Abstract: A distributed computing system is described that leverages a nearline storage layer to minimize the downtime required for bootstrapping a new computing cluster in the distributed computing system. The system executes a computing cluster comprising a set of computing nodes and determines a set of one or more data segments to be written to a nearline storage system. The system writes the data segments to the nearline storage system. In certain examples, the system receives a request to create a second computing cluster and responsive to the request, bootstraps the second computing cluster using the set of data segments stored on the nearline storage system. The system additionally leverages the nearline storage layer to accelerate query processing by the computing nodes of a computing cluster.

Abstract: A distributed computing system is described that leverages a nearline storage layer to minimize the downtime required for bootstrapping a new computing cluster in the distributed computing system. The system executes a computing cluster comprising a set of computing nodes and determines a set of one or more data segments to be written to a nearline storage system. The system writes the data segments to the nearline storage system. In certain examples, the system receives a request to create a second computing cluster and responsive to the request, bootstraps the second computing cluster using the set of data segments stored on the nearline storage system. The system additionally leverages the nearline storage layer to accelerate query processing by the computing nodes of a computing cluster.

Abstract: Techniques for providing improved distributed caching are disclosed. A distributed computing system can be implemented with a cluster including a plurality of worker nodes configured to host one or more executors for processing data related to a query. The worker nodes can host a cache accessible to the executors. The data can be processed as a plurality of data segments. The worker nodes can be uniformly assigned a plurality of token bounds defining a range of integer token values. A hashing algorithm can be used to compute a token for each data segment associated with the query. Tasks can be launched on the executors preferentially, such that the task for processing a data segment having a token within the token bounds associated with the preferred executor. Executors can be instructed to review the associated cache to identify outlier data segments and inform other nodes in the cluster.

Abstract: A distributed computing system is described that leverages a nearline storage layer to minimize the downtime required for bootstrapping a new computing cluster in the distributed computing system. The system executes a computing cluster comprising a set of computing nodes and determines a set of one or more data segments to be written to a nearline storage system. The system writes the data segments to the nearline storage system. In certain examples, the system receives a request to create a second computing cluster and responsive to the request, bootstraps the second computing cluster using the set of data segments stored on the nearline storage system. The system additionally leverages the nearline storage layer to accelerate query processing by the computing nodes of a computing cluster.

Abstract: Techniques for providing improved distributed caching are disclosed. A distributed computing system can be implemented with a cluster including a plurality of worker nodes configured to host one or more executors for processing data related to a query. The worker nodes can host a cache accessible to the executors. The data can be processed as a plurality of data segments. The worker nodes can be uniformly assigned a plurality of token bounds defining a range of integer token values. A hashing algorithm can be used to compute a token for each data segment associated with the query. Tasks can be launched on the executors preferentially, such that the task for processing a data segment having a token within the token bounds associated with the preferred executor. Executors can be instructed to review the associated cache to identify outlier data segments and inform other nodes in the cluster.

Abstract: A distributed computing system is described that leverages a nearline storage layer to minimize the downtime required for bootstrapping a new computing cluster in the distributed computing system. The system executes a computing cluster comprising a set of computing nodes and determines a set of one or more data segments to be written to a nearline storage system. The system writes the data segments to the nearline storage system. In certain examples, the system receives a request to create a second computing cluster and responsive to the request, bootstraps the second computing cluster using the set of data segments stored on the nearline storage system. The system additionally leverages the nearline storage layer to accelerate query processing by the computing nodes of a computing cluster.

Abstract: A distributed computing system is described that leverages a nearline storage layer to minimize the downtime required for bootstrapping a new computing cluster in the distributed computing system. The system executes a computing cluster comprising a set of computing nodes and determines a set of one or more data segments to be written to a nearline storage system. The system writes the data segments to the nearline storage system. In certain examples, the system receives a request to create a second computing cluster and responsive to the request, bootstraps the second computing cluster using the set of data segments stored on the nearline storage system. The system additionally leverages the nearline storage layer to accelerate query processing by the computing nodes of a computing cluster.