neo4j apoc 系列

Neo4j APOC-01-图数据库 apoc 插件介绍

Neo4j GDS-01-graph-data-science 图数据科学插件库概览

Neo4j GDS-02-graph-data-science 插件库安装实战笔记

Neo4j GDS-03-graph-data-science 简单聊一聊图数据科学插件库

Neo4j GDS-04-图的中心性分析介绍

Neo4j GDS-05-neo4j中的中心性分析算法

Neo4j GDS-06-neo4j GDS 库中社区检测算法介绍

Neo4j GDS-07-neo4j GDS 库中社区检测算法实现

Neo4j GDS-08-neo4j GDS 库中路径搜索算法介绍

Neo4j GDS-09-neo4j GDS 库中路径搜索算法实现

Neo4j GDS-10-neo4j GDS 库中相似度算法介绍

Neo4j GDS-11-neo4j GDS 库中相似度算法实现

Neo4j GDS-12-neo4j GDS 库中节点插入(Node Embedding)算法介绍

Neo4j GDS-13-neo4j GDS 库中节点插入算法实现

Neo4j GDS-14-neo4j GDS 库中链接预测算法介绍

Neo4j GDS-15-neo4j GDS 库中链接预测算法实现

Neo4j GDS-16-neo4j GDS 库创建 graph 图投影

Neo4j GDS-17-neo4j GDS 库创建 graph 图投影更复杂的场景

实际测试

数据初始化

i_app 节点,i_app 指向 i_vm,i_phy。

i_app 有 name 属性。

i_vm, i_phy 有 ip 属性。

创建 4 个 i_app 节点,分别指向 4 个 i_vm。2个 i_vm 一组,分别指向 i_phy 节点。

给出 cypher 初始化语句

CREATE (phy1:i_phy {ip: '192.168.1.1'}),
       (phy2:i_phy {ip: '192.168.1.2'}),

(vm1:i_vm {ip: '10.0.0.1'})-[:BELONGS_TO]->(phy1),
(vm2:i_vm {ip: '10.0.0.2'})-[:BELONGS_TO]->(phy1),
(vm3:i_vm {ip: '10.0.0.3'})-[:BELONGS_TO]->(phy2),
(vm4:i_vm {ip: '10.0.0.4'})-[:BELONGS_TO]->(phy2),

(app1:i_app {name: 'app1'})-[:POINTS_TO]->(vm1),
(app2:i_app {name: 'app2'})-[:POINTS_TO]->(vm2),
(app3:i_app {name: 'app3'})-[:POINTS_TO]->(vm3),
(app4:i_app {name: 'app4'})-[:POINTS_TO]->(vm4);

目标

希望达到的效果:

neo4j 中。如何实现一个从 i_app 节点,且 i_app 的 AppName 属性 在指定的列表中,比如 ['app1'],从 i_app 作为 start,i_vm 或者 i_phy 作为终止节点,最大2层。

任意关系。对应的图投影语句怎么写?我最终的目的是创建投影之后,通过 pageRank 找到关联节点比较多的元素返回。

所以你帮我拆分为3步:

1. apoc 找到符合条件的节点+关系 

2. gds 创建图投影

3. gds pageRank 找到score 比较高的元素 Top9 返回。 

先给我拆分具体的每一步的 cypher 语句,然后给出 java 实现.

实现代码

第一个版本

package org.example;

import org.neo4j.driver.*;

import java.util.Arrays;
import java.util.List;

public class PageRankService implements AutoCloseable {
    private final Driver driver;

    // 初始化Driver(建议使用连接池)
    public PageRankService(String uri, String user, String password) {
        driver = GraphDatabase.driver(uri, AuthTokens.basic(user, password));
    }

    // 主业务方法(appNames为动态参数)
    public List<Record> calculateTopPageRankNodes(List<String> appNames) {
        try (Session session = driver.session()) {
            // 步骤1:创建Cypher投影
            createGraphProjection(session, appNames);

            // 步骤2:执行PageRank算法
            return session.writeTransaction(tx -> {
                Result result = tx.run(
                        "CALL gds.pageRank.stream('myGraph') "
                                + "YIELD nodeId, score "
                                + "RETURN gds.util.asNode(nodeId) AS node, score "
                                + "ORDER BY score DESC LIMIT 9");


                // 删除
                tx.run("CALL gds.graph.drop('myGraph')");

                return result.list();
            });
        }
    }

    // 创建动态图投影(私有方法封装)
    private void createGraphProjection(Session session, List<String> appNames) {
        String nodeQuery =
                "MATCH (start:i_app) WHERE start.name IN $appNames " +
                        "CALL apoc.path.expandConfig(start, { " +
                        "    relationshipFilter: '>', " +
                        "    labelFilter: '/i_phy', " +
                        "    minLevel: 1, " +
                        "    maxLevel: 2, " +
                        "    uniqueness: 'NODE_GLOBAL' " +
                        "}) YIELD path " +
                        "UNWIND nodes(path) AS node " +
                        "RETURN DISTINCT id(node) AS id, labels(node) AS labels";

        String relQuery =
                "MATCH (start:i_app) WHERE start.name IN $appNames " +
                        "CALL apoc.path.expandConfig(start, { " +
                        "    relationshipFilter: '>', " +
                        "    labelFilter: '/i_phy', " +
                        "    minLevel: 1, " +
                        "    maxLevel: 2, " +
                        "    uniqueness: 'NODE_GLOBAL' " +
                        "}) YIELD path " +
                        "UNWIND relationships(path) AS rel " +
                        "RETURN DISTINCT id(startNode(rel)) AS source, " +
                        "id(endNode(rel)) AS target, type(rel) AS type";

        // 执行Cypher投影(带参数)
        session.run(
                "CALL gds.graph.create.cypher($graphName, $nodeQuery, $relQuery, {parameters: {appNames: $appNames}})",
                Values.parameters(
                        "graphName", "myGraph",
                        "nodeQuery", nodeQuery,
                        "relQuery", relQuery,
                        "appNames", appNames
                )
        ).consume(); // 显式消费结果确保执行
    }

    @Override
    public void close() throws Exception {
        driver.close();
    }

    // 示例用法
    public static void main(String[] args) {
        try (PageRankService service = new PageRankService("bolt://[::1]:17687", "neo4j", "12345678")) {
            List<String> appNameList = Arrays.asList("app1", "app2", "app3", "app4");
            List<Record> topNodes = service.calculateTopPageRankNodes(appNameList);
            topNodes.forEach(record ->
                    System.out.println(record.get("node").asNode() + " Score: " + record.get("score")));
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
    }
}

执行异常

Exception in thread "main" java.lang.RuntimeException: org.neo4j.driver.exceptions.ClientException: There is no procedure with the name `gds.graph.create.cypher` registered for this database instance. Please ensure you've spelled the procedure name correctly and that the procedure is properly deployed.
	at org.example.PageRankService.main(PageRankService.java:93)
Caused by: org.neo4j.driver.exceptions.ClientException: There is no procedure with the name `gds.graph.create.cypher` registered for this database instance. Please ensure you've spelled the procedure name correctly and that the procedure is properly deployed.
	at org.neo4j.driver.internal.util.Futures.blockingGet(Futures.java:111)
	at org.neo4j.driver.internal.InternalSession.run(InternalSession.java:62)
	at org.neo4j.driver.internal.InternalSession.run(InternalSession.java:47)
	at org.neo4j.driver.internal.AbstractQueryRunner.run(AbstractQueryRunner.java:34)
	at org.example.PageRankService.createGraphProjection(PageRankService.java:69)
	at org.example.PageRankService.calculateTopPageRankNodes(PageRankService.java:22)
	at org.example.PageRankService.main(PageRankService.java:89)
	Suppressed: org.neo4j.driver.internal.util.ErrorUtil$InternalExceptionCause

修正

首先确认当前 GDS 的方法到底是什么?

这个应该是版本问题,这个版本中的 2.5.3 方法是 gds.graph.project.cypher

我当时测试另一个版本 1.8.8 确实是 gds.graph.create.cypher

PS: 当初自己怎么安装了一个这么老的版本。

CALL gds.list()
YIELD name, description
WHERE name contains 'graph'
RETURN name, description

结果:

╒═══════════════════════════════════════════════╤══════════════════════════════════════════════════════════════════════╕
│name                                           │description                                                           │
╞═══════════════════════════════════════════════╪══════════════════════════════════════════════════════════════════════╡
...
├───────────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│"gds.graph.project"                            │"Creates a named graph in the catalog for use by algorithms."         │
├───────────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│"gds.graph.project.cypher"                     │"Creates a named graph in the catalog for use by algorithms."         │
├───────────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────┤
...
└───────────────────────────────────────────────┴──────────────────────────────────────────────────────────────────────┘

参数

CALL gds.list()
YIELD name, description, signature
WHERE name contains 'gds.graph.project.cypher'
RETURN name, description, signature

如下:

╒═══════════════════════════════════╤═════════════════════════════════════════════════════════════════════╤══════════════════════════════════════════════════════════════════════╕
│name                               │description                                                          │signature                                                             │
╞═══════════════════════════════════╪═════════════════════════════════════════════════════════════════════╪══════════════════════════════════════════════════════════════════════╡
│"gds.graph.project.cypher"         │"Creates a named graph in the catalog for use by algorithms."        │"gds.graph.project.cypher(graphName :: STRING?, nodeQuery :: STRING?, │
│                                   │                                                                     │relationshipQuery :: STRING?, configuration = {} :: MAP?) :: (nodeQuer│
│                                   │                                                                     │y :: STRING?, relationshipQuery :: STRING?, graphName :: STRING?, node│
│                                   │                                                                     │Count :: INTEGER?, relationshipCount :: INTEGER?, projectMillis :: INT│
│                                   │                                                                     │EGER?)"                                                               │
├───────────────────────────────────┼─────────────────────────────────────────────────────────────────────┼──────────────────────────────────────────────────────────────

API 为:

CALL gds.graph.project.cypher(
    graphName: String, 
    nodeQuery: String, 
    relationshipQuery: String, 
    configuration: Map
)

再次执行

修正为当前版本的方法名。

session.run(
                "CALL gds.graph.project.cypher($graphName, $nodeQuery, $relQuery, {parameters: {appNames: $appNames}})",
                Values.parameters(
                        "graphName", "myGraph",
                        "nodeQuery", nodeQuery,
                        "relQuery", relQuery,
                        "appNames", appNames
                )
        ).consume(); // 显式消费结果确保执行

结果如下:

node<0> Score: 0.62175
node<1> Score: 0.62175
node<4> Score: 0.2775
node<3> Score: 0.2775
node<5> Score: 0.2775
node<2> Score: 0.2775
node<8> Score: 0.15000000000000002
node<9> Score: 0.15000000000000002
node<7> Score: 0.15000000000000002

发现如果匹配的数据为空,实际上会报错如下:

Exception in thread "main" java.lang.RuntimeException: org.neo4j.driver.exceptions.ClientException: Failed to invoke procedure `gds.graph.project.cypher`: Caused by: java.lang.IllegalArgumentException: Node-Query returned no nodes
	at org.example.PageRankService.main(PageRankService.java:91)
Caused by: org.neo4j.driver.exceptions.ClientException: Failed to invoke procedure `gds.graph.project.cypher`: Caused by: java.lang.IllegalArgumentException: Node-Query returned no nodes
	at org.neo4j.driver.internal.util.Futures.blockingGet(Futures.java:111)
	at org.neo4j.driver.internal.InternalResult.blockingGet(InternalResult.java:107)
	at org.neo4j.driver.internal.InternalResult.consume(InternalResult.java:98)
	at org.example.PageRankService.createGraphProjection(PageRankService.java:75)
	at org.example.PageRankService.calculateTopPageRankNodes(PageRankService.java:20)
	at org.example.PageRankService.main(PageRankService.java:87)

节点本身的基本信息

NodeInfo 基本信息:

public class NodeInfo {

    private Long nodeId;
    private List<String> labels;
    private Map<String, Object> properties;

    //get/set/toString

}

核心方法:

public List<NodeInfo> getNodeInfo(List<Long> nodeIds) {
    Session session = driver.session();
    String cypherQuery =
            "MATCH (n) " +
                    "WHERE id(n) IN $nodeIds " +
                    "RETURN id(n) AS nodeId, labels(n) AS labels, properties(n) AS properties";
    List<NodeInfo> nodeInfoList = new ArrayList<>();
    session.readTransaction(tx -> {
        Result result = tx.run(cypherQuery, Values.parameters("nodeIds", nodeIds));
        while (result.hasNext()) {
            Record record = result.next();
            NodeInfo info = new NodeInfo();
            info.setNodeId(record.get("nodeId").asLong());
            info.setLabels(record.get("labels").asList(Value::asString));
            info.setProperties(record.get("properties").asMap(Value::asObject));
            nodeInfoList.add(info);
        }
        return null;
    });
    return nodeInfoList;
}

测试效果:

public static void main(String[] args) {
        try (PageRankService_V2_5_3 service = new PageRankService_V2_5_3("bolt://[::1]:17687", "neo4j", "12345678")) {
            List<String> appNameList = Arrays.asList("app1", "app2", "app3", "app4");
            List<Record> topNodes = service.calculateTopPageRankNodes(appNameList);
            topNodes.forEach(record ->
                    System.out.println(record.get("node").asNode() + " Score: " + record.get("score")));

            // 结果处理
            List<Long> nodeIdList = topNodes.stream().map(new Function<Record, Long>() {
                @Override
                public Long apply(Record record) {
                    return record.get("node").asNode().id();
                }
            }).collect(Collectors.toList());

            List<NodeInfo> nodeInfoList = service.getNodeInfo(nodeIdList);
            nodeInfoList.forEach(System.out::println);
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
    }

输出如下:

NodeInfo{nodeId=0, labels=[i_phy], properties={ip=192.168.1.1}}
NodeInfo{nodeId=1, labels=[i_phy], properties={ip=192.168.1.2}}
NodeInfo{nodeId=2, labels=[i_vm], properties={ip=10.0.0.1}}
NodeInfo{nodeId=3, labels=[i_vm], properties={ip=10.0.0.2}}
NodeInfo{nodeId=4, labels=[i_vm], properties={ip=10.0.0.3}}
NodeInfo{nodeId=5, labels=[i_vm], properties={ip=10.0.0.4}}
NodeInfo{nodeId=7, labels=[i_app], properties={name=app2}}
NodeInfo{nodeId=8, labels=[i_app], properties={name=app3}}
NodeInfo{nodeId=9, labels=[i_app], properties={name=app4}}

查询对应的数据

我们可以直接查询对应的数据效果

MATCH (start:i_app) WHERE start.name IN ['app1', 'app2', 'app3', 'app4']
CALL apoc.path.expandConfig
(start, 
{     
    relationshipFilter: '>',     
    labelFilter: '/i_phy',     
    minLevel: 1,     
    maxLevel: 2,     
    uniqueness: 'NODE_GLOBAL' 
}
) 
YIELD path 
with path, nodes(path) as all_nodes
UNWIND all_nodes as node
with path, all_nodes, node
where id(node) in [0]
with path, all_nodes
return distinct relationships(path) as all_relationships, all_nodes

效果如下:

Neo4j Graph VisualizationCreated using Neo4j (http://www.neo4j.com/)POINTS_TOBELONGS_TOPOINTS_TOBELONGS_TO app1 10.0.0.1 192.168.… app2 10.0.0.2

一些技巧

有些保证图的删除。

如果每次图都不同,又保证不同的图不冲突,可以生成唯一标识,生成后删除。

或者改为定时删除。


chat

目标场景

希望达到的效果:

如何实现一个从 i_app 节点,且 i_app 的 AppName 属性 在指定的列表中,比如 ['my-web'],从 i_app 作为 start,i_vm 或者 i_phy 作为终止节点,最大2层。任意关系。

对应的图投影语句怎么写?结合 apoc 的方式来实现

实现

分步实现说明

步骤1:使用APOC找到符合条件的节点和关系

通过apoc.path.expandConfig动态获取起始节点为i_app(AppName在指定列表),终止节点为i_vmi_phy,最大深度为2的路径。此步骤直接嵌入到后续的图投影中,无需单独存储中间结果。

// 步骤1和步骤2合并:动态获取子图并创建投影
CALL gds.graph.project.cypher(
    'myGraph',
    // 节点查询:收集所有路径中的节点(包括i_app、中间节点、i_vm/i_phy)
    'MATCH (start:i_app) WHERE start.name IN $appNames
     CALL apoc.path.expandConfig(start, {
         relationshipFilter: ">",       // 允许所有关系类型和方向
         labelFilter: ">i_vm|i_phy",   // 终止节点必须是i_vm或i_phy
         minLevel: 1,                  // 最小深度1(避免仅包含i_app)
         maxLevel: 2,                  // 最大深度2
         uniqueness: "NODE_GLOBAL"     // 节点全局唯一,避免重复遍历
     }) YIELD path
     UNWIND nodes(path) AS node
     RETURN DISTINCT id(node) AS id, labels(node) AS labels',
    // 关系查询:收集所有路径中的关系
    'MATCH (start:i_app) WHERE start.name IN $appNames
     CALL apoc.path.expandConfig(start, {
         relationshipFilter: ">",
         labelFilter: ">i_vm|i_phy",
         minLevel: 1,
         maxLevel: 2,
         uniqueness: "NODE_GLOBAL"
     }) YIELD path
     UNWIND relationships(path) AS rel
     RETURN DISTINCT id(startNode(rel)) AS source, id(endNode(rel)) AS target, type(rel) AS type',
    { parameters: { appNames: ['my-web'] } }  // 指定应用名列表
)
YIELD graphName, nodeCount, relationshipCount;  // 返回投影信息

关键点解析

  • 路径过滤逻辑:通过labelFilter: ">i_vm|i_phy"确保路径末端节点为目标类型,minLevel:1排除仅包含起始节点的情况。
  • 性能优化uniqueness: "NODE_GLOBAL"减少重复遍历。

步骤2:创建GDS图投影

使用Cypher投影方式动态构建子图,仅包含步骤1筛选出的节点和关系。此方法无需预先定义标签或关系类型,适合动态场景。

// (已合并到步骤1的代码中)

参数说明

  • 节点投影:返回所有路径节点的ID及标签。
  • 关系投影:返回所有路径关系的起点、终点及类型。
  • 动态性优势:无需预先知道涉及的标签或关系类型,直接通过路径扩展动态捕获。

步骤3:运行PageRank算法并返回Top9结果

对投影后的图执行PageRank算法,按得分降序取前9名节点。

// 步骤3:执行PageRank并返回高影响力节点
CALL gds.pageRank.stream('myGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId) AS node, score  // 将节点ID转换为实际节点对象
ORDER BY score DESC
LIMIT 9;

算法配置

  • 默认参数:阻尼系数为0.85,迭代次数为20(未显式指定时使用默认值)。
  • 结果解释:PageRank分数反映节点在图中的连接重要性,高分节点通常是关键枢纽。

完整代码整合

// 步骤1+2:动态创建子图投影
CALL gds.graph.project.cypher(
    'myGraph',
    'MATCH (start:i_app) WHERE start.name IN $appNames
     CALL apoc.path.expandConfig(start, {
         relationshipFilter: ">",
         labelFilter: ">i_vm|i_phy",
         minLevel: 1,
         maxLevel: 2,
         uniqueness: "NODE_GLOBAL"
     }) YIELD path
     UNWIND nodes(path) AS node
     RETURN DISTINCT id(node) AS id, labels(node) AS labels',
    'MATCH (start:i_app) WHERE start.name IN $appNames
     CALL apoc.path.expandConfig(start, {
         relationshipFilter: ">",
         labelFilter: ">i_vm|i_phy",
         minLevel: 1,
         maxLevel: 2,
         uniqueness: "NODE_GLOBAL"
     }) YIELD path
     UNWIND relationships(path) AS rel
     RETURN DISTINCT id(startNode(rel)) AS source, id(endNode(rel)) AS target, type(rel) AS type',
    { parameters: { appNames: ['app1'] } }
)
YIELD graphName, nodeCount, relationshipCount;

// 步骤3:PageRank计算
CALL gds.pageRank.stream('myGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId) AS node, score
ORDER BY score DESC
LIMIT 9;

常见问题及优化建议

  1. 性能问题
    • 路径深度限制:最大深度设为2层,避免大规模图遍历。
    • 索引优化:为i_app节点的AppName属性创建索引,加速起始节点查询。
      CREATE INDEX FOR (n:i_app) ON (n.name);
      
  2. 结果准确性
    • 终止节点验证:通过labelFilter确保终止节点类型正确,避免无关节点混入。
    • 权重调整:若需考虑关系权重,可在关系查询中返回weight属性,并在PageRank配置中添加relationshipWeightProperty参数。
  3. 扩展性
    • 动态参数:将appNames设为变量,便于灵活调整目标应用。
    • 多算法对比:可尝试其他中心性算法(如Betweenness)验证结果一致性。

graph.cypher 参数说明

全面解析 gds.graph.project.cypher 方法的参数及核心逻辑

gds.graph.project.cypher 是 Neo4j Graph Data Science(GDS)库中用于动态构建内存图投影的核心方法,其参数设计直接决定了图的筛选逻辑、属性和性能表现。以下从参数结构、使用规范到实践策略进行深度剖析:


一、参数构成与语法规范

参数名 是否必选 数据类型 描述 资料引用
graphName String 内存图投影的唯一标识名,用于后续算法调用  
nodeQuery String 定义节点筛选逻辑的Cypher查询,需返回 id 列(节点ID)和可选的 labels(标签)列  
relationshipQuery String 定义关系筛选逻辑的Cypher查询,需返回 source(起点)、target(终点)列  
configuration Map 控制内存分配、并发等高级参数(如 readConcurrencyparameters 动态传参)  

语法原型

CALL gds.graph.project.cypher(
    graphName: String,
    nodeQuery: String,
    relationshipQuery: String,
    configuration: Map
) YIELD graphName, nodeCount, relationshipCount

二、核心参数详解

1. nodeQuery(节点查询)
  • 必选字段id(节点ID)
  • 可选字段labels(节点标签)、自定义属性(如 n.age AS age
  • 作用:通过Cypher查询动态筛选参与图计算的节点集合
  • 示例
    // 筛选年龄小于30且标签为Person的节点,携带属性
    'MATCH (n:Person) WHERE n.age < 30 RETURN id(n) AS id, labels(n) AS labels, n.age AS age'
    
  • 特殊场景
    • 虚拟节点:通过 UNWIND 生成虚拟节点(如 RETURN 0 AS id
    • 动态传参:通过 $param 语法注入外部参数(如 WHERE n.name = $name
2. relationshipQuery(关系查询)
  • 必选字段source(起点ID)、target(终点ID)
  • 可选字段type(关系类型)、自定义属性(如 r.weight AS weight
  • 作用:定义节点间的连接关系及属性,支持聚合逻辑(如 COUNT(*) AS count
  • 示例
    // 筛选KNOWS类型关系,聚合重复边并计算权重总和
    'MATCH (a)-[r:KNOWS]->(b) 
     RETURN id(a) AS source, id(b) AS target, 
            SUM(r.weight) AS totalWeight, 
            "KNOWS" AS type'
    
  • 高级特性
    • 关系去重:使用 DISTINCTCOUNT() 处理平行边
    • 方向控制:通过 [:REL_TYPE] 或反向查询实现关系方向调整
3. configuration(配置参数)
  • 关键子参数

    参数名 作用 默认值
    parameters 向查询中注入动态参数(如列表过滤) {}
    readConcurrency 控制并发线程数(影响内存占用和性能) 4
    validateRelationships 验证关系两端节点是否存在于节点集合中(避免孤立边) true
  • 示例

    { 
        parameters: { appNames: ["my-web"] },
        readConcurrency: 8,
        validateRelationships: false
    }
    

三、返回值与性能优化

YIELD 字段

  • graphName:投影图的名称(与输入参数一致)
  • nodeCount:加载的节点总数
  • relationshipCount:加载的关系总数

优化策略

  1. 索引加速:为 nodeQuery 中的筛选条件(如 AppName)创建索引
  2. 内存控制:使用 gds.graph.create.cypher.estimate 预估算内存消耗(避免OOM)
  3. 动态参数化:通过 parameters 配置复用查询模板,提升灵活性
  4. 批量写入:在事务外执行投影操作,减少内存锁竞争

四、典型错误与规避方案

  1. 空图错误
    • 现象Failed to load a relationship because...
    • 根因nodeQuery 未覆盖关系的端点节点
    • 解决:启用 validateRelationships: false 或调整节点查询逻辑
  2. 版本兼容性
    • Neo4j 4.1.1 异常:升级到最新补丁版本
    • Legacy Cypher 迁移:使用新语法 gds.graph.cypher.project
  3. 属性缺失
    • 处理技巧:使用 coalesce(r.weight, 1.0) 设置默认值

五、应用场景对比

场景特征 适用方法 优势 局限
动态子图筛选 Cypher投影 灵活定义节点/关系逻辑 内存消耗较高
全图或固定模式 原生投影(Native) 加载速度快,适合生产环境 灵活性低
复杂属性处理 混合投影(Cypher+Native) 结合两种优势(需GDS 2.0+) 配置复杂度高

六、完整示例

// 动态创建包含过滤条件的图投影
CALL gds.graph.project.cypher(
    'filteredGraph',
    // 节点查询:筛选AppName在列表中的i_app节点及其关联节点
    'MATCH (n:i_app) WHERE n.name IN $apps
     CALL apoc.path.expandConfig(n, {maxLevel:2}) 
     UNWIND nodes(path) AS node 
     RETURN DISTINCT id(node) AS id, labels(node) AS labels',
    // 关系查询:捕获所有关联关系(含方向)
    'MATCH (a)-[r]->(b) 
     WHERE a.name IN $apps AND b.name IN $apps 
     RETURN id(a) AS source, id(b) AS target, type(r) AS type',
    // 配置参数:动态传入应用名列表
    { parameters: { apps: ['my-web'] } }
)
YIELD nodeCount, relationshipCount;

// 执行PageRank算法
CALL gds.pageRank.stream('filteredGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).id AS nodeId, score
ORDER BY score DESC LIMIT 10;

设计要点

  • 使用 apoc.path.expandConfig 实现深度控制
  • 通过 DISTINCT 避免重复节点
  • 参数化提升查询复用性

将刚才符合我要求 i_app 开始,pageRank 排序的 cypher 代码,调整为 neo4j driver java 代码实现方法,其中 appNames 作为方法的参数。

基于 Java Driver 的 PageRank 动态图计算实现方案

一、需求解读

需将原有 Cypher 查询转换为 Java 代码,实现以下功能:

  1. 动态参数传递appNames 作为方法参数传入
  2. 内存图投影:通过 Cypher 动态构建子图(i_app 起始,最大 2 层)
  3. 算法执行:运行 PageRank 并返回 Top9 结果
  4. 资源管理:符合 Driver API 规范(连接池、事务、异常处理)

二、核心实现逻辑分解

flowchart TD
    A[初始化Driver] --> B[创建内存图投影]
    B --> C[执行PageRank算法]
    C --> D[提取Top9结果]
    D --> E[资源释放]

三、完整 Java 实现代码

import org.neo4j.driver.*;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class PageRankService implements AutoCloseable {
    private final Driver driver;

    // 初始化Driver(建议使用连接池)
    public PageRankService(String uri, String user, String password) {
        driver = GraphDatabase.driver(uri, AuthTokens.basic(user, password));
    }

    // 主业务方法(appNames为动态参数)
    public List<Record> calculateTopPageRankNodes(List<String> appNames) {
        try (Session session = driver.session()) {
            // 步骤1:创建Cypher投影
            createGraphProjection(session, appNames);
            
            // 步骤2:执行PageRank算法
            return session.executeRead(tx -> {
                Result result = tx.run(
                    "CALL gds.pageRank.stream('myGraph') "
                    + "YIELD nodeId, score "
                    + "RETURN gds.util.asNode(nodeId) AS node, score "
                    + "ORDER BY score DESC LIMIT 9");
                
                return result.list();
            });
        }
    }

    // 创建动态图投影(私有方法封装)
    private void createGraphProjection(Session session, List<String> appNames) {
        String nodeQuery = 
            "MATCH (start:i_app) WHERE start.name IN $appNames " +
            "CALL apoc.path.expandConfig(start, { " +
            "    relationshipFilter: '>', " +
            "    labelFilter: '>i_vm|i_phy', " +
            "    minLevel: 1, " +
            "    maxLevel: 2, " +
            "    uniqueness: 'NODE_GLOBAL' " +
            "}) YIELD path " +
            "UNWIND nodes(path) AS node " +
            "RETURN DISTINCT id(node) AS id, labels(node) AS labels";

        String relQuery = 
            "MATCH (start:i_app) WHERE start.name IN $appNames " +
            "CALL apoc.path.expandConfig(start, { " +
            "    relationshipFilter: '>', " +
            "    labelFilter: '>i_vm|i_phy', " +
            "    minLevel: 1, " +
            "    maxLevel: 2, " +
            "    uniqueness: 'NODE_GLOBAL' " +
            "}) YIELD path " +
            "UNWIND relationships(path) AS rel " +
            "RETURN DISTINCT id(startNode(rel)) AS source, " +
            "id(endNode(rel)) AS target, type(rel) AS type";

        // 执行Cypher投影(带参数)
        session.run(
            "CALL gds.graph.project.cypher($graphName, $nodeQuery, $relQuery, {parameters: {appNames: $appNames}})",
            Values.parameters(
                "graphName", "myGraph",
                "nodeQuery", nodeQuery,
                "relQuery", relQuery,
                "appNames", appNames
            )
        ).consume(); // 显式消费结果确保执行
    }

    @Override
    public void close() throws Exception {
        driver.close();
    }

    // 示例用法
    public static void main(String[] args) {
        try (PageRankService service = new PageRankService("bolt://localhost:7687", "neo4j", "password")) {
            List<Record> topNodes = service.calculateTopPageRankNodes(List.of("my-web"));
            topNodes.forEach(record -> 
                System.out.println(record.get("node").asNode() + " Score: " + record.get("score")));
        }
    }
}

四、关键实现细节解析

  1. 参数化传递
    • 使用 $appNames 占位符注入参数(避免 SQL 注入)(#evidence1)
    • 通过 Values.parameters() 构建参数映射,支持 List/Map 等复杂类型
  2. 资源管理
    • 实现 AutoCloseable 接口自动关闭 Driver(#evidence8)
    • 使用 try-with-resources 管理 Session 生命周期
  3. 投影创建优化
    • 将节点/关系查询拆分为独立字符串,提高可维护性
    • session.run().consume() 确保操作原子性(#evidence15)
  4. 结果处理
    • executeRead 用于只读操作,提升并发性能(#evidence3)
    • 使用 gds.util.asNode() 直接获取节点对象,减少二次查询

五、性能优化建议

  1. 索引加速
    // 在应用启动时创建索引
    session.run("CREATE INDEX FOR (n:i_app) ON (n.name)").consume();
    
  2. 批量处理
    // 分批次处理大型appNames列表
    List<List<String>> batches = Lists.partition(appNames, 100);
    batches.forEach(batch -> calculateTopPageRankNodes(batch));
    
  3. 内存控制
    // 添加内存估算检查
    Result result = session.run(
        "CALL gds.graph.project.cypher.estimate($nodeQuery, $relQuery) " +
        "YIELD requiredMemory", params);
    String memory = result.single().get("requiredMemory").asString();
    

六、异常处理策略

  1. 超时机制
    session.withConfig(QueryConfig.builder().withTimeout(Duration.ofSeconds(30)))
    
  2. 重试逻辑
    int retries = 3;
    while (retries-- > 0) {
        try {
            return calculateTopPageRankNodes(appNames);
        } catch (ServiceUnavailableException e) {
            Thread.sleep(1000);
        }
    }
    

七、扩展性设计

  1. 多算法支持
    public enum Algorithm {
        PAGERANK("gds.pageRank.stream"), 
        BETWEENNESS("gds.betweenness.stream");
    
        private final String procedure;
    }
    
  2. 动态结果映射
    public <T> List<T> mapResult(Result result, Function<Record, T> mapper) {
        return result.stream().map(mapper).collect(Collectors.toList());
    }
    

八、生产环境注意事项

  1. 连接池配置
    Config config = Config.builder()
        .withConnectionPoolSize(10)
        .withMaxConnectionLifetime(30, TimeUnit.MINUTES)
        .build();
    driver = GraphDatabase.driver(uri, AuthTokens.basic(user, password), config);
    
  2. 监控集成
    MetricsAdapter metrics = new MetricsAdapter();
    driver.metrics(metrics); // 对接Micrometer/Prometheus
    

以上实现方案充分利用了 Java Driver 的参数化查询、资源自动管理等特性(#evidence1)(#evidence8),同时结合 GDS 库的内存图计算能力(#evidence4),构建了一个高效、可维护的动态 PageRank 计算服务。通过方法参数化、异常重试、监控集成等设计,可满足生产环境复杂场景需求。

参考资料

https://github.com/neo4j/graph-data-science