TuGraph Quick Start

This document is intended for new users to get started quickly and contains an introduction, features, installation, and use of TuGraph.

1. Introduction

TuGraph is a large-scale graph computing system independently developed by Ant Group, providing graph database engine and graph analysis engine. Its main features are large data storage and computation, high throughput, and flexible API, while supporting efficient online transaction processing (OLTP) and online analytical processing (OLAP). LightGraph and GeaGraph are former names of TuGraph.

The main functional features include:

• Labeled property Graph Model
• Support multiple Graphs
• Full ACID transaction processing
• Built-in 25+ graph analysis algorithm
• Graph visualization tool based on Web client
• RESTful API and RPC are supported
• OpenCypher graph query language
• Stored procedure based on C++/Python/Java
• The Traversal API for efficient graph algorithm development

Performance and scalability features include:

• TB large capacity
• High throughput of ten million vertices per second
• High Availability Support (Enterprise Edition)
• High-performance Batch Import
• Online/offline backup

2. The installation

TuGraph can be installed quickly via Docker Image or locally via RPM /deb packages.

rpm/deb/docker images download address: https://www.tugraph.org/download

Fast experience through Docker

1. The Docker environment installed locally

   The docker official documentation：https://docs.docker.com/get-started/

2. Pull the docker images

   docker pull tugraph/tugraph-runtime-centos7
   

3. Start docker

   docker run -d -p 7070:7070 -p 9090:9090 --name tugraph_demo tugraph/tugraph-runtime-centos7 lgraph_server
   # 7070 is default http port，for web visiting。
   # 9090 is default RPC port，for RPC client visiting。
   
   

4. Open by browser

   http://x.x.x.x:7070

   Default account admin,Default password 73@TuGraph

5. Start

   docker run -dt -p 7070:7070 --name tugraph_demo tugraph/tugraph-runtime-centos7
   docker exec -it tugraph_demo bash
   # start the service
   lgraph_server -d start
   

Web operations

Create schema and import data

After login, click "Help", click "Quick Start", click "new schema", and click "import data" to complete the creation of Movie Graph.

Movie Graph：

Query demo

demo 1

Query all the actors in the movie 'Forrest Gump' and return a subgraph of the movie and the actors.

MATCH (m:movie {title: 'Forrest Gump'})<-[:acted_in]-(a:person) RETURN a, m

demo 2

Query all actors in the movie 'Forrest Gump' and list the roles the actors played in the movie.

MATCH (m:movie {title: 'Forrest Gump'})<-[r:acted_in]-(a:person) RETURN a.name,r.role

demo 3

Query all Michael's movies rated below 3.

MATCH (u:user {login: 'Michael'})-[r:rate]->(m:movie) WHERE r.stars < 3 RETURN m.title, r.stars

demo 4

Search for users who have the same hated movies as Michael, with the dislike criteria being less than three points.

MATCH (u:user {login: 'Michael'})-[r:rate]->(m:movie)<-[s:rate]-(v) WHERE r.stars < 3 AND s.stars < 3 RETURN u, m, v

demo5

To recommend movies to Michael, the method is to find out the users who hate the same movies as Michael, and then screen out the movies that these users like.

MATCH (u:user {login: 'Michael'})-[r:rate]->(m:movie)<-[s:rate]-(v)-[r2:rate]->(m2:movie) WHERE r.stars < 3 AND s.stars < 3 AND r2.stars > 3 RETURN u, m, v, m2

demo 6

Find out what Michael's friends like.

MATCH (u:user {login: 'Michael'})-[:is_friend]->(v:user)-[r:rate]->(m:movie) WHERE r.stars > 3 RETURN u, v, m

demo 7

Recommend similar movies to users who like 'Forrest Gump' by finding out which movies people who rate 'Forrest Gump' high also like.

MATCH (m:movie {title:'Forrest Gump'})<-[r:rate]-(u:user)-[r2:rate]->(m2:movie) WHERE r.stars>3 AND r2.stars>3 RETURN m, u,m2