International Business Machines Corporation

Amazon Technologies, Inc.

Mengchu Cai

Guogen Zhang

Ippokratis Pandis

Yu Cheng Chen

Thomas Abel Beavin

Fen-Ling Lin

Li-Mey P Lee

Eric N Katayama

Anurag Windlass Gupta

Li Fu Chen

Ruiping Li

Naresh Chainani

Kwai Hing Man

Yonghua Ding

Jason Alexander Cu

Rong-Hoang Chang

Yun Yu Wang

Yao-Ching Stephen Chen

Martin Grund

Yao Ching Stephen Chen

Terence Patrick Purcell

Jae Hwang Lee

Gaurav Saxena

Jerry Mukai

Li Jun Xia

Allan Benjamin Lebovitz

Mingda Li

Shengxi Suo

Yoichi Tsuji

Mohammad Rezaur Rahman

Irene Ching-Hua Liu

Hsiuying Yen Cheng

Ying Zeng

Dengfeng Gao

Demai Ni

Wei Ey Li

Robert William Lyle

Nathan Lorenzo Binkert

James Alan Ruddy

Michail Petropoulos

Krishna Chaitanya Gudipati

Fabian Oliver Nagel

Andrew Scott Richardson

Chunbin Lin

Sanjay Wangoo

Yue Huang

Maor Kleider

Meng Tong

Huiyuan Wang

Qichen Zhang

Nikolaos Armenatzoglou

Dynamic selection of topologies for database data may be performed for performing queries. Different possible query plans for different assignments of topologies are determined for a query. One of the query plans is selected according to a cost-based model that assigns costs to different uses of the different topologies. The query is then performed using the selected query plan.

Dynamic selection of database data topologies for performing queries

A database management system receives a command defining a view of the database. The view definition is accepted without determining whether references to schema elements within the view definition are resolvable to existing elements of the database schema. A query of the view is received. In response to the query of the view, the database management system resolves references to schema elements in the view definition by determining whether the references correspond to data available for processing the query.

Late-binding database views

A primary query engine may receive a query and determine whether the query is eligible for performance at a secondary query engine. If eligible, the primary query engine may evaluate the availability of the first query engine to perform the query. The first query engine may determine whether to assign the query to the primary query engine or to the secondary query according to availability evaluation. For queries assigned to the secondary query engine, the primary query engine may send a request to the secondary query engine to being processing of the query.

Dynamically assigning queries to secondary query processing resources

Data processing engines implement format independent data processing for distributed data in local and remote data stores. Requests execute at least a portion of an access request are received from a data processing engine that does not support a data format for data stored in a data store. The data format for the data is identified and the data accessed according to the identified data format. The portion of the access request is applied to the accessed data in order to generate results. The generated results for the portion of the access request are then sent to the data processing engine.

Format independent processing for distributed data

A data store may implement dynamic query predicates for processing queries. Query predicate indexes may be maintained for individual storage locations in a data store. The query predicate indexes may indicate data values not stored in the storage location that satisfy a query predicate in the query predicate index. The query predicate index may be populated when queries are received at the data store which include new query predicates. When queries are received that include query predicates that correspond to query predicates in the query predicate index, the index values for each of the storage locations may be evaluated. Based, at least in part, on the evaluation, particular storage locations may be identified to read for servicing the queries.

Dynamic predicate indexing for data stores

Relocating data sharing operations for query processing may be implemented when generating plans to perform a query. A query operation that causes a node to share data obtained from a different set of nodes with other nodes in a same set of nodes may be identified. The identified query operation may be replaced with another operation that causes the different set of nodes to share the data directly with the other nodes in the same set of nodes.

Relocating data sharing operations for query processing

Data processing engines implement tiered data processing for distributed data in local and remote data stores. Requests to access distributed data including a data object in a remote data store are received at a data processing engine. A query plan is generated to service the access request. Different operations in the query plan are identified and assigned to one or more remote query processing engines that may access the remote data object. Requests to perform the different operations are sent to the one or more remote query processing engines. A final result is generated for the request based on the results received for the different operations from the remote query processing engine and results from operations performed with respect to locally stored data.

Tiered data processing for distributed data

Queries that involve multiple tables in a data warehouse system may be specified through structured query language ('SQL') statements that include union, order by, and limit clauses. When the tables are partitioned, a query of this type may be processed by forming a query plan in which intermediate results are formed by applying sort and limit operators at the partition level, combining the intermediate results from the partitions, and again applying sort and limit operations. Additional clauses, such as a unique clause, may also be pushed down to the partition level and reapplied at the level of the result set.

Distributed union all queries

Optimizing update instructions in hierarchically structured documents is provided. A pending update list including a first plurality of items is received. Each item of the first plurality of items describes an update to a hierarchically structured document. Each of one or more items of the first plurality of items is added to a subsumed update list having a second plurality of items. The subsumed update list is ordered based, at least in part, on a document order and on a target node of each of the second plurality of items. Subsume logic is applied to each of the one or more items based, at least in part, on the second plurality of items. Responsive to determining that subsume logic has been applied to each item of the first plurality of items, each of the second plurality of items of the subsumed update list is executed.

Optimizing update operations in hierarchically structured documents

System, method, and computer program product to modify a received query, the received query including a first predicate specifying a comparison operator and a second predicate specifying an IS NULL operator, by replacing the comparison operator of the first predicate with an extended comparison operator, and removing the IS NULL operator from the received query, the extended comparison operator configured to support searching of NULL values in a database management system (DBMS), wherein the extended comparison operator is configured to return rows having column data matching a value predefined to represent NULL values.

Boolean term conversion for null-tolerant disjunctive predicates

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San Jose, CA, United States of America

Mengchu Cai