Abstract: Data can be processed in parallel across a cluster of nodes using a parallel processing framework. Using Web services calls between components allows the number of nodes to be scaled as necessary, and allows developers to build applications on the framework using a Web services interface. A job scheduler works together with a queuing service to distribute jobs to nodes as the nodes have capacity, such that jobs can be performed in parallel as quickly as the nodes are able to process the jobs. Data can be loaded efficiently across the cluster, and levels of nodes can be determined dynamically to process queries and other requests on the system.

Abstract: Data can be processed in parallel across a cluster of nodes using a parallel processing framework. Using Web services calls between components allows the number of nodes to be scaled as necessary, and allows developers to build applications on the framework using a Web services interface. A job scheduler works together with a queuing service to distribute jobs to nodes as the nodes have capacity, such that jobs can be performed in parallel as quickly as the nodes are able to process the jobs. Data can be loaded efficiently across the cluster, and levels of nodes can be determined dynamically to process queries and other requests on the system.

Abstract: Data can be processed in parallel across a cluster of nodes using a parallel processing framework. Using Web services calls between components allows the number of nodes to be scaled as necessary, and allows developers to build applications on the framework using a Web services interface. A job scheduler works together with a queuing service to distribute jobs to nodes as the nodes have capacity, such that jobs can be performed in parallel as quickly as the nodes are able to process the jobs. Data can be loaded efficiently across the cluster, and levels of nodes can be determined dynamically to process queries and other requests on the system.

Abstract: Data can be processed in parallel across a cluster of nodes using a parallel processing framework. Using Web services calls between components allows the number of nodes to be scaled as necessary, and allows developers to build applications on the framework using a Web services interface. A job scheduler works together with a queuing service to distribute jobs to nodes as the nodes have capacity, such that jobs can be performed in parallel as quickly as the nodes are able to process the jobs. Data can be loaded efficiently across the cluster, and levels of nodes can be determined dynamically to process queries and other requests on the system.

Abstract: Method and apparatus for measuring true end-to-end latency for calls to Web services are described. In embodiments, a Web service client and a Web service provider may collaborate to collect timing/latency data for calls to the Web service. This data may be collected, stored, and analyzed by a latency measurement service to generate displays and/or reports on true end-to-end latency measurements for Web service calls. Embodiments may collect Internet/network infrastructure latency for Web service calls up to and including the “last mile” to the Web service client and the Web service processing time. Additionally, by analyzing latency data collected from a number of Web services clients and/or Web service providers, embodiments may provide a macro-level view into overall Internet performance. In one embodiment, the latency measurement service may be a Web service.