Patents by Inventor Anindya Patthak

Anindya Patthak has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10437781
    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.
    Type: Grant
    Filed: June 30, 2017
    Date of Patent: October 8, 2019
    Assignee: Oracle International Corporation
    Inventors: Anindya Patthak, Victor Chen, Shasank Kisan Chavan, Jesse Kamp, Amit Ganesh, Vineet Marwah
  • Publication number: 20190197026
    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.
    Type: Application
    Filed: February 27, 2019
    Publication date: June 27, 2019
    Inventors: TIRTHANKAR LAHIRI, MARTIN A. REAMES, KIRK EDSON, NEELAM GOYAL, KAO MAKINO, ANINDYA PATTHAK, DINA THOMAS, SUBHRADYUTI SARKAR, CHI-KIM HOANG, QINGCHUN JIANG
  • Patent number: 10311154
    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.
    Type: Grant
    Filed: December 5, 2013
    Date of Patent: June 4, 2019
    Assignee: Oracle International Corporation
    Inventors: Tirthankar Lahiri, Martin A. Reames, Kirk Edson, Neelam Goyal, Kao Makino, Anindya Patthak, Dina Thomas, Subhradyuti Sarkar, Chi-Kim Hoang, Qingchun Jiang
  • Publication number: 20170300510
    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.
    Type: Application
    Filed: June 30, 2017
    Publication date: October 19, 2017
    Inventors: ANINDYA PATTHAK, VICTOR CHEN, SHASANK KISAN CHAVAN, JESSE KAMP, AMIT GANESH, VINEET MARWAH
  • Patent number: 9697221
    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.
    Type: Grant
    Filed: July 21, 2014
    Date of Patent: July 4, 2017
    Assignee: Oracle International Corporation
    Inventors: Anindya Patthak, Victor Chen, Shasank Kisan Chavan, Jesse Kamp, Amit Ganesh, Vineet Marwah
  • Publication number: 20150269180
    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.
    Type: Application
    Filed: July 21, 2014
    Publication date: September 24, 2015
    Inventors: VICTOR CHEN, ANINDYA PATTHAK, SHASANK KISAN CHAVAN, JESSE KAMP, VINEET MARWAH, AMIT GANESH
  • Publication number: 20150088813
    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.
    Type: Application
    Filed: December 5, 2013
    Publication date: March 26, 2015
    Applicant: Oracle International Corporation
    Inventors: Tirthankar Lahiri, Martin A. Reames, Kirk Edson, Neelam Goyal, Kao Makino, Anindya Patthak, Dina Thomas, Subhradyuti Sarkar, Chi-Kim Hoang, Qingchun Jiang