FAIR AND EFFICIENT CONCURRENCY MANAGEMENT FOR GRAPH PROCESSING

    公开(公告)号:US20190235913A1

    公开(公告)日:2019-08-01

    申请号:US15886745

    申请日:2018-02-01

    CPC classification number: G06F9/48

    Abstract: Techniques are described herein for concurrently evaluating graph processing tasks in a fair and efficient manner. In an embodiment, a request to execute a graph processing task is received. A first mapping associates each graph processing task of a plurality of graph processing tasks to a set of workload characteristics of a plurality of sets of workload characteristics. A second mapping associates each set of workload characteristics of the plurality of sets of workload characteristics to a set of execution parameters of a plurality of sets of execution parameters. Using the first mapping, a set of workload characteristics is determined based on the graph processing task. Using the second mapping, a set of execution parameters is determined based on the determined set of workload characteristics. The graph processing task is executed based on the determined set of execution parameters.

    Dynamic asynchronous traversals for distributed graph queries

    公开(公告)号:US11675785B2

    公开(公告)日:2023-06-13

    申请号:US16778668

    申请日:2020-01-31

    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.

    FAST IN-MEMORY TECHNIQUE TO BUILD A REVERSE CSR GRAPH INDEX IN AN RDBMS

    公开(公告)号:US20210286790A1

    公开(公告)日:2021-09-16

    申请号:US16816686

    申请日:2020-03-12

    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.

    PARALLEL AND EFFICIENT TECHNIQUE FOR BUILDING AND MAINTAINING A MAIN MEMORY CSR BASED GRAPH INDEX IN A RDBMS

    公开(公告)号:US20210224235A1

    公开(公告)日:2021-07-22

    申请号:US16747827

    申请日:2020-01-21

    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.

    SPACE-EFFICIENT METHODOLOGY FOR REPRESENTING LABEL INFORMATION IN LARGE GRAPH DATA FOR FAST DISTRIBUTED GRAPH QUERY

    公开(公告)号:US20200073868A1

    公开(公告)日:2020-03-05

    申请号:US16378424

    申请日:2019-04-08

    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.

    Deterministic semantic for graph property update queries and its efficient implementation

    公开(公告)号:US11928097B2

    公开(公告)日:2024-03-12

    申请号:US17479006

    申请日:2021-09-20

    CPC classification number: G06F16/2315 G06F11/0772 G06F16/2365

    Abstract: Efficiently implemented herein is a deterministic semantic for property updates by graph queries. Mechanisms of determinism herein ensure data consistency for graph mutation. These mechanisms facilitate optimistic execution of graph access despite a potential data access conflict. This approach may include various combinations of special activities such as detecting potential conflicts during query compile time, applying query transformations to eliminate those conflicts during code generation where possible, and executing updates in an optimistic way that safely fails if determinism cannot be guaranteed. In an embodiment, a computer receives a request to modify a graph. The request to modify the graph is optimistically executed after preparation and according to safety precautions as presented herein. Based on optimistically executing the request, a data access conflict actually occurs and is automatically detected. Based on the data access conflict, optimistically executing the request is prematurely and automatically halted without finishing executing the request.

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