Abstract: Methods, systems, and computer-readable media are directed towards receiving, at an untrusted component, a query for a data store. The query includes a plurality of data operations. The data store is accessible by the untrusted component. A first proper subset of data operations is determined from the plurality of data operations that do not access sensitive data within the data store. A second proper subset of data operations is determined from the plurality of data operations that access sensitive data within the data store. The first proper subset of data operations is executed, at the untrusted component, to create first results. The second proper subset of data operations is sent to a trusted component for execution. Second results based on the sending the second proper subset of data operations are received from the trusted component. Results to the query are returned based on the first results and the second results.

Abstract: Deferred verification of the integrity of data operations over a set of data that is hosted at an untrusted module (UM) is controlled. The controlling includes generating a request for a data operation on the set of data. The request includes an authentication portion. The request is sent to the UM. A response to the request is received from the UM. The response includes cryptographic verification information attesting the integrity of the data operation with respect to prior data operations on the set of data. The response includes results from deferred verification at a trusted module (TM).

Abstract: Calculation of aggregated values in a history database table can be optimized using an approach in which an ordered history table is accessed. The ordered history table can include a sequential listing of commit identifiers associated with updates, insertions, and/or deletions to values in the database table. The ordered history table can be traversed in a single pass to calculate an aggregation function using an optimized algorithm. The optimized algorithm can enable calculation of an aggregated metric of the values based on a selected method for tracking invalidated values to their corresponding commit identifiers. The calculated metric is generated for a current version of the database table; and promoted.

Abstract: Data that includes a query of a temporal database is received from a remote application server. The query specifies at least one fact and a system time and an application time for the at least one fact. Thereafter, a bitemporal timeline index is accessed to identify data responsive to the query. The bitemporal timeline index includes a system time dimension and an application time dimension. Next, the identified data can be retrieved and provided to the remote application server. Related apparatus, systems, techniques and articles are also described.

Abstract: Embodiments described herein generally relate to creating a timeline index for executing queries on temporal data. A computer-implemented method is described. The method includes creating a first list of tuples that are invalidated in a temporal table and sorting the first list of the invalidated tuples. The method can further include creating a second list of tuples that are activated in the temporal table and combining the first list and the second list into a third list that includes the activated tuples and the invalidated tuples, wherein the third list contains ROW_IDs of both the activated tuples and the invalidated tuples.

Abstract: The technique described herein provides a way to summarize data and can also minimize unnecessary accesses to a data store. In one embodiment, the technique creates and stores an adaptive range filter that contains a compact summary of the contents of an index for a data store in the form of a trie data structure. Before accessing the index of the data store in response to a query, the technique checks the filter to determine whether the data store does not contain any keys for a specific range of data. If the adaptive range filter indicates that the index contains no keys satisfying the query predicate, the index of the data store and the data itself is not accessed. The adaptive range filter of the technique supports both range predicates and equality predicates. It is adaptive to changes in data and queries by learning the query and data distribution.

Abstract: Calculation of aggregated values in a history database table can be optimized using an approach in which an ordered history table is accessed. The ordered history table can include a sequential listing of commit identifiers associated with updates, insertions, and/or deletions to values in the database table. The ordered history table can be traversed in a single pass to calculate an aggregation function using an optimized algorithm. The optimized algorithm can enable calculation of an aggregated metric of the values based on a selected method for tracking invalidated values to their corresponding commit identifiers. The calculated metric is generated for a current version of the database table; and promoted.

Abstract: A number of transmissions of secure data communicated between a secure trusted device and an unsecure untrusted device in a DBMS is controlled. The data is communicated for database transaction processing in the secure trusted device. The number of transmissions may be controlled by receiving, from the untrusted device, an encrypted key value of a key and a representation of an index of a B-tree structure, decrypting, at the trusted device, the key and one or more encrypted index values, and initiating a transmission, a pointer value that identifies a lookup position in the index for the key. The index comprises secure, encrypted index values. Other optimizations for secure processing are also described, including controlling available computation resources on a secure trusted device in a DBMS and controlling transmissions of secure data that is communicated between a secure trusted device and an unsecure untrusted device in a DBMS.

Abstract: An event processing system is configured to process a stream of events operating on a database system. The event processing system comprises an event load balancing unit, a plurality of event computing nodes, and a plurality of event state stores, wherein the event load balancing unit is configured to route the stream of events to the plurality of event computing nodes, wherein the plurality of event state stores are configured to store states of the plurality of event computing nodes for maintaining a state of the event processing, and wherein the plurality of event computing nodes are configured to process the events, to change their states, and to update the plurality of event state stores based on their changed states.

Abstract: A distributed data store includes a storage layer comprised of a plurality of storage nodes, each storage node configured to receive and queue table operations, and an aggregation layer comprised of aggregator nodes configured to route table operations to storage nodes in the storage layer and merge results of table operations returned by storage nodes in the storage layer. Each storage node includes in part scan threads dedicated to continuous and full scanning of segments of a table that are respectively associated with processor cores upon which such scan threads are running.

Abstract: Partitioning of temporal databases can implement distributed storage of temporal data via time-based or space-based techniques to improve performance of operators on the database. A variety of operators can be supported for the partitioned tables, including temporal aggregation, time travel, and temporal join. The use of checkpoints can greatly increase performance in a variety of scenarios. The described partitioning techniques can be applied in a parallel execution context to great benefit. The partitioning can also reduce local memory footprint, facilitating in-memory database processing.

Abstract: Data that includes a query of a temporal database is received from a remote application server. The query specifies at least one fact and a system time and an application time for the at least one fact. Thereafter, a bitemporal timeline index is accessed to identify data responsive to the query. The bitemporal timeline index includes a system time dimension and an application time dimension. Next, the identified data can be retrieved and provided to the remote application server. Related apparatus, systems, techniques and articles are also described.

Abstract: A distributed data store includes a storage layer comprised of a plurality of storage nodes, each storage node configured to receive and queue table operations, and an aggregation layer comprised of aggregator nodes configured to route table operations to storage nodes in the storage layer and merge results of table operations returned by storage nodes in the storage layer. Each storage node includes in part scan threads dedicated to continuous and full scanning of segments of a table that are respectively associated with processor cores upon which such scan threads are running.

Abstract: A method for storing and retrieving data in a storage node of a data store and storage node of a data store, storing in main-memory at least one segment of a relational table. The storage node includes at least one computational core running at least one scan thread each dedicated to the scanning of one of the at least one segment. At least one scan thread uniquely, continuously and exhaustively scans the dedicated segment of the relational table. The storage node receives and processes batches of query and update operations for the at least one segment of the relational table. The query and update operations of a batch are re-indexed at beginning of each scan by the scan thread. Then, the indexed query and update operations of a batch are independently joined to data records of said segment that match with predicates of the indexed query and update operations.

Abstract: A system and method of performing snapshot isolation in distributed databases. Each node stores local snapshot information that enforces snapshot isolation for that node. The method includes partially processing a distributed transaction by a first node, receiving a global commit identifier from a coordinator, and continuing to process the distributed transaction, by the first node and a second node, in accordance with the global commit identifier.

Abstract: The subject disclosure is directed towards using trusted hardware to achieve secure data processing over a network. For a given set of data store operations, some operations are directed to sensitive data (e.g., encrypted data fields). These operations are compiled into a set of expressions invoking trusted hardware code configured to evaluate these expressions using corresponding data centric primitive programs. Because the trusted hardware is configured to maintain key data for encrypting/decrypting the sensitive data, the sensitive data is not accessible by an untrusted component while the sensitive data is decrypted.

Abstract: Calculation of aggregated values in a history database table can be optimized using an approach in which an ordered history table is accessed. The ordered history table can include a sequential listing of commit identifiers associated with updates, insertions, and/or deletions to values in the database table. The ordered history table can be traversed in a single pass to calculate an aggregation function using an optimized algorithm. The optimized algorithm can enable calculation of an aggregated metric of the values based on a selected method for tracking invalidated values to their corresponding commit identifiers. The calculated metric is generated for a current version of the database table; and promoted.

Abstract: The technique described herein provides a way to summarize data and can also minimize unnecessary accesses to a data store. In one embodiment, the technique creates and stores an adaptive range filter that contains a compact summary of the contents of an index for a data store in the form of a trie data structure. Before accessing the index of the data store in response to a query, the technique checks the filter to determine whether the data store does not contain any keys for a specific range of data. If the adaptive range filter indicates that the index contains no keys satisfying the query predicate, the index of the data store and the data itself is not accessed. The adaptive range filter of the technique supports both range predicates and equality predicates. It is adaptive to changes in data and queries by learning the query and data distribution.

Abstract: Embodiments described herein generally relate to creating a timeline index for executing queries on temporal data. A computer-implemented method is described. The method includes creating a first list of tuples that are invalidated in a temporal table and sorting the first list of the invalidated tuples. The method can further include creating a second list of tuples that are activated in the temporal table and combining the first list and the second list into a third list that includes the activated tuples and the invalidated tuples, wherein the third list contains ROW_IDs of both the activated tuples and the invalidated tuples.

Abstract: Calculation of aggregated values in a history database table can be optimized using an approach in which an ordered history table is accessed. The ordered history table can include a sequential listing of commit identifiers associated with updates, insertions, and/or deletions to values in the database table. The ordered history table can be traversed in a single pass to calculate an aggregation function using an optimized algorithm. The optimized algorithm can enable calculation of an aggregated metric of the values based on a selected method for tracking invalidated values to their corresponding commit identifiers. The calculated metric is generated for a current version of the database table; and promoted.