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 manager agent access a grain hierarchy that represents a state for a computation that is to performed by a distributed processing system, wherein the grain hierarchy includes manager agent grains including metadata for manager agent processes that manage the performance of the computation by the distributed processing system, and worker agent grains including, for tasks to be performed by the distributed processing system, metadata for worker agents that each correspond to a subset of the plurality of data processors for performing the task. A manager agent performs processes defined by a manager agent grain to manage the computation by worker agents and storing, within the grain, metadata describing the manager agent process performed by the manager agent, and worker agents perform tasks assigned to the worker agents based on an assignment of a respective worker agent grain to the worker agent.

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: 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 manager agent access a grain hierarchy that represents a state for a computation that is to performed by a distributed processing system, wherein the grain hierarchy includes manager agent grains including metadata for manager agent processes that manage the performance of the computation by the distributed processing system, and worker agent grains including, for tasks to be performed by the distributed processing system, metadata for worker agents that each correspond to a subset of the plurality of data processors for performing the task. A manager agent performs processes defined by a manager agent grain to manage the computation by worker agents and storing, within the grain, metadata describing the manager agent process performed by the manager agent, and worker agents perform tasks assigned to the worker agents based on an assignment of a respective worker agent grain to the worker agent.

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 manager agent access a grain hierarchy that represents a state for a computation that is to performed by a distributed processing system, wherein the grain hierarchy includes manager agent grains including metadata for manager agent processes that manage the performance of the computation by the distributed processing system, and worker agent grains including, for tasks to be performed by the distributed processing system, metadata for worker agents that each correspond to a subset of the plurality of data processors for performing the task. A manager agent performs processes defined by a manager agent grain to manage the computation by worker agents and storing, within the grain, metadata describing the manager agent process performed by the manager agent, and worker agents perform tasks assigned to the worker agents based on an assignment of a respective worker agent grain to the worker agent.

Abstract: A manager agent access a grain hierarchy that represents a state for a computation that is to performed by a distributed processing system, wherein the grain hierarchy includes manager agent grains including metadata for manager agent processes that manage the performance of the computation by the distributed processing system, and worker agent grains including, for tasks to be performed by the distributed processing system, metadata for worker agents that each correspond to a subset of the plurality of data processors for performing the task. A manager agent performs processes defined by a manager agent grain to manage the computation by worker agents and storing, within the grain, metadata describing the manager agent process performed by the manager agent, and worker agents perform tasks assigned to the worker agents based on an assignment of a respective worker agent grain to the worker agent.

Abstract: A manager agent access a grain hierarchy that represents a state for a computation that is to performed by a distributed processing system, wherein the grain hierarchy includes manager agent grains including metadata for manager agent processes that manage the performance of the computation by the distributed processing system, and worker agent grains including, for tasks to be performed by the distributed processing system, metadata for worker agents that each correspond to a subset of the plurality of data processors for performing the task. A manager agent performs processes defined by a manager agent grain to manage the computation by worker agents and storing, within the grain, metadata describing the manager agent process performed by the manager agent, and worker agents perform tasks assigned to the worker agents based on an assignment of a respective worker agent grain to the worker agent.

Abstract: The present disclosure relates to dynamically adjusting shard allocation during parallel processing operations. One example method includes determining a target completion time for a batch data processing job of an input data set performed by a plurality of tasks, each of the plurality of tasks processing a different input shard including a different portion of the input data set; identifying a first task having an estimated completion time greater than the target completion time of the batch data processing job; and splitting the first input shard into a first split input shard and a second split input shard different from the first split input shard, the first split input shard including a first portion of the first input shard, and the second split input shard including a second portion of the first input shard different from the first portion.

Abstract: A manager agent access a grain hierarchy that represents a state for a computation that is to performed by a distributed processing system, wherein the grain hierarchy includes manager agent grains including metadata for manager agent processes that manage the performance of the computation by the distributed processing system, and worker agent grains including, for tasks to be performed by the distributed processing system, metadata for worker agents that each correspond to a subset of the plurality of data processors for performing the task. A manager agent performs processes defined by a manager agent grain to manage the computation by worker agents and storing, within the grain, metadata describing the manager agent process performed by the manager agent, and worker agents perform tasks assigned to the worker agents based on an assignment of a respective worker agent grain to the worker agent.

Abstract: The present disclosure relates to cataloging data sets for reuse in pipeline applications. One example method includes identifying a data set produced by a particular pipeline object included in a first pipeline instance, the first pipeline instance including a plurality of pipeline objects, each pipeline object configured to perform a computation, and the particular pipeline object configured to perform a particular computation; determining a set of metadata for the data set, the set of metadata including identifying information for the data set to identify the data set to pipeline instances separate from the first pipeline instance; and allowing pipeline instances separate from the first pipeline instance to retrieve the data set based at least in part on the set of metadata, wherein the pipeline instances avoid performing the particular computation by using the retrieved data set.

Abstract: Methods, systems, and apparatus are described for managing metadata for a distributed processing system. In one aspect, a method includes receiving a computation specification that includes a set of grains that specifies an initial state for a computation that is to be performed by a distributed processing system, wherein each grain comprises metadata that specifies a portion of the initial state for the computation; storing a grain hierarchy that represents a state for the computation based on a grain type associated with each grain, the grain hierarchy comprising subscription grains for subscriptions for the grain hierarchy, each subscription corresponding to one or more grains included in the grain hierarchy, and each subscription specifying one or more actions to be performed by the hub device; and performing, during performance of the computation, at least one of the actions specified by at least one of the subscriptions.

Abstract: Aspects of the disclosure relate to learning the most useful backup tasks when processing a job in a distributed computing application. For example, training data may be generated by running a job and running backup tasks for some of the tasks for that job. List of features may be generated for each backup task at different times. A representative list of features may be labeled according to the backup tasks usefulness to the job. The lists and usefulness labels may be used to generate a model of usefulness. The model may then be used to evaluate the usefulness of the tasks for the next job. This information may then be used to schedule backup tasks for execution. In addition, lists of features for these backup tasks may be generated and then used to update the model in a continuous learning loop.

Abstract: The present disclosure relates to dynamically adjusting shard allocation during parallel processing operations. One example method includes determining a target completion time for a batch data processing job of an input data set performed by a plurality of tasks, each of the plurality of tasks processing a different input shard including a different portion of the input data set; identifying a first task having an estimated completion time greater than the target completion time of the batch data processing job; and splitting the first input shard into a first split input shard and a second split input shard different from the first split input shard, the first split input shard including a first portion of the first input shard, and the second split input shard including a second portion of the first input shard different from the first portion.

Abstract: Methods, systems, and apparatus are described for managing metadata for a distributed processing system. In one aspect, a method includes receiving a computation specification that includes a set of grains that specifies an initial state for a computation that is to be performed by a distributed processing system, wherein each grain comprises metadata that specifies a portion of the initial state for the computation; storing a grain hierarchy that represents a state for the computation based on a grain type associated with each grain, the grain hierarchy comprising subscription grains for subscriptions for the grain hierarchy, each subscription corresponding to one or more grains included in the grain hierarchy, and each subscription specifying one or more actions to be performed by the hub device; and performing, during performance of the computation, at least one of the actions specified by at least one of the subscriptions.