Abstract:
Techniques are described for managing distributed execution of programs. In some situations, the techniques include determining configuration information to be used for executing a particular program in a distributed manner on multiple computing nodes and/or include providing information and associated controls to a user regarding ongoing distributed execution of one or more programs to enable the user to modify the ongoing distributed execution in various manners. Determined configuration information may include, for example, configuration parameters such as a quantity of computing nodes and/or other measures of computing resources to be used for the executing, and may be determined in various manners, including by interactively gathering values for at least some types of configuration information from an associated user (e.g., via a GUI that is displayed to the user) and/or by automatically determining values for at least some types of configuration information (e.g., for use as recommendations to a user).
Abstract:
Techniques are described for providing clients with access to functionality for creating, configuring and executing defined workflows that manipulate source data in defined manners, such as under the control of a configurable workflow service that is available to multiple remote clients over one or more public networks. A defined workflow for a client may, for example, include multiple interconnected workflow components that are specified by the client and that each are configured to perform one or more types of data manipulation operations on a specified type of input data. The configurable workflow service may further execute the defined workflow at one or more times and in one or more manners, such as in some situations by provisioning multiple computing nodes provided by the configurable workflow service to each implement at least one of the workflow components for the defined workflow.
Abstract:
Techniques are described for managing aggregation of data in a distributed manner, such as for a particular client based on specified configuration information. The described techniques may include storing aggregated data values for an OLAP cube or other data structure in a distributed manner, such as in some situations in a distributed hash table. The aggregated data values to be stored may be generated in various manners, such as by performing multi-stage data manipulation operations—for example, a map-reduce architecture may be used, with a first stage involving the use of one or more specified map functions to be performed, and with at least a second stage involving the use of one or more specified reduce functions to be performed.
Abstract:
Techniques are described for managing distributed execution of programs. In some situations, the techniques include dynamically modifying the distributed program execution in various manners, such as based on monitored status information. The dynamic modifying of the distributed program execution may include adding and/or removing computing nodes from a cluster that is executing the program, modifying the amount of computing resources that are available for the distributed program execution, terminating or temporarily suspending execution of the program (e.g., if an insufficient quantity of computing nodes of the cluster are available to perform execution), etc.
Abstract:
Techniques are described for providing clients with access to functionality for creating, configuring and executing defined workflows that manipulate source data in defined manners, such as under the control of a configurable workflow service that is available to multiple remote clients over one or more public networks. A defined workflow for a client may, for example, include multiple interconnected workflow components that are specified by the client and that each are configured to perform one or more types of data manipulation operations on a specified type of input data. The configurable workflow service may further execute the defined workflow at one or more times and in one or more manners, such as in some situations by provisioning multiple computing nodes provided by the configurable workflow service to each implement at least one of the workflow components for the defined workflow.
Abstract:
Techniques are described for managing distributed execution of programs, including by dynamically scaling a cluster of multiple computing nodes performing ongoing distributed execution of a program, such as to increase and/or decrease computing node quantity. An architecture may be used that has core nodes that each participate in a distributed storage system for the distributed program execution, and that has one or more other auxiliary nodes that do not participate in the distributed storage system. Furthermore, as part of performing the dynamic scaling of a cluster, computing nodes that are only temporarily available may be selected and used, such as computing nodes that might be removed from the cluster during the ongoing program execution to be put to other uses and that may also be available for a different fee (e.g., a lower fee) than other computing nodes that are available throughout the ongoing use of the cluster.
Abstract:
Techniques are described for managing aggregation of data in a distributed manner, such as for a particular client based on specified configuration information. The described techniques may include storing aggregated data values for an OLAP cube or other data structure in a distributed manner, such as in some situations in a distributed hash table. The aggregated data values to be stored may be generated in various manners, such as by performing multi-stage data manipulation operations—for example, a map-reduce architecture may be used, with a first stage involving the use of one or more specified map functions to be performed, and with at least a second stage involving the use of one or more specified reduce functions to be performed.
Abstract:
Techniques are described for managing distributed execution of programs, including by dynamically scaling a cluster of multiple computing nodes performing ongoing distributed execution of a program, such as to increase and/or decrease computing node quantity. An architecture may be used that has core nodes that each participate in a distributed storage system for the distributed program execution, and that has one or more other auxiliary nodes that do not participate in the distributed storage system. Furthermore, as part of performing the dynamic scaling of a cluster, computing nodes that are only temporarily available may be selected and used, such as computing nodes that might be removed from the cluster during the ongoing program execution to be put to other uses and that may also be available for a different fee (e.g., a lower fee) than other computing nodes that are available throughout the ongoing use of the cluster.
Abstract:
Techniques are described for providing clients with access to functionality for creating, configuring and executing defined workflows that manipulate source data in defined manners, such as under the control of a configurable workflow service that is available to multiple remote clients over one or more public networks. A defined workflow for a client may, for example, include multiple interconnected workflow components that are specified by the client and that each are configured to perform one or more types of data manipulation operations on a specified type of input data. The configurable workflow service may further execute the defined workflow at one or more times and in one or more manners, such as in some situations by provisioning multiple computing nodes provided by the configurable workflow service to each implement at least one of the workflow components for the defined workflow.
Abstract:
Techniques are described for managing distributed execution of programs, including by dynamically scaling a cluster of multiple computing nodes performing ongoing distributed execution of a program, such as to increase and/or decrease computing node quantity. An architecture may be used that has core nodes that each participate in a distributed storage system for the distributed program execution, and that has one or more other auxiliary nodes that do not participate in the distributed storage system. Furthermore, as part of performing the dynamic scaling of a cluster, computing nodes that are only temporarily available may be selected and used, such as computing nodes that might be removed from the cluster during the ongoing program execution to be put to other uses and that may also be available for a different fee (e.g., a lower fee) than other computing nodes that are available throughout the ongoing use of the cluster.