Oracle International Corporation

Amazon Technologies, Inc.

Hakan Jakobsson

Cetin Ozbutun

Jeffrey Ira Cohen

Michael Charles Depledge

Mark Kremer

Quoc Tai Tran

Alexander C Ho

Julian Hyde

Sanjay Kaluskar

Dmitry Mikhailovich Potapov

Namit Jain

Mohamed Ziauddin

Ari Wolfe Mozes

Graham Wood

Umesh Panchaksharaiah

Poojan Kumar

Zelaine Fong

Yiu Woon Lau

Benoit Dageville

Stefano Stefani

Anurag Windlass Gupta

Juan R Loaiza

Tirthankar Lahiri

Andrew Witkowski

William H Waddington

Wei Ey Li

Mohamed Zait

Sumanta Kumar Chatterjee

Rafi Ahmed

Margaret Susairaj

William McKenna

Neil Thombre

Atif Chaudhry

Fei Ge

Varun Malhotra

Mikhail Tyulenev

A query optimizer may receive a query block that includes a hint. The hint may include a unique object identifier to specify an object to which the hint applies. In some instances, the object may correspond to a nested query block. A query optimization plan may be determined based, at least in part, on the hint. The query execution plan may then be executed to query a data store.

Query hint specification

A data warehousing system maintains large tables comprising a significant quantity of historical data. In general, the addition of small quantities of additional data is not likely to significantly alter the accuracy of statistics relied upon by a query optimizer. However, certain statistics may have an influence on optimizer behavior, even though the size of the additional data is small. Certain statistics relied on by the optimizer may be incrementally updated based at least in part on characteristics of the additional data without requiring a scan of the preexisting data.

Incremental statistics update

Processing resources at a storage system for a database server are utilized to perform aspects of a join operation that would conventionally be performed by the database server. When requesting a range of data units from a storage system, the database server includes join metadata describing aspects of the join operation for which the data is being requested. The join metadata may be, for instance, a bloom filter. The storage system reads the requested data from disk as normal. However, prior to sending the requested data back to the storage system, the storage system analyzes the raw data based on the join metadata, removing a certain amount of data that is guaranteed to be irrelevant to the join operation. The storage system then returns filtered data to the database server. The database system thereby avoids the unnecessary transfer of certain data between the storage system and the database server.

Hash join using collaborative parallel filtering in intelligent storage with offloaded bloom filters

A method, system, and computer program product for cost based analysis for data access in a database management system. In one approach, the method, system, and computer program product identifies data to access. A first cost for direct I/O storage access and a second cost for cache access are then determined for accessing the data. A comparison between the first cost and the second cost is then performed. Finally, a first portion of identified data is accessed based at least in part upon the comparison.

Cost based analysis of direct I/O access

A method and mechanism for performing improved frequent itemset operations is provided. A set of item groups are divided into a plurality of subsets. Each item group is composed of a set of data items. Possible combinations of data items that may frequently appear together in the same item group are referred to as candidate combinations. Candidate combinations comprising a first set of data items are identified, and thereafter the occurrence of each candidate combination in any item group in each subset is counted by comparing item bitmaps, associated with items in the candidate combination, in each subset in turn. The comparison of item bitmaps is performed in volatile memory. A total frequent itemset count that describes the frequency of candidate combinations in items groups across all subsets is obtained. Thereafter, the total frequent itemset count for candidate combinations having a larger number of data items may be determined.

Frequent itemset counting using subsets of bitmaps

The methods and systems for database statement execution plan optimization exploit bind variable data available on the network to build and optimize an execution plan for the statement. A system for database statement execution plan optimization comprises a bind variable analyzer, a frame allocator, and an optimizer, in addition to a parser for parsing and analyzing the statement, a type checker for type checking the statement, and a tree builder for building an expression tree.

Methods and systems for database statement execution plan optimization

Described herein are approaches to implementing dynamic sampling in a way that lessens or eliminates the additional overhead incurred to perform dynamic sampling. Also described are techniques for determining characteristics about predicates not previously determined by conventional techniques for dynamic sampling. Dynamic sampling is used by a query optimizer to dynamically estimate predicate selectivities and statistics. When a database statement is received by a database server, an initial analysis of the database statement is made to determine the efficacy of dynamic sampling, that is, to determine whether optimization of the query would benefit from dynamic sampling and whether performance is not excessively impacted by the dynamic sampling process. If this analysis determines dynamic sampling should be used, then dynamic sampling is undertaken.

Optimizer dynamic sampling

Statements having non-commutative connection conditions are optimized by replacing the non-commutative connection conditions with equivalent expressions that allow for consideration of additional connection orders that would otherwise violate the ordering rules of the non-commutative connection condition. Elements on the right-hand side of a non-commutative connection condition are converted into distinct results elements, thereby allowing the elements to be used on the left-hand side of a commutative connection operator, while preserving accuracy of the statement results set. This increased set of connection orders is evaluated and the execution plan for the optimal connection order is selected.

Systems and methods for increasing allowable join orders in query optimization

A method and system for performing partition pruning for queries that include a non-single table predicate is disclosed. According to an embodiment of the invention, this type of query is processed by performing a transformation of the query to include additional predicates comprising subqueries. The transformed query includes single table predicates on the partitioning column of the table being queried, based upon join predicates that exist in the original query.

Method and mechanism for partition pruning

An embodiment of the invention optimizes a DBMS operation based upon multiple criteria, such as input/outputs and CPU time. Multiple optimization criteria are normalized to a common unit of measurement. Normalized optimization criteria are used in a cost function. Estimated execution times are calculated under a variety of different workload environments. Cost values are scaled to a variety of computer architectures.

Query optimizer cost model

Techniques for processing, in a database management system, a query referencing a plurality of tables of a database include determining whether a join should be performed between a first table and a second table. The determination is based on a particular set of one or more columns of the second table referenced in the query and a join index on the first table for an index key column from the second table. If it is determined that the join should not be performed, the query is processed without performing the join between the first table and the second table. By avoiding one or more joins, substantial savings in the consumption of computational resources are achieved.

Techniques for eliminating database table joins based on a join index

A method and apparatus for segmenting bitmaps in a bitmap index is provided. A segmented bitmap includes a plurality of bitmap segments that are used to indicate which records in a body of records that is associated with the segmented bitmap satisfy a particular criteria. Each bitmap segment contains a string of bits that correspond to a corresponding range of records in the body of records. There may be gaps between the ranges represented by the bitmap segments when, for example, the body of records does not contain records in a particular range. For the purposes of retrieval, compression, de-compression, locking and logging, the database system treats each bitmap segment as a distinct data item.

Bitmap segmentation

Bitmap index compression

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San Francisco, CA, United States of America

Hakan Jakobsson