图数据库之TinkerPop Provider

时间:2022-07-23
本文章向大家介绍图数据库之TinkerPop Provider,主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项,具有一定的参考价值,需要的朋友可以参考一下。

Apache TinkerPop 提供了图数据库的抽象接口,方便第三方实现自己的图数据库以接入TinkerPop 技术栈,享受TinkerPop 的Gremlin、算法等福利。TinkerPop将这些第三方称为“Provider ”,知名的Provider包含janusGraph、neo4j、hugegraph等。

Provider包含:

  • Graph System Provider
    • Graph Database Provider
    • Graph Processor Provider
  • Graph Driver Provider
  • Graph Language Provider
  • Graph Plugin Provider

Graph Structure API(图谱数据结构)

Graph最高层的抽象数据结构包含 Graph(图), Vertex(顶点), Edge(边), VertexProperty(属性) and Property.

基于这些基础数据结构,就可以对进行基本的图谱操作。

Graph graph = TinkerGraph.open(); //1
Vertex marko = graph.addVertex(T.label, "person", T.id, 1, "name", "marko", "age", 29); //2
Vertex vadas = graph.addVertex(T.label, "person", T.id, 2, "name", "vadas", "age", 27);
Vertex lop = graph.addVertex(T.label, "software", T.id, 3, "name", "lop", "lang", "java");
Vertex josh = graph.addVertex(T.label, "person", T.id, 4, "name", "josh", "age", 32);
Vertex ripple = graph.addVertex(T.label, "software", T.id, 5, "name", "ripple", "lang", "java");
Vertex peter = graph.addVertex(T.label, "person", T.id, 6, "name", "peter", "age", 35);
marko.addEdge("knows", vadas, T.id, 7, "weight", 0.5f); //3
marko.addEdge("knows", josh, T.id, 8, "weight", 1.0f);
marko.addEdge("created", lop, T.id, 9, "weight", 0.4f);
josh.addEdge("created", ripple, T.id, 10, "weight", 1.0f);
josh.addEdge("created", lop, T.id, 11, "weight", 0.4f);
peter.addEdge("created", lop, T.id, 12, "weight", 0.2f);
  1. 创建一个基于内存存储的TinkerGraph 实例(TinkerGraph是官方实现的,基于内存的Graph)

2 .创建一个顶点

  1. 创建边

上面的代码构建了一个基本的图,下面的代码演示如何进行图谱的操作。

实现 Gremlin-Core

一个标准的Graph Provider需要实现OLTP 和OLAP两类接口,官方推荐学习TinkerGraph(in-memory OLTP and OLAP in tinkergraph-gremlin),以及 Neo4jGraph (OLTP w/ transactions in neo4j-gremlin) ,还有 Neo4jGraph (OLTP w/ transactions in neo4j-gremlin) ,还有 HadoopGraph (OLAP in hadoop-gremlin) 。

  1. 在线事务处理 Graph Systems (OLTP)
1.  数据结构 API: `Graph`, `Element`, `Vertex`, `Edge`, `Property` and `Transaction` (if transactions are supported).

2.  处理API : `TraversalStrategy` instances for optimizing Gremlin traversals to the provider’s graph system (i.e. `TinkerGraphStepStrategy`).
  1. 在线分析 图系统 (OLAP)
    1. Everything required of OLTP is required of OLAP (but not vice versa).
    2. GraphComputer API: GraphComputer, Messenger, Memory.

OLTP 实现

需要实现structure包下的interface,包含Graph, Vertex, Edge, Property, Transaction等等。

  • Graph实现时,需要命名为XXXGraph (举例: TinkerGraph, Neo4jGraph, HadoopGraph, etc.).
    • 需要兼容GraphFactory ,也就是提供一个静态的 Graph open(Configuration) 方法。

OLAP 实现

需要实现:

  1. GraphComputer: 图计算器,提供隔离环境,执行VertexProgram,和MapReduce任务.
  2. Memory: A global blackboard for ANDing, ORing, INCRing, and SETing values for specified keys.
  3. Messenger: The system that collects and distributes messages being propagated by vertices executing the VertexProgram application.
  4. MapReduce.MapEmitter: The system that collects key/value pairs being emitted by the MapReduce applications map-phase.
  5. MapReduce.ReduceEmitter: The system that collects key/value pairs being emitted by the MapReduce applications combine- and reduce-phases.

作者:Jadepeng 出处:jqpeng的技术记事本--http://www.cnblogs.com/xiaoqi 您的支持是对博主最大的鼓励,感谢您的认真阅读。 本文版权归作者所有,欢迎转载,但未经作者同意必须保留此段声明,且在文章页面明显位置给出原文连接,否则保留追究法律责任的权利。