Skip to content

NebulaGraph Algorithm

NebulaGraph Algorithm (Algorithm) is a Spark application based on GraphX. It uses a complete algorithm tool to perform graph computing on the data in the NebulaGraph database by submitting a Spark task. You can also programmatically use the algorithm under the lib repository to perform graph computing on DataFrame.

Version compatibility

The correspondence between the NebulaGraph Algorithm release and the NebulaGraph core release is as follows.

NebulaGraph Algorithm NebulaGraph
3.0-SNAPSHOT nightly
3.0.0 3.2.1
2.6.x 2.6.x
2.5.0 2.5.0,2.5.1
2.1.0 2.0.0,2.0.1

Prerequisites

Before using the NebulaGraph Algorithm, users need to confirm the following information:

  • The Spark version is 2.4.x.
  • The Scala version is 2.11.
  • (Optional) If users need to clone, compile, and package the latest Algorithm in Github, install Maven.

Limitations

  • When submitting the algorithm package directly, the data of the vertex ID must be an integer. That is, the vertex ID can be INT or String, but the data itself is an integer.
  • For non-integer String data, it is recommended to use the algorithm interface. You can use the dense_rank function of SparkSQL to encode the data as the Long type instead of the String type.
  • Graph computing outputs vertex datasets, and the algorithm results are stored in DataFrames as the properties of vertices. You can do further operations such as statistics and filtering according to your business requirements.

Supported algorithms

The graph computing algorithms supported by NebulaGraph Algorithm are as follows.

Algorithm Description Scenario Properties name Properties type
PageRank The rank of pages Web page ranking, key node mining pagerank double/string
Louvain Louvain Community mining, hierarchical clustering louvain int/string
KCore K core Community discovery, financial risk control kcore int/string
LabelPropagation Label propagation Information spreading, advertising, and community discovery lpa int/string
Hanp Label propagation advanced Community discovery, recommendation system hanp int/string
ConnectedComponent Weakly connected component Community discovery, island discovery cc int/string
StronglyConnectedComponent Strongly connected component Community discovery scc int/string
ShortestPath The shortest path Path planning, network planning shortestpath string
TriangleCount Triangle counting Network structure analysis trianglecount int/string
GraphTriangleCount Graph triangle counting Network structure and tightness analysis count int
BetweennessCentrality Intermediate centrality Key node mining, node influence computing betweenness double/string
ClosenessCentrality Closeness centrality Key node mining, node influence computing closeness double/string
DegreeStatic Degree of statistical Graph structure analysis degree,inDegree,outDegree int/string
ClusteringCoefficient Aggregation coefficient Recommendation system, telecom fraud analysis clustercoefficient double/string
Jaccard Jaccard similarity Similarity computing, recommendation system jaccard string
BFS Breadth-First Search Sequence traversal, shortest path planning bfs string
Node2Vec - Graph classification node2vec string

Note

When writing the algorithm results into the NebulaGraph, make sure that the tag in the corresponding graph space has properties names and data types corresponding to the table above.

Implementation methods

NebulaGraph Algorithm implements the graph calculating as follows:

  1. Read the graph data of DataFrame from the NebulaGraph database using the NebulaGraph Spark Connector.

  2. Transform the graph data of DataFrame to the GraphX graph.

  3. Use graph algorithms provided by GraphX (such as PageRank) or self-implemented algorithms (such as Louvain).

For detailed implementation methods, see Scala file.

Get NebulaGraph Algorithm

Compile and package

  1. Clone the repository nebula-algorithm.

    $ git clone -b v3.0.0 https://github.com/vesoft-inc/nebula-algorithm.git
    
  2. Enter the directory nebula-algorithm.

    $ cd nebula-algorithm
    
  3. Compile and package.

    $ mvn clean package -Dgpg.skip -Dmaven.javadoc.skip=true -Dmaven.test.skip=true
    

After the compilation, a similar file nebula-algorithm-3.x.x.jar is generated in the directory nebula-algorithm/target.

Download maven from the remote repository

Download address

How to use

The lib repository provides 10 common graph algorithms.

  1. Add dependencies to the file pom.xml.

    <dependency>
         <groupId>com.vesoft</groupId>
         <artifactId>nebula-algorithm</artifactId>
         <version>3.0.0</version>
    </dependency>
    
  2. Use the algorithm (take PageRank as an example) by filling in parameters. For more examples, see example.

    Note

    By default, the DataFrame that executes the algorithm sets the first column as the starting vertex, the second column as the destination vertex, and the third column as the edge weights (not the rank in the NebulaGraph).

    val prConfig = new PRConfig(5, 1.0)
    val louvainResult = PageRankAlgo.apply(spark, data, prConfig, false)
    

    If your vertex IDs are Strings, see Pagerank Example for how to encoding and decoding them.

Submit the algorithm package directly

Note

There are limitations to use sealed packages. For example, when sinking a repository into NebulaGraph, the property name of the tag created in the sunk graph space must match the preset name in the code. The first method is recommended if the user has development skills.

  1. Set the Configuration file.

    {
      # Configurations related to Spark
      spark: {
        app: {
            name: LPA
            # The number of partitions of Spark
            partitionNum:100
        }
        master:local
      }
    
      data: {
        # Data source. Optional values are nebula, csv, and json.
        source: csv
        # Data sink. The algorithm result will be written into this sink. Optional values are nebula, csv, and text.
        sink: nebula
        # Whether the algorithm has a weight.
        hasWeight: false
      }
    
      # Configurations related to NebulaGraph
      nebula: {
        # Data source. When NebulaGraph is the data source of the graph computing, the configuration of `nebula.read` is valid.
        read: {
            # The IP addresses and ports of all Meta services. Multiple addresses are separated by commas (,). Example: "ip1:port1,ip2:port2".
            # To deploy NebulaGraph by using Docker Compose, fill in the port with which Docker Compose maps to the outside.
            # Check the status with `docker-compose ps`.
            metaAddress: "192.168.*.10:9559"
            # The name of the graph space in NebulaGraph.
            space: basketballplayer
            # Edge types in NebulaGraph. When there are multiple labels, the data of multiple edges will be merged.
            labels: ["serve"]
            # The property name of each edge type in NebulaGraph. This property will be used as the weight column of the algorithm. Make sure that it corresponds to the edge type.
            weightCols: ["start_year"]
        }
    
        # Data sink. When the graph computing result sinks into NebulaGraph, the configuration of `nebula.write` is valid.
        write:{
            # The IP addresses and ports of all Graph services. Multiple addresses are separated by commas (,). Example: "ip1:port1,ip2:port2".
            # To deploy by using Docker Compose, fill in the port with which Docker Compose maps to the outside.
            # Check the status with `docker-compose ps`.
            graphAddress: "192.168.*.11:9669"
            # The IP addresses and ports of all Meta services. Multiple addresses are separated by commas (,). Example: "ip1:port1,ip2:port2".
            # To deploy NebulaGraph by using Docker Compose, fill in the port with which Docker Compose maps to the outside.
            # Check the staus with `docker-compose ps`.
            metaAddress: "192.168.*.12:9559"
            user:root
            pswd:nebula
            # Before submitting the graph computing task, create the graph space and tag.
            # The name of the graph space in NebulaGraph.
            space:nb
            # The name of the tag in NebulaGraph. The graph computing result will be written into this tag. The property name of this tag is as follows.
            # PageRank: pagerank
            # Louvain: louvain
            # ConnectedComponent: cc
            # StronglyConnectedComponent: scc
            # LabelPropagation: lpa
            # ShortestPath: shortestpath
            # DegreeStatic: degree,inDegree,outDegree
            # KCore: kcore
            # TriangleCount: tranglecpunt
            # BetweennessCentrality: betweennedss
            tag:pagerank
        }
        }  
    
      local: {
        # Data source. When the data source is csv or json, the configuration of `local.read` is valid.
        read:{
            filePath: "hdfs://127.0.0.1:9000/edge/work_for.csv"
            # If the CSV file has a header or it is a json file, use the header. If not, use [_c0, _c1, _c2, ..., _cn] instead.
            # The header of the source VID column.
            srcId:"_c0"
            # The header of the destination VID column.
            dstId:"_c1"
            # The header of the weight column.
            weight: "_c2"
            # Whether the csv file has a header.
            header: false
            # The delimiter in the csv file.
            delimiter:","
        }
    
        # Data sink. When the graph computing result sinks to the csv or text file, the configuration of `local.write` is valid.
        write:{
            resultPath:/tmp/
        }
        }
    
    
      algorithm: {
        # The algorithm to execute. Optional values are as follow: 
        # pagerank, louvain, connectedcomponent, labelpropagation, shortestpaths, 
        # degreestatic, kcore, stronglyconnectedcomponent, trianglecount ,
        # betweenness, graphtriangleCount.
        executeAlgo: pagerank
    
        # PageRank
        pagerank: {
            maxIter: 10
            resetProb: 0.15 
        }
    
        # Louvain
        louvain: {
            maxIter: 20
            internalIter: 10
            tol: 0.5
        }
    
       # ...
    
    }
    }
    
  2. Submit the graph computing task.

    ${SPARK_HOME}/bin/spark-submit --master <mode> --class com.vesoft.nebula.algorithm.Main <nebula-algorithm-3.0.0.jar_path> -p <application.conf_path>
    

    Example:

    ${SPARK_HOME}/bin/spark-submit --master "local" --class com.vesoft.nebula.algorithm.Main /root/nebula-algorithm/target/nebula-algorithm-3.0-SNAPSHOT.jar -p /root/nebula-algorithm/src/main/resources/application.conf
    

Last update: September 16, 2022