portion of the results of the received data based on the processing time and the event time.

Data flow windowing and triggering

One example method includes identifying synchronous code including instructions specifying a computing operation to be performed on a set of data; transforming the synchronous code into a pipeline application including one or more pipeline objects; identifying a first input data set on which to execute the pipeline application; executing the pipeline application on a first input data set to produce a first output data set; after executing the pipeline application on the first input data set, identifying a second input data set on which to execute the pipeline application; determining a set of differences between the first input data set and second input data set; and executing the pipeline application on the set of differences to produce a second output data set.

Incremental parallel processing of data

A job that receives as input a data stream is executed. For the job, it is iteratively determined a backlog growth over a first period of time; a backlog volume; and whether to adjust a quantity of processing resources. For each iteration for which the quantity of processing resources allocated to the job are determined to be adjusted, adjusting the quantity of processing resources allocated to the job. For each iteration for which the quantity of processing resources allocated to the job are determined not to be adjusted, maintaining the quantity of processing resources allocated to the job.

Resource allocation for computer processing

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Robert Bradshaw