Abstract: Methods and systems to detect and resolve failure in a distributed database system is described herein. A first node in the distributed database system can detect an interruption in communication with at least one other node in the distributed database system. This indicates a network failure. In response to detection of this failure, the first node starts a failure resolution protocol. This invokes coordinated broadcasts of respective lists of suspicious nodes among neighbor nodes. Each node compares its own list of suspicious nodes with its neighbors' lists of suspicious nodes to determine which nodes are still directly connected to each other. Each node determines the largest group of these directly connected nodes and whether or not it is in that group. If a node isn't in that group, it fails itself to resolve the network failure.

Abstract: Methods and systems to detect and resolve failure in a distributed database system is described herein. A first node in the distributed database system can detect an interruption in communication with at least one other node in the distributed database system. This indicates a network failure. In response to detection of this failure, the first node starts a failure resolution protocol. This invokes coordinated broadcasts of respective lists of suspicious nodes among neighbor nodes. Each node compares its own list of suspicious nodes with its neighbors' lists of suspicious nodes to determine which nodes are still directly connected to each other. Each node determines the largest group of these directly connected nodes and whether or not it is in that group. If a node isn't in that group, it fails itself to resolve the network failure.

Abstract: Methods and systems to detect and resolve failure in a distributed database system is described herein. A first node in the distributed database system can detect an interruption in communication with at least one other node in the distributed database system. This indicates a network failure. In response to detection of this failure, the first node starts a failure resolution protocol. This invokes coordinated broadcasts of respective lists of suspicious nodes among neighbor nodes. Each node compares its own list of suspicious nodes with its neighbors' lists of suspicious nodes to determine which nodes are still directly connected to each other. Each node determines the largest group of these directly connected nodes and whether or not it is in that group. If a node isn't in that group, it fails itself to resolve the network failure.

Abstract: Methods and systems to detect and resolve failure in a distributed database system is described herein. A first node in the distributed database system can detect an interruption in communication with at least one other node in the distributed database system. This indicates a network failure. In response to detection of this failure, the first node starts a failure resolution protocol. This invokes coordinated broadcasts of respective lists of suspicious nodes among neighbor nodes. Each node compares its own list of suspicious nodes with its neighbors' lists of suspicious nodes to determine which nodes are still directly connected to each other. Each node determines the largest group of these directly connected nodes and whether or not it is in that group. If a node isn't in that group, it fails itself to resolve the network failure.

Abstract: Methods and systems to detect and resolve failure in a distributed database system is described herein. A first node in the distributed database system can detect an interruption in communication with at least one other node in the distributed database system. This indicates a network failure. In response to detection of this failure, the first node starts a failure resolution protocol. This invokes coordinated broadcasts of respective lists of suspicious nodes among neighbor nodes. Each node compares its own list of suspicious nodes with its neighbors' lists of suspicious nodes to determine which nodes are still directly connected to each other. Each node determines the largest group of these directly connected nodes and whether or not it is in that group. If a node isn't in that group, it fails itself to resolve the network failure.

Abstract: In an example, a transitive local predicates may be inferred across multiple joins, wherein the multiple outer joins include multiple outer joins. A query connecting tables by the multiple joins is analyzed. A local predicate may then be identified from the analyzed query and may be determined to be either a where-clause local predicate or an on-clause local predicate. Accordingly, a transitive local predicate may be inferred across a selection of the tables based on the determination.

Abstract: A method for query processing includes receiving a query, and determining a query type by determining if the query is a structured query language (SQL) query including multiple distinct aggregates (MDAs), or a SQL query including cube, rollup or grouping sets operations. Based on the determination, the method includes processing the query to generate an output, and performing a predetermined number of maximum group by operations on the output based on the query type to generate a response to the query.

Abstract: In an example, a transitive local predicates may be inferred across multiple joins, wherein the multiple outer joins include multiple outer joins. A query connecting tables by the multiple joins is analyzed. A local predicate may then be identified from the analyzed query and may be determined to be either a where-clause local predicate or an on-clause local predicate. Accordingly, a transitive local predicate may be inferred across a selection of the tables based on the determination.

Abstract: In an example, data from a source location is merged into a target location, in which both the source location and the target location contain tuples of data. A matched tuple in the target table that matches a The deleting of the tuple in the target table includes executing a semi inner join operation between the source table and the target table to identify the matched tuple. Each tuple contained in the source table may be inserted into the target table as a new tuple.

Abstract: According to an example, a method for executing a query having multiple set operators includes adding an additional column that associates an identifier of the result table with data contained in the input result table in each input result table of an input query. The method also includes performing a Union All operation on the data contained in the input result tables to generate an intermediate result set, performing a Group By operation on the data contained in the intermediate result set to generate a grouped result set, wherein the Group By operation operates on various groups of rows and returns one row per each of the various groups, adding aggregation columns in the grouped result set containing counts of a number of times that each tuple exists in each of the input result tables, and applying a predicate on the grouped result set to execute the query.

Abstract: A method for query processing includes receiving a query, and determining a query type by determining if the query is a structured query language (SQL) query including multiple distinct aggregates (MDAs), or a SQL query including cube, rollup or grouping sets operations. Based on the determination, the method includes processing the query to generate an output, and performing a predetermined number of maximum group by operations on the output based on the query type to generate a response to the query.

Abstract: A computer readable medium storing a database query language statement tuning base in a tuning base memory location is disclosed. The tuning base includes tuning information for one or more query language statements. The tuning information for each statement includes one or more tuning actions for the statement, and a signature to allow an optimizer to identify the one or more tuning actions for the statement.

Abstract: Methods, systems and program products for database storage. In one implementation, data of a projection of a database is stored at least partly in grouped ROS format and partly in column format based on patterns of updating the projection data. The projection data is updated so that the updated projection is stored partly in grouped ROS format and partly in column format.

Abstract: A method of analyzing an expression to infer output data properties, comprises storing, in a storage device, a root node “e” in R, in which “e” is an expression and R is a root node of an abstract syntax tree (AST) of the expression “e”, storing a number of child nodes of R into C1, . . . Cn, with a processor coupled to the storage device, calling analyze(Ci), storing output properties of expression C into Pi I, and determining if any of the child nodes Ci in {C1 . . . Cn} has not been analyzed, in which if the last child node has been analyzed, with the processor, calling compute-output-properties(R, P1, . . . Pn), storing, in the storage device, the result in Poutput, and returning Poutput as the output properties of the expression “e.

Abstract: Methods, systems and program products for database storage. In one implementation, data of a projection of a database is stored at least partly in grouped ROS format and partly in column format based on patterns of updating the projection data. The projection data is updated so that the updated projection is stored partly in grouped ROS format and partly in column format.

Abstract: A computer readable medium storing a database query language statement tuning base in a tuning base memory location is disclosed. The tuning base includes tuning information for one or more query language statements. The tuning information for each statement includes one or more tuning actions for the statement, and a signature to allow an optimizer to identify the one or more tuning actions for the statement.