公开(公告)号：US12189652B2

公开(公告)日：2025-01-07

申请号：US18174535

申请日：2023-02-24

Applicant: Oracle International Corporation

Inventor： Iraklis Psaroudakis ,  Stefan Kaestle ,  Daniel J. Goodman ,  Jean-Pierre Lozi ,  Matthias Grimmer ,  Timothy L. Harris

IPC: G06F9/455 ,  G06F9/54 ,  G06F16/27

Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.

公开(公告)号：US12001425B2

公开(公告)日：2024-06-04

申请号：US17116831

申请日：2020-12-09

Applicant: Oracle International Corporation

Inventor： Tomas Faltin ,  Vasileios Trigonakis ,  Jean-Pierre Lozi ,  Sungpack Hong ,  Hassan Chafi

IPC: G06F16/2452 ,  G06F16/2455 ,  G06F16/248

CPC classification number: G06F16/24526 ,  G06F16/24556 ,  G06F16/248

Abstract: Systems and methods for improving evaluation of graph queries through depth first traversals are described herein. In an embodiment, a multi-node system evaluates against graph data a graph query that specifies a particular pattern to match by determining, at a first node of the multi-node system, in a particular instance of evaluating the graph query, that one or more first vertices on the first node match a first portion of the graph query and that a second vertex that is to be evaluated next is stored on a second node separate from the first node. In response to determining that the next vertex to be evaluated is stored on the second node separate from the first node, the first node generates a message to the second node comprising one or more results of the first portion of the graph query based on the one or more first vertices, an identifier of the next vertex, and a current stage of evaluating the graph query. In response to generating the message from the first node to the second node, the first node ceases the particular instance of evaluating the graph query.

公开(公告)号：US11675785B2

公开(公告)日：2023-06-13

申请号：US16778668

申请日：2020-01-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Vasileios Trigonakis ,  Tomas Faltin ,  Jean-Pierre Lozi ,  Vlad Ioan Haprian ,  Sungpack Hong ,  Hassan Chafi

IPC: G06F16/2452 ,  G06F16/2458

CPC classification number: G06F16/24526 ,  G06F16/2471

Abstract: Techniques are described for enabling in-memory execution of any-sized graph data query by utilizing both depth first search (DFS) principles and breadth first search (BFS) principles to control the amount of memory used during query execution. Specifically, threads implementing a graph DBMS switch between a BFS mode of data traversal and a DFS mode of data traversal. For example, when a thread detects that there are less than a configurable threshold number of intermediate results in memory, the thread enters BFS-based traversal techniques to increase the number of intermediate results in memory. When the thread detects that there are at least the configurable threshold number of intermediate results in memory, the thread enters DFS mode to produce final results, which generally works to move the intermediate results that are currently available in memory to final query results, thereby reducing the number of intermediate results in memory.

公开(公告)号：US20210286790A1

公开(公告)日：2021-09-16

申请号：US16816686

申请日：2020-03-12

Applicant: Oracle International Corporation

Inventor： Jean-Pierre Lozi ,  Marco Arnaboldi ,  Laurent Phillipe Daynes ,  Vlad Ioan Haprian ,  Hugo Kapp

IPC: G06F16/22 ,  G06F16/21

Abstract: In an embodiment, a computer obtains a mapping of a relational schema of a database to a graph data model. The relational schema identifies vertex table(s) that correspond to vertex type(s) in the graph data model and edge table(s) that correspond to edge type(s) in the graph data model. Each edge type is associated with a source vertex type and a target vertex type. Based on that mapping, a forward compressed sparse row (CSR) representation is populated for forward traversal of edges of a same edge type. Each edge originates at a source vertex and terminates at a target vertex. Based on the forward CSR representation, a reverse CSR representation of the edge type is populated for reverse traversal of the edges of the edge type. Acceleration occurs in two ways. Values calculated for the forward CSR are reused for the reverse CSR. Elastic and inelastic scaling may occur.

公开(公告)号：US20210224235A1

公开(公告)日：2021-07-22

申请号：US16747827

申请日：2020-01-21

Applicant: Oracle International Corporation

Inventor： Marco Arnaboldi ,  Jean-Pierre Lozi ,  Laurent Phillipe Daynes ,  Vlad Ioan Haprian ,  Shasank Kisan Chavan ,  Hugo Kapp ,  Sungpack Hong

IPC: G06F16/21 ,  G06F16/901 ,  G06F16/28 ,  G06F16/27

Abstract: Herein are techniques that concurrently populate entries in a compressed sparse row (CSR) encoding, of a type of edge of a heterogenous graph. In an embodiment, a computer obtains a mapping of a relational schema to a graph data model. The relational schema defines vertex tables that correspond to vertex types in the graph data model, and edge tables that correspond to edge types in the graph data model. Each edge type is associated with a source vertex type and a target vertex type. For each vertex type, a sequence of persistent identifiers of vertices is obtained. Based on the mapping and for a CSR representation of each edge type, a source array is populated that, for a same vertex ordering as the sequence of persistent identifiers for the source vertex type, is based on counts of edges of the edge type that originate from vertices of the source vertex type. For the CSR, the computer populates, in parallel and based on said mapping, a destination array that contains canonical offsets as sequence positions within the sequence of persistent identifiers of the vertices.

公开(公告)号：US20210042323A1

公开(公告)日：2021-02-11

申请号：US17067479

申请日：2020-10-09

Applicant: Oracle International Corporation

Inventor： Iraklis Psaroudakis ,  Stefan Kaestle ,  Daniel J. Goodman ,  Jean-Pierre Lozi ,  Matthias Grimmer ,  Timothy L. Harris

IPC: G06F16/27 ,  G06F9/54 ,  G06F9/455

Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.

公开(公告)号：US10803087B2

公开(公告)日：2020-10-13

申请号：US16165593

申请日：2018-10-19

Applicant: Oracle International Corporation

Inventor： Iraklis Psaroudakis ,  Stefan Kaestle ,  Daniel J. Goodman ,  Jean-Pierre Lozi ,  Matthias Grimmer ,  Timothy L. Harris

IPC: G06F16/27 ,  G06F9/50 ,  G06F9/455 ,  G06F9/54

Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.

公开(公告)号：US20200125668A1

公开(公告)日：2020-04-23

申请号：US16165593

申请日：2018-10-19

Applicant: Oracle International Corporation

Inventor： Iraklis Psaroudakis ,  Stefan Kaestle ,  Daniel J. Goodman ,  Jean-Pierre Lozi ,  Matthias Grimmer ,  Timothy L. Harris

IPC: G06F17/30 ,  G06F9/54 ,  G06F9/455

Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.

公开(公告)号：US20200073868A1

公开(公告)日：2020-03-05

申请号：US16378424

申请日：2019-04-08

Applicant: Oracle International Corporation

Inventor： Arnaud Delamare ,  Vasileios Trigonakis ,  Vlad Ioan Haprian ,  Oskar Van Rest ,  Sungpack Hong ,  Hassan Chafi ,  Tomas Faltin ,  Jean-Pierre Lozi

IPC: G06F16/2453 ,  G06F16/901 ,  G06F16/22 ,  H03M7/30

Abstract: Techniques are described herein for space-efficient encoding of label information of property graphs. In an embodiment, an input graph is received. The input graph comprises a plurality of entities and a plurality of label sets. Each entity of said plurality of entities is associated with a label set of the plurality of label sets and each label set of the plurality of label sets comprises zero or more labels of a plurality of labels. A first mapping is generated that maps each label of the plurality of labels to a label code. A second mapping is generated that maps each label integer set of a plurality of label integer sets to a label code. Each label integer set of the plurality of label integer sets corresponds to a label set of the plurality of label sets, wherein each label integer set of the plurality of label integer sets comprises label codes from the first mapping that are mapped to each label included in the corresponding label set. A compressed label set is generated for each entity of the plurality of entities. Each compressed label set comprises a plurality of bits that indicate a zeroth state, a first state, a second state, or a third state. The compressed label sets and the first and second mappings are used to efficiently evaluate graph label queries.