Oracle International Corporation

Dina Thomas

Tirthankar Lahiri

Shasank Kisan Chavan

Ajit Mylavarapu

Chi-Kim Hoang

Prashant Gaharwar

Kirk Meredith Edson

Subhradyuti Sarkar

Mark L McAuliffe

Anindya Chandra Patthak

Marie-Anne Neimat

Chih-Ping Wang

Dennis Lui

Sheldon A K Lewis

Martin Reames

Kao Makino

Neelam Goyal

Qingchun Jiang

Jesse Kamp

John William Miller

Rohan Aranha

Nagender Bandi

Roger Dermot Macnicol

Sourav Ghosh

Paul Tuck

Martin A Reames

Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated 'in-place', that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language (“DDL”) commands that declare the row-major columns and column-major columns of a table.

Combined row and columnar storage for in-memory databases for OLTP and analytics workloads

Techniques are described herein for reducing the number of redundant evaluations that occur when an expression is evaluated against an encoded column vector by caching results of expression evaluations. When executing a query that includes an expression that references columns for which dictionary-encoded column vectors exist, the database server performs a cost-based analysis to determine which expressions (or sub-expressions) would benefit from caching the expression's evaluation result. For each such expression, the database server performs the necessary computations and caches the results for each of the possible distinct input values. When evaluating an expression for a row with a particular set of input codes, a look-up is performed based on the input code combination to retrieve the pre-computed results of that evaluation from the cache.

Evaluating SQL expressions on dictionary encoded vectors

In column domain dictionary compression, column values in one or more columns are tokenized by a single dictionary. The domain of the dictionary is the entire set of columns. A dictionary may not only map a token to a tokenized value, but also to a count ('token count') of the number of occurrences of the token and corresponding tokenized value in the dictionary's domain. Such information may be used to compute queries on the base table.

Column domain dictionary compression

Techniques are described herein for sharing a dictionary across multiple in-memory compression units (IMCUs). After a dictionary is used to encode a first column vector in a first IMCU, the same dictionary is used to encode a second column vector in a second IMCU. The entries in the dictionary are in sort order to facilitate binary searching when performing value-to-code look-ups. If, during the encoding of the second column vector, values are encountered for which the dictionary does not already have codes, then a 'sort-order-boundary' is established after the last entry in the dictionary, and entries for the newly encountered values are added to the dictionary, after the sort-order-boundary. To facilitate value-to-code look-ups, the new entries are also sorted relative to each other, creating a second 'sort order set'. A new version of the dictionary may be created when the number of sort order sets in the first version of the dictionary reaches a configurable threshold.

In-memory column-level multi-versioned global dictionary for in-memory databases

Techniques are described for encoding join columns that belong to the same domain with a common dictionary. The tables are encoded with dictionary indexes that make the comparison operation of a join query a quick equality check of two integers and there is no need to compute any hashes during execution. Additionally, the techniques described herein minimize the bloom filter creation and evaluation cost as well because the dictionary indexes serve as hash values into the bloom filter. If the bloom filter is as large as the range of dictionary indexes, then the filter is no longer a probabilistic structure and can be used to filter rows in the probe phase with full certainty without any significant overhead.

Using shared dictionaries on join columns to improve performance of joins in relational databases

Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated 'in-place', that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language ('DDL') commands that declare the row-major columns and column-major columns of a table.

A replication track is a designated group of transactions that are to be replicated at a destination database in a way that, with respect to any other transaction in the replication track, preserves transactional dependency. Further, transactions in a replication track can be replicated at the destination database without regard to transactional dependency of other transactions in another track. This facilitates concurrent parallel replication of transactions of different tracks. Replicating data in this manner is referred to herein as track replication. An application may request execution of transactions and designate different tracks for transactions.

User-defined parallelization in transactional replication of in-memory database

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Palo Alto, CA, United States of America

Dina Thomas