Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A system and method for a text search of a database, including converting a text search expression to a query plan and implementing the text search as the query plan on the database. The implementing of the text search includes a one-pass indexing as a single scan of an inverse index table associated with the database.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: In one example in accordance with the present disclosure, a method may include separating a list of keywords into a set of word tokens and a set of wildcard tokens. The method may also include removing each wildcard token in the set of wildcard tokens that is inferred by at least one word token in the set of word tokens and removing each wildcard token in the set of wildcard tokens that is inferred by at least one other wildcard token in the set of wildcard tokens. The method may also include executing a search query comprising a new list of keywords that includes each wildcard token not removed from the set of wildcard tokens.

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: Query integration across databases and file systems is disclosed. An example method may include streaming data managed by a first database file system for a query. The method may also include streaming data managed by a second database file system for the query. The method may also include joining the streaming data managed by the first database file system with the streaming data managed by the second database file system.

Abstract: A system and method for a text search of a database. A text search expression is converted to a query plan having multiple search tokens. A one-pass indexing of an invested word index filters the inverted word index based on a search condition and identifies the applicable documents having the multiple search tokens.

Abstract: Examples disclosed herein relate to a database comparison operation to identify an object. For example, a processor may enroll a set of object templates in a storage based on objects within input content and enroll a target object template in the storage based on a target object in target content. The processor may identify an object within the input content associated with the target object based on a database comparison operation of the stored set of object templates to the stored target object template. The processor may output object recognition information related to the identified 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: 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: 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 system and method for a text search of a database, including converting a text search expression to a query plan and implementing the text search as the query plan on the database. The implementing of the text search includes a one-pass indexing as a single scan of an inverse index table associated with the database.

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: In one example in accordance with the present disclosure, a system comprises a computing node. The computing node comprises: a memory, and a processor to: execute a database in the memory, and invoke, with the database, singular value decomposition (SVD) on a data set. To invoke SVD, the processor may sparsify, with the database, the data set to produce a sparse data set, iteratively decompose, with the database, the data set to produce a set of eigenvalues, solve, with the database a linear system to produce a set of eigenvectors, and multiply, with the database, the eigenvectors with the data set to produce a data set of reduced dimension.

Abstract: A technique for visualizing topics includes depicting topic bubbles including pixels. In one example, selected topics are identified from records based on scoring candidate terms in the records according to a user-specified metric and a metric selected from among frequencies of occurrence of records pertaining to the respective candidate terms, and negativity of sentiment expressed with respect to the candidate terms in the records. A visualization is generated including bubbles representing topics, the bubbles including pixels representing corresponding records. A bubble has a shape dependent upon a number of records and a time interval represented by the bubble. Visual indicators are assigned to the pixels in a given bubble according to values of an attribute expressed in the corresponding records for the topic represented by the given bubble, resulting in the analysis of the selected topics being less time consuming and labor intensive.

Abstract: Examples relate to identifying shortest paths. In one example, a computing device may: access an edge table that specifies, for each edge of a graph, an edge source, an edge destination, and an edge distance value; access a current path table that specifies paths between nodes of the graph and, for each path, a source node, a destination node, a distance, and a node path; identify each path included in the current path table as a shortest known path; and for each path having a destination node that matches an edge source node, add a new path to the current path table, the new path specifying: the source node as a new source; the edge destination as a new destination; a sum of the edge value and the path distance as a new distance; and the edge destination appended to the node path as a new node path.