Abstract: Technology for using a nested probabilistic data structure to determine properties of a data set. An example method may involve: receiving a data item comprising a first and second item values; accessing a first probabilistic data structure comprising elements with references to a plurality of second probabilistic data structures; evaluating the first probabilistic data structure to identify a set of the second probabilistic data structures, wherein the evaluating comprises applying a set of hash functions to the first item value to generate hash values indicating the set of second probabilistic data structures corresponding to the first item value; evaluating one of the second probabilistic data structures in view of the second item value to identify a set of elements of the second probabilistic data structure corresponding to the second item value; and updating the set of elements of the second probabilistic data structure to represent the data item.

Abstract: Repeatable distributed pseudorandom number generation is disclosed. For example, a system has a plurality of pseudorandom number generators (“PRNGs”) including a first and second PRNGs and a randomization engine including a seed engine configured to control the plurality of PRNGs by executing to generate a plurality of seed values equal in quantity to the plurality of PRNGs, including first and second seed values. The first seed value is assigned to the first PRNG and the second seed value to the second PRNG. A first pseudorandom number (“PRN”) set is received from the first PRNG and a second PRN set from the second PRNG. A plurality of PRN sets from the plurality of PRNGs is combined into a combined number set.

Abstract: Repeatable distributed pseudorandom number generation is disclosed. For example, a system has a plurality of pseudorandom number generators (“PRNGs”) including a first and second PRNGs and a randomization engine including a seed engine configured to control the plurality of PRNGs by executing to generate a plurality of seed values equal in quantity to the plurality of PRNGs, including first and second seed values. The first seed value is assigned to the first PRNG and the second seed value to the second PRNG. A first pseudorandom number (“PRN”) set is received from the first PRNG and a second PRN set from the second PRNG. A plurality of PRN sets from the plurality of PRNGs is combined into a combined number set.

Abstract: Distributed function generation with shared structures is disclosed. A plurality of source code instructions that includes a data object definition associated with a data object type and a plurality of function definitions that accesses a data object of the data object type is received. A corresponding plurality of intermediate functions based on the plurality of function definitions is generated. First commands to generate a first set of native functions from corresponding intermediate functions are received. A first set of native functions in corresponding first native instruction sets associated with a first computing environment are generated. Second commands to generate a second set of native functions from corresponding intermediate functions are received. At least one native function in the second set of native functions is absent from the first set of native functions.

Abstract: Distributed function generation with shared structures is disclosed. A plurality of source code instructions that includes a data object definition associated with a data object type and a plurality of function definitions that accesses a data object of the data object type is received. A corresponding plurality of intermediate functions based on the plurality of function definitions is generated. First commands to generate a first set of native functions from corresponding intermediate functions are received. A first set of native functions in corresponding first native instruction sets associated with a first computing environment are generated. Second commands to generate a second set of native functions from corresponding intermediate functions are received. At least one native function in the second set of native functions is absent from the first set of native functions.

Abstract: Implementations provide for generic semantic configuration service for computer systems. A method of the disclosure includes installing, by a processing device, an abstract data structure representing an application, the abstract data structure comprising representation objects corresponding to high-level features of the application, installing a semantic model for the application, receiving an identification of at least one of the high-level features to be configured on at least one component executing the application, validating, using the semantic model, a configuration of the representation objects of the abstract data structure corresponding to the at least one of the high-level features, and generating low-level configuration code in a machine-readable format of the application from the representation objects corresponding to the at least one of the high-level features.