公开(公告)号：US20200250179A1

公开(公告)日：2020-08-06

申请号：US16826113

申请日：2020-03-20

Applicant: Cloudera, Inc.

Inventor： Rituparna Agrawal ,  Anupam Singh ,  Prithviraj Pandian

IPC: G06F16/248 ,  G06F16/2455 ,  G06F16/28 ,  G06F16/21 ,  G06F16/84

Abstract: Systems and methods very fast grouping of “similar” SQL queries according to user-supplied similarity criteria are disclosed. The user-supplied similarity criteria includes a threshold quantifying the degree of similarity between SQL queries and common artifacts included in the queries. A similarity-characterizing data structure is disclosed that allows for the very fast grouping of “similar” SQL queries. Because the computation is distributed among multiple compute nodes, a small cluster of compute nodes takes a short time to compute the similarity-characterizing data on a workload of tens of millions of queries. The user can supply the similarity criteria through a UI or a command line tool. Furthermore, in some embodiments, the user can adjust the degree of similarity by supplying new similarity criteria. Accordingly, the system can display in real time or near real time, updated SQL groupings corresponding to the newly supplied similarity criteria using the originally computed similarity-characterizing data structure.

公开(公告)号：US10599664B2

公开(公告)日：2020-03-24

申请号：US15495397

申请日：2017-04-24

Applicant: Cloudera, Inc.

Inventor： Rituparna Agrawal ,  Anupam Singh ,  Prithviraj Pandian

IPC: G06F16/248 ,  G06F16/84 ,  G06F16/21 ,  G06F16/28 ,  G06F16/2455

Abstract: Systems and methods for very fast grouping of “similar” SQL queries according to user-supplied similarity criteria. The user-supplied similarity criteria include a threshold quantifying the degree of similarity between SQL queries and common artifacts included in the queries. A similarity-characterizing data structure allows for the very fast grouping of “similar” SQL queries. Because the computation is distributed among multiple compute nodes, a small cluster of compute nodes takes a short time to compute the similarity-characterizing data on a workload of tens of millions of queries. The user can supply the similarity criteria through a UI or a command line tool. Furthermore, the user can adjust the degree of similarity by supplying new similarity criteria. Accordingly, the system can display in real time or near real time, updated SQL groupings corresponding to the newly supplied similarity criteria using the originally computed similarity-characterizing data structure.

公开(公告)号：US20230350906A1

公开(公告)日：2023-11-02

申请号：US18127322

申请日：2023-03-28

Applicant: Cloudera, Inc.

Inventor： Rituparna Agrawal ,  Anupam Singh ,  Prithviraj Pandian

IPC: G06F16/248 ,  G06F16/84 ,  G06F16/21 ,  G06F16/28 ,  G06F16/2455

CPC classification number: G06F16/248 ,  G06F16/86 ,  G06F16/211 ,  G06F16/285 ,  G06F16/2455

Abstract: Systems and methods for very fast grouping of “similar” SQL queries according to user-supplied similarity criteria. The user-supplied similarity criteria include a threshold quantifying the degree of similarity between SQL queries and common artifacts included in the queries. A similarity-characterizing data structure allows for the very fast grouping of “similar” SQL queries. Because the computation is distributed among multiple compute nodes, a small cluster of compute nodes takes a short time to compute the similarity-characterizing data on a workload of tens of millions of queries. The user can supply the similarity criteria through a UI or a command line tool. Furthermore, the user can adjust the degree of similarity by supplying new similarity criteria. Accordingly, the system can display in real time or near real time, updated SQL groupings corresponding to the newly supplied similarity criteria using the originally computed similarity-characterizing data structure.

公开(公告)号：US20170308592A1

公开(公告)日：2017-10-26

申请号：US15495397

申请日：2017-04-24

Applicant: Cloudera, Inc.

Inventor： Rituparna Agrawal ,  Anupam Singh ,  Prithviraj Pandian

IPC: G06F17/30

CPC classification number: G06F16/248 ,  G06F16/211 ,  G06F16/2455 ,  G06F16/285 ,  G06F16/86

Abstract: Systems and methods very fast grouping of “similar” SQL queries according to user-supplied similarity criteria are disclosed. The user-supplied similarity criteria includes a threshold quantifying the degree of similarity between SQL queries and common artifacts included in the queries. A similarity-characterizing data structure is disclosed that allows for the very fast grouping of “similar” SQL queries. Because the computation is distributed among multiple compute nodes, a small cluster of compute nodes takes a short time to compute the similarity-characterizing data on a workload of tens of millions of queries. The user can supply the similarity criteria through a UI or a command line tool. Furthermore, in some embodiments, the user can adjust the degree of similarity by supplying new similarity criteria. Accordingly, the system can display in real time or near real time, updated SQL groupings corresponding to the newly supplied similarity criteria using the originally computed similarity-characterizing data structure.

公开(公告)号：US11645294B2

公开(公告)日：2023-05-09

申请号：US16826113

申请日：2020-03-20

Applicant: Cloudera, Inc.

Inventor： Rituparna Agrawal ,  Anupam Singh ,  Prithviraj Pandian

IPC: G06F16/248 ,  G06F16/84 ,  G06F16/21 ,  G06F16/28 ,  G06F16/2455

CPC classification number: G06F16/248 ,  G06F16/211 ,  G06F16/2455 ,  G06F16/285 ,  G06F16/86

Abstract: Systems and methods for very fast grouping of “similar” SQL queries according to user-supplied similarity criteria. The user-supplied similarity criteria include a threshold quantifying the degree of similarity between SQL queries and common artifacts included in the queries. A similarity-characterizing data structure allows for the very fast grouping of “similar” SQL queries. Because the computation is distributed among multiple compute nodes, a small cluster of compute nodes takes a short time to compute the similarity-characterizing data on a workload of tens of millions of queries. The user can supply the similarity criteria through a UI or a command line tool. Furthermore, the user can adjust the degree of similarity by supplying new similarity criteria. Accordingly, the system can display in real time or near real time, updated SQL groupings corresponding to the newly supplied similarity criteria using the originally computed similarity-characterizing data structure.