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	NebulaGraph Algorithm
nightly	3.0-SNAPSHOT
3.0.0 ~ 3.3.x	3.0.0
2.6.x	2.6.x
2.5.0、2.5.1	2.5.0
2.0.0、2.0.1	2.1.0
## Prerequisites

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

• The NebulaGraph services have been deployed and started. For details, see NebulaGraph Installation.

• 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

Use algorithm interface (recommended)

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

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
       }
   
      # ...
   
   }
   }
   

   Note

   When sink: nebula is configured, it means that the algorithm results will be written back to the NebulaGraph cluster. The property names of the tag have implicit conventions. For details, see Supported algorithms section of this topic.

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
   

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Last update: February 19, 2024