Abstract: Techniques for self-tuning an analytics system via observed execution optimization are described. Upon a need for execution resources, a resource manager can select a type of executor from multiple candidate executor types based at least in part on one or more of current execution data associated with the execution of tasks of a user application and/or historic execution data associated with one or more other applications. The current execution data may include event log data originated by the driver application based on the execution of the user application and/or metric data describing characteristics of one or more worker nodes involved with executing the user application or characteristics of one or more other executors implemented by the one or more worker nodes in executing the user application.

Abstract: Techniques for distributed data processing application service networking in a cloud provider network are described. A first request to launch a virtual machine is received, the first request including a session network identifier to identify a network of a distributed data processing application cluster hosted by the provider network. The first virtual machine is launched. An internet protocol (IP) address of the first virtual machine is set, and the IP address of the first virtual machine is formed at least in part by combining an IP subnet assigned to the computer system with the session network identifier. A firewall controlling network traffic to and from the first virtual machine is configured to allow packets having a source or destination address that matches a portion of the IP address of the first virtual machine that includes the session network identifier.

Abstract: Techniques for a distributed data processing application service in a cloud provider network are described. A virtual machine bundle is obtained, the virtual machine bundle including a copy of a memory and one or more registers of a paused virtual machine executing a distributed data processing application runtime. A request to launch a virtual machine to execute a distributed data processing application runtime is received, the request to launch the virtual machine including a bundle identifier that identifies the virtual machine bundle. The virtual machine is launched based at least in part on the virtual machine bundle. The virtual machine sends a message to register the virtual machine with a cluster of virtual machines.

Abstract: Techniques for a distributed data processing application service in a cloud provider network are described. A virtual machine bundle is obtained, the virtual machine bundle including a copy of a memory and one or more registers of a paused virtual machine executing a distributed data processing application runtime. A request to launch a virtual machine to execute a distributed data processing application runtime is received, the request to launch the virtual machine including a bundle identifier that identifies the virtual machine bundle. The virtual machine is launched based at least in part on the virtual machine bundle. The virtual machine sends a message to register the virtual machine with a cluster of virtual machines.

Abstract: Representative embodiments of a distributed processing method of facilitating interactive analytics of streaming data records by receiving the data records at a plurality of distributed computational nodes, establishing and storing dimensions corresponding to attributes of the data records, parsing the streaming data records to identify matches to at least one of the dimensions and based thereon, reducing the number of data records to create a targeted subset of the data, re-distributing the targeted subsets of the streaming data records among the distributed computational nodes in accordance with the dimensions stored on the nodes, updating a database storing measures of the dimensions in accordance with the targeted subsets of the streaming data records, and using the database to respond to a query based on measures associated with one or more of the dimensions.

Abstract: Representative embodiments of a distributed processing method of facilitating interactive analytics of streaming data records by receiving the data records at a plurality of distributed computational nodes, establishing and storing dimensions corresponding to attributes of the data records, parsing the streaming data records to identify matches to at least one of the dimensions and based thereon, reducing the number of data records to create a targeted subset of the data, re-distributing the targeted subsets of the streaming data records among the distributed computational nodes in accordance with the dimensions stored on the nodes, updating a database storing measures of the dimensions in accordance with the targeted subsets of the streaming data records, and using the database to respond to a query based on measures associated with one or more of the dimensions.

Abstract: Representative embodiments of a distributed processing method of facilitating interactive analytics of streaming data records by receiving the data records at a plurality of distributed computational nodes, establishing and storing dimensions corresponding to attributes of the data records, parsing the streaming data records to identify matches to at least one of the dimensions and based thereon, reducing the number of data records to create a targeted subset of the data, re-distributing the targeted subsets of the streaming data records among the distributed computational nodes in accordance with the dimensions stored on the nodes, updating a database storing measures of the dimensions in accordance with the targeted subsets of the streaming data records, and using the database to respond to a query based on measures associated with one or more of the dimensions.

Abstract: Representative embodiments of a distributed processing method of facilitating interactive analytics of streaming data records by receiving the data records at a plurality of distributed computational nodes, establishing and storing dimensions corresponding to attributes of the data records, parsing the streaming data records to identify matches to at least one of the dimensions and based thereon, reducing the number of data records to create a targeted subset of the data, re-distributing the targeted subsets of the streaming data records among the distributed computational nodes in accordance with the dimensions stored on the nodes, updating a database storing measures of the dimensions in accordance with the targeted subsets of the streaming data records, and using the database to respond to a query based on measures associated with one or more of the dimensions.