Tigergraph, Inc.

Graphsql, Inc.

Amobee, Inc.

Turn Inc.

Mingxi Wu

Zixuan Zhuang

Yu Xu

Dan Hu

Chengjie Qin

Yan Qi

Songting Chen

Xinyu Chang

Li Chen

Victor E Lee

Like Gao

Renchu Song

Yiming Pan

Systems, methods, and software described herein provide operations to generate and manage a graph. In one implementation, a graph management system may identify a request to generate a graph from multiple databases. In response to the request, the graph management system may identify attributes of interest in the databases and generate vertices that correspond to the attributes of interest. As the new vertices are generated, the graph management system may determine attributes that are related to the same entity, generate entity vertices that correspond to the entities identified in the databases, and generate edges that connect the attribute vertices to the corresponding entity vertices.

Graph creation from multiple databases

Systems, methods, and software described herein provide enhancements for managing permissions in a shared graph. In one implementation, a graph management system identifies a request to classify a first subgraph in the graph for access by a tenant of a plurality of tenants, wherein the request indicates one or more vertex types and/or one or more edge types for the first subgraph. The graph management system further identifies one or more vertices and/or one or more edges in the graph that qualify for the first subgraph based on the indicated one or more vertex types and/or one or more edge types, and allocates permissions to at least one user associated with the tenant to access the first subgraph.

Tenant based permission allocation for a graph database

A schema-free static query template improves schema-free graph query performance and runtime stability. A user can customize a generated graph query while detecting a query tampering state. Schema-free query templates, which have been compile-time type/syntax/semantic checked by a dummy schema, are installed. The templates are called as if they are functions with their selected graph schema vertex types and edge types. The invocation generates a static query which uses input metadata to instantiate a static query, and goes through type/syntax/semantic checks to capture any input errors at compile time. If there is no error found during compile time, a query binary is generated only containing the metadata related to the parameters and sent to a query engine for execution. If a user later invokes the same query with the same metadata, the compiler matches the generated previous query, bypasses compilation, and goes directly to the existing binary code.

Schema-free static query template

Systems, methods, and software to manage tags in association with vertices of a graph. In one implementation, a graph management system identifies a request to tag vertices in the graph, wherein the request indicates one or more attributes to qualify for the tag as part of a conditional expression. The graph management system further identifies vertices in the graph with the one or more attributes. Once identified, the graph management system assigns the tag to the vertices wherein the tag comprises an attribute assigned to the identified vertices.

Managing vertex level access in a graph via user defined tag rules

A system for querying a graph model and methods for making and using same. An initial vertex set can be received for one or more query blocks. The one or more query blocks can be executed to generate respective output vertex sets. The output vertex sets and the initial vertex set can be enabled to interconnect in a vertex-set-flow graph based on the query blocks. The vertex-set-flow graph can have a Directed Acyclic Graph shape. A selected query block can generate an output vertex set based on an input vertex set with or without traversal over an edge. A selected query block can calculate a runtime attribute. Edges and/or vertices of the graph model can be updated during querying. A selected block can call a graph query as a generic function. Functions for querying the graph model are powerful and can advantageously meet various graph query needs.

System and method for querying a graph model

A system for updating a target schema of a graph model and methods for making and using same. A schema change job can be run to update the target schema. Loaded source data in the graph model can be updated based on the updated target schema. The schema can be changed at vertex type level, edge type level and attribute level for a pre-defined target schema after source data has been loaded. The loaded source data can be updated to be consistent with the updated target schema without a need of being reloaded. The schema change job can be defined declaratively. The target schema can be versioned and stored to maintain a history of target schema update. Advantageously, schema update can be easy to implement. The time-consuming and expensive reloading process can be avoided. Target schema updating can have high flexibility and high performance.

System and method for updating target schema of graph model

A system for managing graph data and methods for making and using same. A loading plan can be generated according to a loading job. Source data can be loaded into the graph model based on the loading plan. The loading job can be defined declaratively. An interpreter can encode the loading job to generate the loading plan having a tree structure. The loading plan can instruct a loading engine to load the source data. The loading engine can be compiled independent of the loading plan. Advantageously, the loading engine can be compiled no more than once. The compiled loading engine can interpret any loading plan. The compiled loading engine can filter or transform the source data at runtime of the loading. Advantageously, time for compiling the loading engine and reading source data can be saved. Graph data loading can have high flexibility and high performance.

System and method for managing graph data

Techniques and mechanisms described herein facilitate the management of data centers. According to various embodiments, a first query dispatcher at a first data center may be disabled. The first data center may include a first data cluster and a first database. The first query dispatcher may be configured to dispatch queries to access data in the first data cluster and in a second data cluster at a second data center. The second data cluster may replicate data stored in the first data cluster. Metadata stored in the first database may be replicated to a second database in the second data center. The metadata may describe a state of the first data cluster. A second query dispatcher may be enabled at the second data center. The second query dispatcher may be configured to dispatch queries to access data in the second data cluster.

High availability and disaster recovery in large-scale data warehouse

A generic approach for automatically ingesting data into an HDFS (Hadoop File System) based data warehouse includes a datahub server, a generic pipelined data loading framework, and a meta-data model that, together, address data loading efficiency, data source heterogeneities, and data warehouse schema evolvement. The loading efficiency is achieved via the MapReduce scale-out solution. The meta-data model is comprised of configuration files and a catalog. The configuration file is setup per ingestion task. The catalog manages the data warehouse schema. When a scheduled data loading task is executed, the configuration files and the catalog collaboratively drive the datahub server to load the heterogeneous data to their destination schemas automatically.

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San Mateo, CA, United States of America

Mingxi Wu