Oracle International Corporation

Korbinian Schmid

Hassan Chafi

Sungpack Hong

Alexander Weld

Martin Sevenich

Felix Kaser

Vlad Ioan Haprian

Zhe Wu

Jayanta Banerjee

Michael Haubenschild

Siham Yousfi

Julia Kindelsberger

Hyungyu Shin

Daniel Langerenken

Vasileios Trigonakis

Oskar Van Rest

Florian Gratzer

Steven Serra

Jan Boettcher

A method, apparatus, and product to provide a parser for property graph queries with precise error reporting and auto-completion based on information from property graph schemas. The approach generally comprises analysis of graph queries prior to their execution to identify issues prior to execution. In some embodiments, the approach includes any of: use of a property graph schema to determine whether names in a received property graph query exist within a corresponding property graph; determining whether the property graph query includes a comparison of mismatched data types; providing an autocomplete suggestion feature for assistance in resolving errors or corresponding to a cursor position within a query string; or evaluation of a property graph query to determine whether it would return an empty result. In some embodiments, property graph query analysis is performed using a context aware approach.

Method, product, and system to provide a parser for property graph queries with precise error reporting and auto-completion based on information from property graph schemas

Techniques herein optimally distribute graph query processing across heterogeneous tiers. In an embodiment, a computer receives a graph query to extract a query result (QR) from a graph in a database operated by a database management system (DBMS). The graph has vertices interconnected by edges. Each vertex has vertex properties, and each edge has edge properties. The computer decomposes the graph query into filter expressions (FE's). Each FE is processed as follows. A filtration tier to execute the FE is selected from: the DBMS which sends at least the QR to a stream, a stream evaluator that processes the stream as it arrives without waiting for the entire QR to arrive and that stores at least the QR into memory, and an in-memory evaluator that identifies the QR in memory. A translation of the FE executes on the filtration tier to obtain vertices and/or edges that satisfy the FE.

Defining subgraphs declaratively with vertex and edge filters

Techniques herein are for navigation data structures for graph traversal. In an embodiment, navigation data structures that a computer stores include: a source vertex array of vertices; a neighbor array of dense identifiers of target vertices terminating edges; a bidirectional map associating, for each vertex, a sparse identifier of the vertex with a dense identifier of the vertex; and a vertex array containing, when a dense identifier of a source vertex is used as an offset, a pair of offsets defining an offset range, for use with the neighbor array. The source vertex array, using the dense identifier of a particular vertex as an offset, contains an offset, into a neighbor array, of a target vertex terminating an edge originating at the particular vertex. The neighbor array contiguously stores dense identifiers of target vertices terminating edges originating from a same source vertex.

In-memory graph analytics system that allows memory and performance trade-off between graph mutation and graph traversal

Techniques are provided for mapping tables and columns of a legacy relational schema into synthetic tables that are dedicated for graph analysis. In an embodiment, a computer receives a mapping of relational tables to node tables and edge tables. The node tables contain columns and rows. The edge tables contain columns and rows. The rows of the node tables and the rows of the edge tables define a graph. Based on the mapping and the relational tables, the computer calculates a value of at least one column of at least one row of the node tables. Based on an execution of a query of the graph, the computer returns the value.

Graph processing system that can define a graph view from multiple relational database tables

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.

Fair and efficient concurrency management for graph processing

Techniques herein visually construct and summarize property graphs. In an embodiment, a computer loads raw data tables and generates vertices and edges based on the data tables. Vertices may be disconnected or may be interconnected by edges based on foreign keys. In an embodiment, a junction vertex can be converted into a special edge that interconnects two other vertices. In an embodiment, a junction vertex can be converted into a self-directed edge that interconnects data within a single vertex. Filtration can be applied when loading raw tables and then adjusted after visual construction of vertices and edges.

Visual graph construction from relational data

Techniques are provided for strategy-based graph simplification. In an embodiment, a computer provides configurable strategies that simplify edges of a graph. A client selects and configures a strategy subset of the configurable strategies to define a particular simplification scheme. The computer simplifies a graph by applying the strategy subset to the graph. In embodiments, predefined classes or other application programming interface (API) is provided to clients to obtain and customize strategy instances, such as with a factory or builder. Strategy instances may be imperative or declarative. A service implementation, such as a graph engine, may be embedded or remoted. Techniques herein provide for reuse and optimization.

Graph processing system that allows flexible manipulation of edges and their properties during graph mutation

Techniques herein organize and display as branches the historical versions of filtrations of a property graph in a way that suits interactive exploration. In embodiments, a computer loads metadata that describes versions of filtration of a graph that contains vertices interconnected by edges. Based on the metadata, the computer displays, along a timeline, version indicators that each represents a respective historical version of filtration of the graph. The computer displays, responsive to receiving an interactive selection of a particular version indicator of the plurality of version indicators, a particular version of filtration of the graph that is represented by the particular version indicator. Subsequences of versions for the timeline may be organized as branches that may be interactively created and merged. Branching and merging are integrated into the general lifecycle of graph filtration. A version timeline may be presented and operated as a tool for historical navigation and speculative exploration.

Visualizing UI tool for graph construction and exploration with alternative action timelines

Techniques herein decouple available results, from graph analysis execution, to adapt to various deployment configurations. In an embodiment, a graph engine is deployed that has multiple mutually-exclusive configuration modes that include being embedded within a software application, centrally serving software applications, or distributed amongst a cluster of computers. Based on a current configuration mode of the graph engine, a software application receives or generates an analysis request to process a graph. The software application provides the analysis request to the graph engine in exchange for access to a computational future, of the graph engine, that is based on the analysis request and the graph. Based on a proxy of said computational future, the software application accesses a result of the analysis request. In an embodiment, a remote proxy exchanges representational state transfer (REST) messages. Network mechanisms, such as transport control protocol (TCP) and hypertext transfer protocol (HTTP), provide enhanced remoting.

Graph analytic engine that implements efficient transparent remote access over representational state transfer

Techniques and a system are provided for managing resources used by user-provided programs. The system includes an application programming interface (API) that allows user-provided programs to access memory resources managed by functions provided by the API. The system stores memory-usage records made during memory allocations. Memory-usage records may be used to identify memory resources, analyze memory usage, and provide other features.

In-memory data analytic system that provides an integrated tracking mechanism for explicit memory resources

Techniques for efficiently loading graph data into memory are provided. A plurality of node ID lists are retrieved from storage. Each node ID list is ordered based on one or more order criteria, such as node ID, and is read into memory. A new list of node IDs is created in memory and is initially empty. From among the plurality of node ID lists, a particular node ID is selected based on the one or more order criteria, removed from the node ID list where the particular node ID originates, and added to the new list. This process of selecting, removing, and adding continues until no more than one node ID list exists, other than the new list. In this way, the retrieval of the plurality of node ID lists from storage may be performed in parallel while the selecting and adding are performed sequentially.

Growing community of inventors

San Mateo, CA, United States of America

Korbinian Schmid