Oracle International Corporation

Krishna Kantikiran Pasupuleti

Nipun Agarwal

Boris Klots

Mohamed Ziauddin

Sunil Parrokkaren Chakkappen

Hong Su

Anantha Kiran Kandukuri

Vijayakrishnan Nagarajan

In an embodiment, a computer-implemented method includes receiving a query from a client and determining a query plan for the query. The query plan comprises one or more query operators for executing at least a portion of the query on a database. The method also includes sending the one or more query operators to one or more computing nodes for the one or more computing nodes to execute the one or more query operators on one or more data fragments of the database. In this example, each computing node of the one or more computing nodes hosts a respective data fragment of the one or more data fragments. Further, the method includes detecting an error in executing a first query operator by a first computing node on a first data fragment, and sending, in response to detecting the error, the first query operator to a replacement computing node for executing on the first data fragment hosted by the spare computing node.

Fault tolerance in scale-out distributed query processing appliance

Disclosed is an approach for applying fine-grained hints to obtain optimal control over error handling during query compilation. Using fine-grained hints causes less disruption to the query plans generated by the optimizer as they constrain specific features while retaining scope for the rest of the optimizer functionality. This approach provides a query plan of better quality that avoid errors, particularly in comparison to coarse-grained approaches that restrict processing to earlier database versions or which disables cost-based processing.

Method and system to implement error handling with fine-grained SQL hints

To increase the applicability of group-by placement (GBP) transformations, according to embodiments described herein, database objects referred to in the aggregate function expression of a query are analyzed to determine whether any of these database objects are referred to exclusively within a condition check portion of the aggregate function expression. Such database objects may be dealt with differently (i.e., with respect to inclusion in GBP views) than the rest of the database objects referred to in aggregate function expressions of the query. This distinction between (a) aggregation-condition database objects exclusively found in condition check portions of an aggregate function expression and (b) aggregation-source database objects within an aggregate function expression allows application of GBP transformation to a wider variety of queries and also increases the possible GBP states that can be costed for GBP transformation-eligible queries.

Early grouping optimization for SQL statements with conditional expressions

Herein are acceleration techniques for resuming offloaded execution by replacing a failed computer with a hot spare computer. In an embodiment, a distributed system configures a DBMS, a set of participating computers, and a set of spare computers. The DBMS receives a query of a database. From the query, an offload query plan is generated for distributed execution. The DBMS sends the offload query plan and a respective portion of the database to each participating computer. The distributed system detects that a participating computer failed after the offload query plan was sent. Responsively, the DBMS sends the same offload query plan and same respective portion of the database of the failed computer to a replacement computer from the spare computers. Despite the computer failure, the DBMS receives results of successful distributed execution of the offload query plan that include a result from the replacement computer.

High availability and automated recovery in scale-out distributed database system

According to embodiments, a database server instance may generate group-by placement (GBP) states that define potential GBP transformations for a query in which some aggregate tables are included in the GBP view and other aggregate tables are not. Such states are referred to herein as relaxed-C states. To rewrite a query based on a selected relaxed-C GBP state such that the rewritten query is semantically equivalent to the original query, any arithmetic expression within an aggregate expression of the query is normalized into terms, and the query rewrite is implemented on a term-specific basis. Specifically, aggregate function-specific query modification rules are applied to each term of the normalized arithmetic expression. The applied query modification rules dictate what is included in the GBP view and what is included in the outer portion of the rewritten query to ensure that the rewritten query is semantically equivalent to the original query.

Growing community of inventors

Belmont, CA, United States of America

Krishna Kantikiran Pasupuleti