公开(公告)号：US20150269180A1

公开(公告)日：2015-09-24

申请号：US14337113

申请日：2014-07-21

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： VICTOR CHEN ,  ANINDYA PATTHAK ,  SHASANK KISAN CHAVAN ,  JESSE KAMP ,  VINEET MARWAH ,  AMIT GANESH

IPC: G06F17/30

CPC classification number: G06F17/30153 ,  G06F12/04 ,  H03M7/3066 ,  H03M7/3088 ,  H03M7/6005 ,  H03M7/6011

Abstract: A method, apparatus, and system for OZIP, a data compression and decompression codec, is provided. OZIP utilizes a fixed size static dictionary, which may be generated from a random sampling of input data to be compressed. Compression by direct token encoding to the static dictionary streamlines the encoding and avoids expensive conditional branching, facilitating hardware implementation and high parallelism. By bounding token definition sizes and static dictionary sizes to hardware architecture constraints such as word size or processor cache size, hardware implementation can be made fast and cost effective. For example, decompression may be accelerated by using SIMD instruction processor extensions. A highly granular block mapping in optional stored metadata allows compressed data to be accessed quickly at random, bypassing the processing overhead of dynamic dictionaries. Thus, OZIP can support low latency random data access for highly random workloads, such as for OLTP systems.

Abstract translation: 提供了一种用于OZIP（数据压缩和解压缩编解码器）的方法，装置和系统。 OZIP使用固定大小的静态字典，其可以从要压缩的输入数据的随机采样生成。 通过直接令牌编码压缩到静态字典可以简化编码，避免昂贵的条件分支，便于硬件实现和高并行性。 通过将令牌定义大小和静态字典大小绑定到诸如字大小或处理器高速缓存大小之类的硬件架构约束，可以实现硬件实现的快速和成本效益。 例如，可以通过使用SIMD指令处理器扩展来加速解压缩。 可选存储的元数据中的高度细粒度的块映射允许随机地快速访问压缩数据，从而绕过动态词典的处理开销。 因此，OZIP可以支持高度随机工作负载的低延迟随机数据访问，例如OLTP系统。

公开(公告)号：US20190197026A1

公开(公告)日：2019-06-27

申请号：US16287569

申请日：2019-02-27

Applicant: Oracle International Corporation

Inventor： TIRTHANKAR LAHIRI ,  MARTIN A. REAMES ,  KIRK EDSON ,  NEELAM GOYAL ,  KAO MAKINO ,  ANINDYA PATTHAK ,  DINA THOMAS ,  SUBHRADYUTI SARKAR ,  CHI-KIM HOANG ,  QINGCHUN JIANG

IPC: G06F16/21 ,  G06F16/27

CPC classification number: G06F16/211 ,  G06F16/21 ,  G06F16/278

Abstract: 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.

公开(公告)号：US20170300510A1

公开(公告)日：2017-10-19

申请号：US15640286

申请日：2017-06-30

Applicant: Oracle International Corporation

Inventor： ANINDYA PATTHAK ,  VICTOR CHEN ,  SHASANK KISAN CHAVAN ,  JESSE KAMP ,  AMIT GANESH ,  VINEET MARWAH

IPC: G06F17/30 ,  H03M7/30 ,  G06F12/04

CPC classification number: G06F16/1744 ,  G06F12/04 ,  H03M7/3066 ,  H03M7/3088 ,  H03M7/6005 ,  H03M7/6011

Abstract: A method, apparatus, and system for OZIP, a data compression and decompression codec, is provided. OZIP utilizes a fixed size static dictionary, which may be generated from a random sampling of input data to be compressed. Compression by direct token encoding to the static dictionary streamlines the encoding and avoids expensive conditional branching, facilitating hardware implementation and high parallelism. By bounding token definition sizes and static dictionary sizes to hardware architecture constraints such as word size or processor cache size, hardware implementation can be made fast and cost effective. For example, decompression may be accelerated by using SIMD instruction processor extensions. A highly granular block mapping in optional stored metadata allows compressed data to be accessed quickly at random, bypassing the processing overhead of dynamic dictionaries. Thus, OZIP can support low latency random data access for highly random workloads, such as for OLTP systems.