Abstract: 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.

Abstract: 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.

Abstract: Aspects of the disclosure relate to evaluating the rollout of a change to a data processing framework (DPF). A first set of jobs is selected out of a plurality of jobs. The first set of jobs is processed by the DPF with the proposed change enabled and the other jobs of the plurality of jobs by the DPF without the change enabled. A performance cost value is determined for each of the jobs in the first set of jobs processed by the DPF with the proposed change enabled and for each of the other jobs of the plurality of jobs processed by the DPF with the proposed change disabled. Based on the performance cost values of the jobs in the first set of jobs and the performance cost values of the other jobs of the plurality of jobs, a determination is made whether the proposed change is beneficial to the DPF.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the disclosure relate to evaluating the rollout of a change to a data processing framework (DPF). A first set of jobs is selected out of a plurality of jobs. The first set of jobs is processed by the DPF with the proposed change enabled and the other jobs of the plurality of jobs by the DPF without the change enabled. A performance cost value is determined for each of the jobs in the first set of jobs processed by the DPF with the proposed change enabled and for each of the other jobs of the plurality of jobs processed by the DPF with the proposed change disabled. Based on the performance cost values of the jobs in the first set of jobs and the performance cost values of the other jobs of the plurality of jobs, a determination is made whether the proposed change is beneficial to the DPF.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An untrusted application is received at a data center including one or more processing modules and providing a native processing environment. The untrusted application includes a data parallel pipeline. Secured processing environments are used to execute the untrusted application.

Abstract: 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.

Abstract: An untrusted application is received at a data center including one or more processing modules and providing a native processing environment. The untrusted application includes a data parallel pipeline. Secured processing environments are used to execute the untrusted application.