Abstract: A system includes a database client, and a distributed database comprising database nodes. The distributed database may receive a database query from the client, determine that the query comprises a range of hash values of a table partition stored by a node of the distributed database, and determine that the range of hash values is not stored by other nodes of the distributed database. Responsive to determining that the range of hash values of the query is stored by the node and not by the other nodes, the database may generate an optimized distributed execution plan that includes the node that stores the range of hash values and excludes the nodes that do not include the range of hash values.

Abstract: In one example in accordance with the present disclosure, a method for dynamic lexer object construction includes receiving a grammar rule including a variable and a Boolean function to be performed on a character sequence using the variable. The method includes receiving the character sequence and a value corresponding to the character sequence and constructing a lexer object that includes the value as the variable used by the Boolean function. The method includes parsing the character sequence using the lexer object.

Abstract: According to an example, dynamic function invocation may include ascertaining a query for a database management system (DBMS). Dynamic function invocation may further include implementing a dynamic function as a meta user defined function to invoke a plurality of different coded functions including a coded function that is to be invoked by the query to perform an operation related to the DBMS, and executing the dynamic function by the query to load and invoke the coded function to perform the operation related to the DBMS.

Abstract: Methods, devices, and techniques for base user defined functions in a database management system are discussed herein. For example, in one aspect, a query request is received from a computer device. The query request may include a query operator representing a specialized user defined function (SUDF). The SUDF may then be executed. Executing the SUDF may include executing a base operation of a base user defined function (BUDF). The base operation may interact with an application programming interface (API) of the query engine to obtain a tuple stored in the database. Executing the SUDF may further include executing a specialized operation that processes the tuple according to an analytics function. The specialized operation may generate a result. Then, a query result may be returned to the computer device. The query result can include the result.

Abstract: Examples disclosed herein relate to accessing electronic databases. Some examples disclosed herein may include partitioning a computation task into subtasks. A processing node of a computation engine may generate a database query for retrieving an electronic data segment associated with at least one of the subtasks from a database. The database query may include pre-processing instructions for a database management system (DBMS) associated with the database to pre-process the electronic data segment before providing the electronic data segment to the processing node. The pre-processing instructions may include at least one of: filtering, projection, join, aggregation, count, and user-defined instructions. The generated query may be provided to the DBMS.

Abstract: Page feed for efficient dataflow between distributed query engines is disclosed. An example method comprises storing a query result relation by a producer query as pages in a query engine buffer pool, and then sending these pages to a distributed caching platform (DCP) as the data source of another query. The example method also includes reading the result relation by a consumer query from the page stored in the DCP.

Abstract: A method for combining data values through associative operations. The method includes, with a processor, arranging any number of data values into a plurality of columns according to natural parallelism of the associative operations and reading each column to a register of an individual processor. The processors are directed to combine the data values in the columns in parallel using a first associative operation. The results of the first associative operation for each column are stored in a register of each processor.

Abstract: Multiple tuples are combined (202) by a processing node into a message that has a key portion including a value of a key, and a payload including a relation containing content of the combined plurality of tuple. The message is sent (204) to a receiving node.

Abstract: A technique includes in a relational database query engine, receiving a query associated with a relational data structure. The received query includes a database graph query. The technique includes using the relational database query engine to integrate a result acquired from the graph database engine into a result provided by the relational database query engine to the received query.

Abstract: A method is disclosed. In one embodiment, the method may include determining a state of an electronic device. In one embodiment, the method may include determining whether the determined state of the electronic device corresponds to a key input working state. In one embodiment, the method may include, in response to the determined state of the electronic device corresponding to the key input working state, operating an input acquisition unit of the electronic device in a key input working mode and displaying a key section on an input section of the electronic device.

Abstract: An example technique for defining a user function can include receiving a user defined function that gathers data from a data source external to a database and returns the data to the database. The example technique for defining the user function can also include creating an instance of a programming class that extends from an application programming interface (API). The instance of the programming class including the user defined function. The example technique for defining the user function can also include executing the user defined function provided by the instance of the programming class.

Abstract: Systems and methods of block computation are disclosed. An example of a method includes buffering a plurality of tuples in a build phase. The method also includes, after buffering a last of the plurality of tuples, applying a computation to all of the buffered tuples in a compute phase. The method also includes returning results of the computation one tuple at a time to a query processing pipeline in a stream-out phase.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage a cache. An example method includes in response to receiving a request to retrieve received data, retrieving first data from a cache, the first data received during a first time period, and retrieving, second data from a storage device, the second data received during a second time period prior to the first time period; and providing the first data and second data as combined data the, combined data being combined based on the first time period and the second period.

Abstract: Example embodiments relate to parallelizing structured query language (SQL) on distributed file systems. In example embodiments, a subquery of a distributed file system is received from a query engine, where the subquery is one of multiple subqueries that are scheduled to execute on a cluster of server nodes. At this stage, a user defined function that comprises local, role-based functionality is executed, where the partitioned magic table triggers parallel execution of the user defined function. The execution of the UDF determines a sequence number based on a quantity of the cluster of server nodes and retrieve nonconsecutive chunks from a file of the distributed file system, where each of the nonconsecutive chunks is offset by the sequence number.

Abstract: Customizing functions performed by data flow operators when processing data streams. An open-executor(s) is provided as part of the data stream analytics platform, wherein such open-executor allows for both of: 1) customizing user plug-ins for the operators, to accommodate changes in user requirements; and 2) predefining templates that are based on specific meta-properties of various operators and that are common therebetween.

Abstract: Example embodiments relate to parallelizing structured query language (SQL) user defined transformation functions. In example embodiments, a subquery of a query is received from a query engine, where each of the subqueries is associated with a distinct magic number in a magic table. A user defined transformation function that includes local, role-based functionality may then be executed, where the magic number triggers parallel execution of the user defined transformation function. At this stage, the results of the user defined transformation function are sent to the query engine, where the query engine unions the results with other results that are obtained from the other database nodes.

Abstract: A data stream is stored in a database. An event pipe stores a sliding window of events from the data stream. A query is responded to by utilizing the sliding window of events stored in the event pipe.

Abstract: A system includes a distributed file system to control storage of data across storage nodes and a database query engine to receive a database query for access of data, the database query engine to process the database query using an index, and using a buffer pool to cache data retrieved in response to the database query and to store updated data. An abstraction layer is provided between the database query engine and the distributed file system, the abstraction layer to read and write data of the distributed file system in response to the database query.

Abstract: In one implementation, a system for processing a data stream can comprise a station engine, an execution engine, and a synchronize engine. A station engine can provide a stream operator to receive application logic, punctuate the data stream, and determine a number of input channels for parallel processing. The execution engine can perform a behavior of the application logic during a process operation. The synchronize engine can hold data of the data stream associated with a window until each input channel has reached a data boundary based on a boundary parameter.

Abstract: A technique for analyzing a parallel data stream using a sliding FP tree can include create a sliding FP tree using input tuples belonging to a parallel sliding window boundary and analyze patterns of the parallel data stream in the parallel sliding window boundary.