Oracle International Corporation

Oracle America, Inc.

Sun Microsystems, Inc.

Laurent Daynes

Vlad Ioan Haprian

Sungpack Hong

Marco Arnaboldi

Hugo Kapp

Hassan Chafi

Grzegorz Jan Czajkowski

Jean-Pierre Lozi

Zhen Hua Liu

Andrew Witkowski

Shasank Kisan Chavan

Matthias Brantner

Lei Sheng

Bastian Hossbach

Benjamin Schlegel

Pit Fender

Vasileios Trigonakis

Alexander Ulrich

Thomas Schatzl

Sunil Soman

Ciprian Baetu

Gregory M Wright

Sabina Petride

Anantha Kiran Kandukuri

Michal Wegiel

Ioannis Alagiannis

Eric Sedlar

Mario I Wolczko

Bernd J Mathiske

Huagang Li

Harshad Kasture

Gregory Bollella

Angela Amor

Krzysztof Palacz

Christian Humer

Guido Wachsmuth

Andrew McClure

Christophe Lizzi

Danica Porobic

Chrislain Razafimahefa

Bernd Jw Mathiske

Andrea Segalini

Chun-Hung Tseng

Jürgen Christ

Juergen Christ

Lucas Braun

Alexey Karyakin

A method and apparatus for graph processing in a relational database environment is provided. Preprocessing transformations that enforce restrictions on a high-level domain-specific language (DSL) input is performed to generate one or more graph iterations. At least one query intermediate representation (IR) is generated by lowering the one or more graph iterations into at least one query. The query IR is/are mapped to one or more corresponding relational queries with procedural extensions. The relational queries with procedural extensions are run against relational database management system (RDBMS) tables representing at least one property graph.

Compilation techniques for algorithmic graph processing in a relational database

A computer-implemented method includes receiving a graph pattern matching query, determining that the query is an unbounded recursive path query (RPQ), and initializing an unbounded root vertex match operator (UBRM) using a starting vertex as input to the UBRM; the UBRM is configured to compute multiple hops between vertices on the graph. The method also includes performing a first reachability search using the UBRM; the UBRM traverses the graph to identify all vertices reachable from the starting vertex. The method further includes generating first level vertices of a path pattern that computes the RPQ; and adding the first level vertices to a first level context for processing by a successor match operator; the successor match operator comprises an unbounded intermediate neighbor match operator (UBNM), the UBNM is configured to compute multiple hops between vertices on the graph and to identify neighbors for each of the first level vertices.

In-memory processing of unbounded recursive graph queries in relational databases

A breadth first search (BFS) algorithm is provided that uses out-of-core external storage in a memory constrained system. Memory resources are used as long as they are available and external storage is used when necessary due to memory pressure. The BFS algorithm uses a disk-spilling hash-table (DSH) as the visited set and disk-spilling queues (DSQs) as the BFS frontier queue. To get the most out of the DSH, subsequent inserts and lookups must happen in the same DSH partition. To ensure that consecutive lookups happen in the same DSH partition, the BFS frontier queue is partitioned in a manner similar to the DSH partitions.

Out-of-core BFS for shortest path graph queries

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

The present invention relates to execution optimization of database queries. Herein are techniques for optimal execution based on query interpretation by translation to a domain specific language (DSL), with optimizations such as partial evaluation, abstract syntax tree (AST) rewriting, just in time (JIT) compilation, dynamic profiling, speculative logic, and Futamura projection. In an embodiment, a database management system (DBMS) that is hosted on a computer generates a query tree that represents a database query that contains an expression that is represented by a subtree of the query tree. The DBMS generates a sequence of DSL instructions that represents the subtree. The sequence of DSL instructions is executed to evaluate the expression during execution of the database query. In an embodiment, an AST is generated from the sequence of DSL instructions. In an embodiment, the DSL AST is optimally rewritten based on a runtime feedback loop that includes dynamic profiling information.

First Futamura projection in the context of SQL expression evaluation

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

First futamura projection in the context of SQL expression evaluation

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

Growing community of inventors

Saint-Ismier, France

Laurent Daynes