图数据库HugeGraph源码解读 (1) —— 入门介绍

HugeGraph介绍

以下引自官方文档:

HugeGraph是一款易用、高效、通用的开源图数据库系统(Graph Database,GitHub项目地址), 实现了Apache TinkerPop3框架及完全兼容Gremlin查询语言, 具备完善的工具链组件,助力用户轻松构建基于图数据库之上的应用和产品。HugeGraph支持百亿以上的顶点和边快速导入,并提供毫秒级的关联关系查询能力(OLTP), 并可与Hadoop、Spark等大数据平台集成以进行离线分析(OLAP)。

HugeGraph典型应用场景包括深度关系探索、关联分析、路径搜索、特征抽取、数据聚类、社区检测、 知识图谱等,适用业务领域有如网络安全、电信诈骗、金融风控、广告推荐、社交网络和智能机器人等。

划重点:
- 基于TinkerPop3框架,兼容Gremlin查询语言
- OLTP(开源) 与 OLAP(商业版)
- 常用图应用支持—— 路径搜索、推荐等

架构介绍

架构图

HugeGraph包括三个层次的功能,分别是存储层、计算层和用户接口层。 HugeGraph支持OLTP和OLAP两种图计算类型

HugeGraph架构

组件

HugeGraph的主要功能分为HugeCore、ApiServer、HugeGraph-Client、HugeGraph-Loader和HugeGraph-Studio等组件构成,各组件之间的通信关系如下图所示。

组件

其中核心组件:

  • HugeCore :HugeGraph的核心模块,TinkerPop的接口主要在该模块中实现。
  • ApiServer :提供RESTFul Api接口,对外提供Graph Api、Schema Api和Gremlin Api等接口服务。
  • HugeGraph-Client:基于Java客户端驱动程序

生态组件:

  • HugeGraph-Loader:数据导入模块。HugeGraph-Loader可以扫描并分析现有数据,自动生成Graph Schema创建语言,通过批量方式快速导入数据。
  • HugeGraph-Studio:基于Web的可视化IDE环境。以Notebook方式记录Gremlin查询,可视化展示Graph的关联关系。HugeGraph-Studio也是本系统推荐的工具。

HugeGraph-Studio 看起来已经被抛弃了,研发团队正开发一个名为'hugegraph-hubble' 的新项目:

hugegraph-hubble is a graph management and analysis platform that provides features: graph data load, schema management, graph relationship analysis and graphical display.

根据官方的说明,hubble定义为图谱管理和分析平台,提供图谱数据加载、schema管理、图分析和可视化展示,目前正在研发中,预计2020年9月份会发布首个版本。

设计理念

常见的图数据表示模型有两种:

  • RDF(Resource Description Framework)模型: 学术界的选择,通过sparql来进行查询,jenagStore等等
  • 属性图(Property Graph)模型,工业界的选择,neo4jjanusgraph都是这种方案。

RDF是W3C标准,而Property Graph是工业标准,受到广大图数据库厂商的广泛支持。HugeGraph采用Property Graph,遵循工业标准。

HugeGraph存储概念模型详见下图:

HugeGraph概念模型

主要包含几个部分:

  • Vertex(顶点),对应一个实体(Entity)
  • Vertex Label(顶点的类型),对应一个概念(Concept)
  • 属性(图里的name、age),PropertyKey
  • Edge边(图里的lives),对应RDF里的Relation

可扩展性

HugeGraph提供了丰富的插件扩展机制,包含几个维度的扩展项:

  • 后端存储
  • 序列化器
  • 自定义配置项
  • 分词器

插件实现机制

  1. HugeGraph提供插件接口HugeGraphPlugin,通过Java SPI机制支持插件化
  2. HugeGraph提供了4个扩展项注册函数:registerOptions()registerBackend()registerSerializer()registerAnalyzer()
  3. 插件实现者实现相应的Options、Backend、Serializer或Analyzer的接口
  4. 插件实现者实现HugeGraphPlugin接口的register()方法,在该方法中注册上述第3点所列的具体实现类,并打成jar包
  5. 插件使用者将jar包放在HugeGraph Server安装目录的plugins目录下,修改相关配置项为插件自定义值,重启即可生效

从案例深入源码

想要深入的理解一个系统的源码,先从具体的应用入手。先查看example代码:

https://github.com/hugegraph/hugegraph/blob/master/hugegraph-example/src/main/java/com/baidu/hugegraph/example/Example1.java

 public static void main(String[] args) throws Exception {
        LOG.info("Example1 start!");

        HugeGraph graph = ExampleUtil.loadGraph();

        Example1.showFeatures(graph);

        Example1.loadSchema(graph);
        Example1.loadData(graph);
        Example1.testQuery(graph);
        Example1.testRemove(graph);
        Example1.testVariables(graph);
        Example1.testLeftIndexProcess(graph);

        Example1.thread(graph);

        graph.close();

        HugeFactory.shutdown(30L);
    }

1. loadGraph

要使用hugegraph,需要先初始化一个HugeGraph对象,而LoadGraph 正是做这个的。

loadGraph

public static HugeGraph loadGraph(boolean needClear, boolean needProfile) {
        if (needProfile) {
            profile();
        }

        registerPlugins();

        String conf = "hugegraph.properties";
        try {
            String path = ExampleUtil.class.getClassLoader()
                                     .getResource(conf).getPath();
            File file = new File(path);
            if (file.exists() && file.isFile()) {
                conf = path;
            }
        } catch (Exception ignored) {
        }

        HugeGraph graph = HugeFactory.open(conf);

        if (needClear) {
            graph.clearBackend();
        }
        graph.initBackend();

        return graph;
    }
1.1 registerPlugins

其中 registerPlugins 注册插件,注意上面介绍的扩展机制。hugegraph所有的后端存储都需要通过插件注册。

 public static void registerPlugins() {
        if (registered) {
            return;
        }
        registered = true;

        RegisterUtil.registerCassandra();
        RegisterUtil.registerScyllaDB();
        RegisterUtil.registerHBase();
        RegisterUtil.registerRocksDB();
        RegisterUtil.registerMysql();
        RegisterUtil.registerPalo();
    }

注册主要是register配置、序列化器和backend,比如下面是mysql的。

public static void registerMysql() {
        // Register config
        OptionSpace.register("mysql",
                "com.baidu.hugegraph.backend.store.mysql.MysqlOptions");
        // Register serializer
        SerializerFactory.register("mysql",
                "com.baidu.hugegraph.backend.store.mysql.MysqlSerializer");
        // Register backend
        BackendProviderFactory.register("mysql",
                "com.baidu.hugegraph.backend.store.mysql.MysqlStoreProvider");
    }
1.2 HugeFactory.open

HugeFactory 是Hugraph的工厂类,支持传入Configuraion配置信息,构建一个HugeGraph实例,注意这里为了线程安全,签名采用synchronized

 public static synchronized HugeGraph open(Configuration config) {
        HugeConfig conf = config instanceof HugeConfig ?
                          (HugeConfig) config : new HugeConfig(config);
        String name = conf.get(CoreOptions.STORE);
        checkGraphName(name, "graph config(like hugegraph.properties)");
        name = name.toLowerCase();
        HugeGraph graph = graphs.get(name);
        if (graph == null || graph.closed()) {
            graph = new StandardHugeGraph(conf);
            graphs.put(name, graph);
        } else {
            String backend = conf.get(CoreOptions.BACKEND);
            E.checkState(backend.equalsIgnoreCase(graph.backend()),
                         "Graph name '%s' has been used by backend '%s'",
                         name, graph.backend());
        }
        return graph;
    }

这里顺带提下配置文件,通过代码看到,默认是读取hugegraph.properties.

1.3 HugeGraph 对象

HugeGraph是一个interface,继承gremlin的Graph接口,定义了图谱的Schema定义、数据存储、查询等API方法。从上面1.2可以看到,默认的实现是StandardHugeGraph

public interface HugeGraph extends Graph {

    public HugeGraph hugegraph();

    public SchemaManager schema();

    public Id getNextId(HugeType type);

    public void addPropertyKey(PropertyKey key);
    public void removePropertyKey(Id key);
    public Collection<PropertyKey> propertyKeys();
    public PropertyKey propertyKey(String key);
    public PropertyKey propertyKey(Id key);
    public boolean existsPropertyKey(String key);

...
   

1.4 graph.clearBackend 与initBackend

clearBackend将后端数据清理,initBackend初始化基本的数据结构。

2. loadSchema

该方法,用来定义schema:

public static void loadSchema(final HugeGraph graph) {

        SchemaManager schema = graph.schema();

        // Schema changes will be commit directly into the back-end
        LOG.info("===============  propertyKey  ================");
        schema.propertyKey("id").asInt().create();
        schema.propertyKey("name").asText().create();
        schema.propertyKey("gender").asText().create();
        schema.propertyKey("instructions").asText().create();
        schema.propertyKey("category").asText().create();
        schema.propertyKey("year").asInt().create();
        schema.propertyKey("time").asText().create();
        schema.propertyKey("timestamp").asDate().create();
        schema.propertyKey("ISBN").asText().create();
        schema.propertyKey("calories").asInt().create();
        schema.propertyKey("amount").asText().create();
        schema.propertyKey("stars").asInt().create();
        schema.propertyKey("age").asInt().valueSingle().create();
        schema.propertyKey("comment").asText().valueSet().create();
        schema.propertyKey("contribution").asText().valueSet().create();
        schema.propertyKey("nickname").asText().valueList().create();
        schema.propertyKey("lived").asText().create();
        schema.propertyKey("country").asText().valueSet().create();
        schema.propertyKey("city").asText().create();
        schema.propertyKey("sensor_id").asUUID().create();
        schema.propertyKey("versions").asInt().valueList().create();

        LOG.info("===============  vertexLabel  ================");

        schema.vertexLabel("person")
              .properties("name", "age", "city")
              .primaryKeys("name")
              .create();
        schema.vertexLabel("author")
              .properties("id", "name", "age", "lived")
              .primaryKeys("id").create();
        schema.vertexLabel("language").properties("name", "versions")
              .primaryKeys("name").create();
        schema.vertexLabel("recipe").properties("name", "instructions")
              .primaryKeys("name").create();
        schema.vertexLabel("book").properties("name")
              .primaryKeys("name").create();
        schema.vertexLabel("reviewer").properties("name", "timestamp")
              .primaryKeys("name").create();

        // vertex label must have the properties that specified in primary key
        schema.vertexLabel("FridgeSensor").properties("city")
              .primaryKeys("city").create();

        LOG.info("===============  vertexLabel & index  ================");
        schema.indexLabel("personByCity")
              .onV("person").secondary().by("city").create();
        schema.indexLabel("personByAge")
              .onV("person").range().by("age").create();

        schema.indexLabel("authorByLived")
              .onV("author").search().by("lived").create();

        // schemaManager.getVertexLabel("author").index("byName").secondary().by("name").add();
        // schemaManager.getVertexLabel("recipe").index("byRecipe").materialized().by("name").add();
        // schemaManager.getVertexLabel("meal").index("byMeal").materialized().by("name").add();
        // schemaManager.getVertexLabel("ingredient").index("byIngredient").materialized().by("name").add();
        // schemaManager.getVertexLabel("reviewer").index("byReviewer").materialized().by("name").add();

        LOG.info("===============  edgeLabel  ================");

        schema.edgeLabel("authored").singleTime()
              .sourceLabel("author").targetLabel("book")
              .properties("contribution", "comment")
              .nullableKeys("comment")
              .create();

        schema.edgeLabel("write").multiTimes().properties("time")
              .sourceLabel("author").targetLabel("book")
              .sortKeys("time")
              .create();

        schema.edgeLabel("look").multiTimes().properties("timestamp")
              .sourceLabel("person").targetLabel("book")
              .sortKeys("timestamp")
              .create();

        schema.edgeLabel("created").singleTime()
              .sourceLabel("author").targetLabel("language")
              .create();

        schema.edgeLabel("rated")
              .sourceLabel("reviewer").targetLabel("recipe")
              .create();
    }

划重点:
- SchemaManager schema = graph.schema() 获取SchemaManager
- schema.propertyKey(NAME).asXXType().create() 创建属性
- schema.vertexLabel("person") // 定义概念
.properties("name", "age", "city") // 定义概念的属性
.primaryKeys("name") // 定义primary Keys,primary Key组合后可以唯一确定一个实体
.create();
- schema.indexLabel("personByCity").onV("person").secondary().by("city").create(); 定义索引
- schema.edgeLabel("authored").singleTime()
.sourceLabel("author").targetLabel("book")
.properties("contribution", "comment")
.nullableKeys("comment")
.create(); // 定义关系

3. loadData

创建实体,注意格式,K-V成对出现:

graph.addVertex(T.label, "book", "name", "java-3");

创建关系,Vertex的addEdge方法:

    Vertex james = tx.addVertex(T.label, "author", "id", 1,
                                "name", "James Gosling",  "age", 62,
                                "lived", "San Francisco Bay Area");

    Vertex java = tx.addVertex(T.label, "language", "name", "java",
                               "versions", Arrays.asList(6, 7, 8));
    Vertex book1 = tx.addVertex(T.label, "book", "name", "java-1");
    Vertex book2 = tx.addVertex(T.label, "book", "name", "java-2");
    Vertex book3 = tx.addVertex(T.label, "book", "name", "java-3");

    james.addEdge("created", java);
    james.addEdge("authored", book1,
                  "contribution", "1990-1-1",
                  "comment", "it's a good book",
                  "comment", "it's a good book",
                  "comment", "it's a good book too");
    james.addEdge("authored", book2, "contribution", "2017-4-28");

    james.addEdge("write", book2, "time", "2017-4-28");
    james.addEdge("write", book3, "time", "2016-1-1");
    james.addEdge("write", book3, "time", "2017-4-28");	

添加后,需要commit

4. testQuery 测试查询

查询主要通过GraphTraversal, 可以通过graph.traversal()获得:

public static void testQuery(final HugeGraph graph) {
        // query all
        GraphTraversal<Vertex, Vertex> vertices = graph.traversal().V();
        int size = vertices.toList().size();
        assert size == 12;
        System.out.println(">>>> query all vertices: size=" + size);

        // query by label
        vertices = graph.traversal().V().hasLabel("person");
        size = vertices.toList().size();
        assert size == 5;
        System.out.println(">>>> query all persons: size=" + size);

        // query vertex by primary-values
        vertices = graph.traversal().V().hasLabel("author").has("id", 1);
        List<Vertex> vertexList = vertices.toList();
        assert vertexList.size() == 1;
        System.out.println(">>>> query vertices by primary-values: " +
                           vertexList);

        VertexLabel author = graph.schema().getVertexLabel("author");
        String authorId = String.format("%s:%s", author.id().asString(), "11");

        // query vertex by id and query out edges
        vertices = graph.traversal().V(authorId);
        GraphTraversal<Vertex, Edge> edgesOfVertex = vertices.outE("created");
        List<Edge> edgeList = edgesOfVertex.toList();
        assert edgeList.size() == 1;
        System.out.println(">>>> query edges of vertex: " + edgeList);

        vertices = graph.traversal().V(authorId);
        vertexList = vertices.out("created").toList();
        assert vertexList.size() == 1;
        System.out.println(">>>> query vertices of vertex: " + vertexList);

        // query edge by sort-values
        vertices = graph.traversal().V(authorId);
        edgesOfVertex = vertices.outE("write").has("time", "2017-4-28");
        edgeList = edgesOfVertex.toList();
        assert edgeList.size() == 2;
        System.out.println(">>>> query edges of vertex by sort-values: " +
                           edgeList);

        // query vertex by condition (filter by property name)
        ConditionQuery q = new ConditionQuery(HugeType.VERTEX);
        PropertyKey age = graph.propertyKey("age");
        q.key(HugeKeys.PROPERTIES, age.id());
        if (graph.backendStoreFeatures()
                 .supportsQueryWithContainsKey()) {
            Iterator<Vertex> iter = graph.vertices(q);
            assert iter.hasNext();
            System.out.println(">>>> queryVertices(age): " + iter.hasNext());
            while (iter.hasNext()) {
                System.out.println(">>>> queryVertices(age): " + iter.next());
            }
        }

        // query all edges
        GraphTraversal<Edge, Edge> edges = graph.traversal().E().limit(2);
        size = edges.toList().size();
        assert size == 2;
        System.out.println(">>>> query all edges with limit 2: size=" + size);

        // query edge by id
        EdgeLabel authored = graph.edgeLabel("authored");
        VertexLabel book = graph.schema().getVertexLabel("book");
        String book1Id = String.format("%s:%s", book.id().asString(), "java-1");
        String book2Id = String.format("%s:%s", book.id().asString(), "java-2");

        String edgeId = String.format("S%s>%s>%s>S%s",
                                      authorId, authored.id(), "", book2Id);
        edges = graph.traversal().E(edgeId);
        edgeList = edges.toList();
        assert edgeList.size() == 1;
        System.out.println(">>>> query edge by id: " + edgeList);

        Edge edge = edgeList.get(0);
        edges = graph.traversal().E(edge.id());
        edgeList = edges.toList();
        assert edgeList.size() == 1;
        System.out.println(">>>> query edge by id: " + edgeList);

        // query edge by condition
        q = new ConditionQuery(HugeType.EDGE);
        q.eq(HugeKeys.OWNER_VERTEX, IdGenerator.of(authorId));
        q.eq(HugeKeys.DIRECTION, Directions.OUT);
        q.eq(HugeKeys.LABEL, authored.id());
        q.eq(HugeKeys.SORT_VALUES, "");
        q.eq(HugeKeys.OTHER_VERTEX, IdGenerator.of(book1Id));

        Iterator<Edge> edges2 = graph.edges(q);
        assert edges2.hasNext();
        System.out.println(">>>> queryEdges(id-condition): " +
                           edges2.hasNext());
        while (edges2.hasNext()) {
            System.out.println(">>>> queryEdges(id-condition): " +
                               edges2.next());
        }

        // NOTE: query edge by has-key just supported by Cassandra
        if (graph.backendStoreFeatures().supportsQueryWithContainsKey()) {
            PropertyKey contribution = graph.propertyKey("contribution");
            q.key(HugeKeys.PROPERTIES, contribution.id());
            Iterator<Edge> edges3 = graph.edges(q);
            assert edges3.hasNext();
            System.out.println(">>>> queryEdges(contribution): " +
                               edges3.hasNext());
            while (edges3.hasNext()) {
                System.out.println(">>>> queryEdges(contribution): " +
                                   edges3.next());
            }
        }

        // query by vertex label
        vertices = graph.traversal().V().hasLabel("book");
        size = vertices.toList().size();
        assert size == 5;
        System.out.println(">>>> query all books: size=" + size);

        // query by vertex label and key-name
        vertices = graph.traversal().V().hasLabel("person").has("age");
        size = vertices.toList().size();
        assert size == 5;
        System.out.println(">>>> query all persons with age: size=" + size);

        // query by vertex props
        vertices = graph.traversal().V().hasLabel("person")
                        .has("city", "Taipei");
        vertexList = vertices.toList();
        assert vertexList.size() == 1;
        System.out.println(">>>> query all persons in Taipei: " + vertexList);

        vertices = graph.traversal().V().hasLabel("person").has("age", 19);
        vertexList = vertices.toList();
        assert vertexList.size() == 1;
        System.out.println(">>>> query all persons age==19: " + vertexList);

        vertices = graph.traversal().V().hasLabel("person")
                        .has("age", P.lt(19));
        vertexList = vertices.toList();
        assert vertexList.size() == 1;
        assert vertexList.get(0).property("age").value().equals(3);
        System.out.println(">>>> query all persons age<19: " + vertexList);

        String addr = "Bay Area";
        vertices = graph.traversal().V().hasLabel("author")
                        .has("lived", Text.contains(addr));
        vertexList = vertices.toList();
        assert vertexList.size() == 1;
        System.out.println(String.format(">>>> query all authors lived %s: %s",
                           addr, vertexList));
    }

划重点

查询指定label的实体:
 vertices = graph.traversal().V().hasLabel("person");
 size = vertices.toList().size();
根据primary-values查询实体:
 vertices = graph.traversal().V().hasLabel("author").has("id", 1);
        List<Vertex> vertexList = vertices.toList();
查询edge:

查询所有edge:

GraphTraversal<Edge, Edge> edges = graph.traversal().E().limit(2);

根据ID查询edge:

	EdgeLabel authored = graph.edgeLabel("authored");
    VertexLabel book = graph.schema().getVertexLabel("book");
    String book1Id = String.format("%s:%s", book.id().asString(), "java-1");
    String book2Id = String.format("%s:%s", book.id().asString(), "java-2");

    String edgeId = String.format("S%s>%s>%s>S%s",
                                  authorId, authored.id(), "", book2Id);
    edges = graph.traversal().E(edgeId);

注意,edge的id由几个字段拼接起来的: "S%s>%s>%s>S%s",authorId, authored.id(), "", book2Id)

根据条件查询edge:

 q = new ConditionQuery(HugeType.EDGE);
        q.eq(HugeKeys.OWNER_VERTEX, IdGenerator.of(authorId));
        q.eq(HugeKeys.DIRECTION, Directions.OUT);
        q.eq(HugeKeys.LABEL, authored.id());
        q.eq(HugeKeys.SORT_VALUES, "");
        q.eq(HugeKeys.OTHER_VERTEX, IdGenerator.of(book1Id));

        Iterator<Edge> edges2 = graph.edges(q);
        assert edges2.hasNext();
        System.out.println(">>>> queryEdges(id-condition): " +
                           edges2.hasNext());
        while (edges2.hasNext()) {
            System.out.println(">>>> queryEdges(id-condition): " +
                               edges2.next());
        }

可以指定DIRECTION,

5. 删除

删除Vetex,调用vetex自带的remove方法

       // remove vertex (and its edges)
        List<Vertex> vertices = graph.traversal().V().hasLabel("person")
                                     .has("age", 19).toList();
        assert vertices.size() == 1;
        Vertex james = vertices.get(0);
        Vertex book6 = graph.addVertex(T.label, "book", "name", "java-6");
        james.addEdge("look", book6, "timestamp", "2017-5-2 12:00:08.0");
        james.addEdge("look", book6, "timestamp", "2017-5-3 12:00:08.0");
        graph.tx().commit();
        assert graph.traversal().V(book6.id()).bothE().hasNext();
        System.out.println(">>>> removing vertex: " + james);
        james.remove();
        graph.tx().commit();
        assert !graph.traversal().V(james.id()).hasNext();
        assert !graph.traversal().V(book6.id()).bothE().hasNext();

    

删除关系,也类似:

    // remove edge
        VertexLabel author = graph.schema().getVertexLabel("author");
        String authorId = String.format("%s:%s", author.id().asString(), "11");
        EdgeLabel authored = graph.edgeLabel("authored");
        VertexLabel book = graph.schema().getVertexLabel("book");
        String book2Id = String.format("%s:%s", book.id().asString(), "java-2");

        String edgeId = String.format("S%s>%s>%s>S%s",
                                      authorId, authored.id(), "", book2Id);

        List <Edge> edges = graph.traversal().E(edgeId).toList();
        assert edges.size() == 1;
        Edge edge = edges.get(0);
        System.out.println(">>>> removing edge: " + edge);
        edge.remove();
        graph.tx().commit();
        assert !graph.traversal().E(edgeId).hasNext();

小结

本文初步介绍了hugegraph设计理念、基本使用等。


作者:Jadepeng
出处:jqpeng的技术记事本--http://www.cnblogs.com/xiaoqi
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posted @ 2020-08-10 19:32  JadePeng  阅读(1029)  评论(0编辑  收藏  举报