Oracle International Corporation

Marco Arnaboldi

Laurent Daynes

Vlad Ioan Haprian

Jean-Pierre Lozi

Sungpack Hong

Hugo Kapp

Zhen Hua Liu

Hassan Chafi

Andrew Witkowski

Shasank Kisan Chavan

Lei Sheng

Vasileios Trigonakis

Ciprian Baetu

Sabina Petride

Danica Porobic

Andrea Segalini

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query that includes a bounded recursive pattern query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries that include bounded recursive patterns on top of the relational engine by avoiding any change in the existing SQL engine.

Efficient compilation of bounded recursive graph queries on top of SQL based relational engine

An RDBMS specifies a graph algorithm function (GAF) that takes a graph object as input and returns a logical graph object as output. GAFs are used within graph queries to compute temporary and output properties ('GAF-computed properties'), which are live for the duration of the query cursor execution. GAF-computed output properties are accessible in the enclosing graph pattern matching query as though they were part of the input graph object of the GAF. Temporary cursor-duration tables are generated for the query cursor during compilation of a graph query that includes a GAF, and are used to store the GAF-computed properties. Each temporary table corresponds to one of the primary tables of the input graph, and includes, as a foreign key, primary key information from the corresponding primary table.

Using temporary tables to store graph algorithm results for a relational database management system

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Efficient compilation of graph queries including complex expressions on top of sql based relational engine

Techniques described herein allow a user of an RDBMS to specify a graph algorithm function (GAF) declaration, which defines a graph algorithm that takes a graph object as input and returns a logical graph object as output. A database dictionary stores the GAF declaration, which allows addition of GAFs without changing the RDBMS kernel. GAFs are used within graph queries to compute output properties of property graph objects. Output properties are accessible in the enclosing graph pattern matching query, and are live for the duration of the query cursor execution. According to various embodiments, the declaration of a GAF includes a DESCRIBE function, used for semantic analysis of the GAF, and an EXECUTE function, which defines the operations performed by the GAF. Furthermore, composition of GAFs in a graph query is done by supplying, as the input graph argument of an outer GAF, the result of an inner GAF.

Inline graph algorithm execution with a relational SQL engine

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.

Parallel and efficient technique for building and maintaining a main memory CSR based graph index in a RDBMS

Efficient compilation of graph queries on top of SQL based relational engine

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.

Fast in-memory technique to build a reverse CSR graph index in an RDBMS

Efficient compilation of graph queries involving long graph query patterns on top of SQL based relational engine

Vertex/edge table rows are mapped to unique integer identifiers, to enable construction of in-memory representation of a graph from existing, unmodified RDBMS tables. The unique integer identifiers are based on an encoding of primary key values of the table rows. The unique integer identifiers are used as graph indexes of the in-memory representation.

Efficient identification of vertices and edges for graph indexes in an RDBMS

An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Hybrid in-memory BFS-DFS approach for computing graph queries involving complex path patterns including trees and cycles inside relational database systems

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Zurich, Switzerland

Marco Arnaboldi