Sybase, Inc.

Sap Se

David Edward DeHaan

Mihnea Andrei

Colin Florendo

Anil Kumar Goel

Rolando Blanco

Anisoara Nica

Ivan T Bowman

Daniel James Farrar

Güneș Aluç

Sergiu Pocol

Yogi Joshi

Systems, methods, and computer media are described for index-based join size estimation. For a join operation between two tables, a filter is applied to the first table, resulting in a filter output. The filter output is then sampled. For each sample, an index for a second table is accessed and counts of records in the second table that match the sample are retrieved. Using the sample size and the retrieved counts from the index of the second table, a data size for the join operation can be efficiently and accurately estimated. Statistical confidence in the estimate can also be assessed using variance-based calculations.

Index-based, adaptive join size estimation

A delta store giving row-level versioning semantics to a non-row-level versioning underlying store is described. An example method includes establishing a column-based in-memory database including a main store and a delta store, where the main store allows only non-concurrent transactions on a same table and the delta store has a plurality of row-visibility bitmaps implementing a row-level versioning mechanism that allows concurrent transactions on the same table. A local RID space is established for a table fragment, that for each table in the database, the data of the table is stored in one or more main table fragment in the main store and in one or more delta table fragments in the delta store. Each table fragment has a local RID space, and the local RID space is a collection of one-based contiguous integer local RIDs (Row IDs) describing local positions of the rows of the table fragment.

Multiple RID spaces in a delta-store-based database to support long running transactions

A method and system to determine a q, &#x3b8;-optimal histogram comprising a plurality of buckets over a data distribution where for any cardinality estimate made using the histogram the cardinality estimate is constrained to obey an acceptability criteria parameterized by q and &#x3b8; that bounds a ratio error between the cardinality estimate and a true value of the cardinality, q being a factor by which the estimate deviates, at most, from a true value of the cardinality and &#x3b8; being a threshold value which the cardinality does not exceed, wherein a maximum number of possible query intervals generated in determining the acceptability of the q, &#x3b8;-optimal histogram is less than quadratic in the number of values.

Methods and systems for constructing q, &#x3b8;-optimal histogram buckets

A locking mechanism in a delta-store-based database to support long running transactions across multiple RID spaces is described. An example method includes establishing a column-based in-memory database including a main store and a delta store. A delete or an update statement is executed with a transaction on a table having plurality of table versions. The table versions are represented by bitmaps in the delta store and the bitmaps and table fragments corresponding to the table versions implement RID spaces for the table. A lock on a row of the table manipulated by the delete or the update statement is requested to preclude other transaction from deleting or updating an obsolete version of data. Upon a successful validation that the row to be locked is not the obsolete version in the RID spaces of the table, a lock is granted to the transaction.

Locking across multiple RID spaces

A query plan predictor receives a query instance according to a query template having a set of parameters. The query plan predictor determines a plan space point for the query instance which is based upon the set of parameters. The query plan predictor predicts a predicted query plan for the plan space point, which is selected from a map of query plans according to a confidence threshold from a plan cache that includes at least one histogram. Each histogram is representative of a cached query plan in the plan cache combined with a previously-selected locality-sensitive hashing function. The histogram stores a frequency distribution of a mapping of sample plan points for the cached query plan by the locality-sensitive hashing function.

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Waterloo, Canada

David Edward DeHaan