Abstract: Scalable implementations of exact distinct counts and multiple exact distinct counts in distributed query processing systems are implemented via systems and devices. Distinct counts and multiple exact distinct counts for identifiers/values are performed based on keys. For distinct counts, datasets including data fields are sorted by values of fields and divided into balanced partitions in distributed servers. Subsets of fields with the same value are partitioned together. Key presence is determined for subsets on each partition, and the number of instances for the key are aggregated for exact distinct counts of values. For multiple distinct counts, fields of a dataset are combined by un-pivoting field columns. Compound keys are generated for combined fields from field identifiers of the combined fields and values of another field. Totals of unique values of the combined fields are determined for values in the counted field based on the compound keys.

Abstract: Systems and devices implement scalable implementations of multi-dimensional aggregations with input blending in distributed query processing systems. Multi-dimensional aggregations for identifiers/values designated fields in datasets are performed based on keys. Datasets are sorted by identifier/value and divided into first partitions. Each row of data with a specific sorted-by-identifier/value is only present in one of the first partitions. Keys are generated from each combination of two or more dataset fields, and a blended table of data is generated over the partitions based on each different key combination. Designated data field characteristics are determined for the blended table based on the different key combinations. The characteristics are divided into second partitions based on the keys, where each key is present in only one of the second partitions. A final designated data field characteristic is determined for each row of data in each of the second partitions as the multi-dimensional aggregation.

Abstract: Systems and devices implement scalable implementations of multi-dimensional aggregations with input blending in distributed query processing systems. Multi-dimensional aggregations for identifiers/values designated fields in datasets are performed based on keys. Datasets are sorted by identifier/value and divided into first partitions. Each row of data with a specific sorted-by-identifier/value is only present in one of the first partitions. Keys are generated from each combination of two or more dataset fields, and a blended table of data is generated over the partitions based on each different key combination. Designated data field characteristics are determined for the blended table based on the different key combinations. The characteristics are divided into second partitions based on the keys, where each key is present in only one of the second partitions. A final designated data field characteristic is determined for each row of data in each of the second partitions as the multi-dimensional aggregation.

Abstract: Scalable implementations of exact distinct counts and multiple exact distinct counts in distributed query processing systems are implemented via systems and devices. Distinct counts and multiple exact distinct counts for identifiers/values are performed based on keys. For distinct counts, datasets including data fields are sorted by values of fields and divided into balanced partitions in distributed servers. Subsets of fields with the same value are partitioned together. Key presence is determined for subsets on each partition, and the number of instances for the key are aggregated for exact distinct counts of values. For multiple distinct counts, fields of a dataset are combined by un-pivoting field columns. Compound keys are generated for combined fields from field identifiers of the combined fields and values of another field. Totals of unique values of the combined fields are determined for values in the counted field based on the compound keys.

Abstract: Scalable implementations of exact distinct counts and multiple exact distinct counts in distributed query processing systems are implemented via systems and devices. Distinct counts and multiple exact distinct counts for identifiers/values are performed based on keys. For distinct counts, datasets including data fields are sorted by values of fields and divided into balanced partitions in distributed servers. Subsets of fields with the same value are partitioned together. Key presence is determined for subsets on each partition, and the number of instances for the key are aggregated for exact distinct counts of values. For multiple distinct counts, fields of a dataset are combined by un-pivoting field columns. Compound keys are generated for combined fields from field identifiers of the combined fields and values of another field. Totals of unique values of the combined fields are determined for values in the counted field based on the compound keys.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to receive at least one database query to be executed. At least one computation graph corresponding to the at least one database query is generated. The computation graph is transformed to an optimized computation graph. The respective portions of the optimized computation graph are distributed to a plurality of distributed computing systems for execution. A result for the at least one database query is provided.

Abstract: Various embodiments can include systems, methods, and non-transitory computer readable media configured to receive at least one operation to be performed using (i) first data that is managed by a first computing system and (ii) second data that is managed by a second computing system, the operation being received through an interface provided by the computing system, and wherein the operation is based at least in part on a Structured Query Language (SQL). At least one optimization can be performed based at least in part on the operation. The operation can be executed using at least the first data and the second data. A result generated can be provided upon executing the operation through the interface provided by the computing system. The computing system, the first computing system, and the second computing system are each able to concurrently process, access, and create at least a portion of the generated result.

Abstract: Various embodiments can include systems, methods, and non-transitory computer readable media configured to receive at least one operation to be performed using (i) first data that is managed by a first computing system and (ii) second data that is managed by a second computing system, the operation being received through an interface provided by the computing system, and wherein the operation is based at least in part on a Structured Query Language (SQL). At least one optimization can be performed based at least in part on the operation. The operation can be executed using at least the first data and the second data. A result generated can be provided upon executing the operation through the interface provided by the computing system. The computing system, the first computing system, and the second computing system are each able to concurrently process, access, and create at least a portion of the generated result.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to receive at least one database query to be executed. At least one computation graph corresponding to the at least one database query is generated. The computation graph is transformed to an optimized computation graph. The respective portions of the optimized computation graph are distributed to a plurality of distributed computing systems for execution. A result for the at least one database query is provided.

Abstract: A method for mixed-mode execution in object-oriented programs is disclosed whereby certain portions of source code can be executed by a higher-level mode of execution having access to the program at its highest level of abstraction, while other portions can be executed by a lower-level mode of execution. The invention described can be applied to any object-oriented environment where the higher-level mode of execution has components that are executed by the lower-level mode of execution and where new objects can be added to a running program at the lower-level mode of execution. In a presently preferred embodiment of the present invention, a source code interpreter operates directly on portions of Java source code where detailed information about the program is required (such as debugging information, profiling information or coverage information) while a virtual machine executes compiled byte code at all other times.