Nebula Algorithm¶

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

Prerequisites¶

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

• The Nebula Graph services have been deployed and started. For details, see Nebula 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¶

• 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 Nebula Algorithm are as follows.

Implementation methods¶

Nebula Algorithm implements the graph calculating as follows:

1. Read the graph data of DataFrame from the Nebula Graph database using the Nebula 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 Nebula Algorithm¶

Compile and package¶

1. Clone the repository nebula-algorithm.

   $ git clone -b v2.5 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-2.5.1.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>2.5.1</version>
   </dependency>
   

2. Use the algorithm (take PageRank as an example) by filling in parameters. For more example, 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 Nebula Graph).

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

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 Nebula Graph
     nebula: {
       # Data source. When Nebula Graph 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 Nebula Graph 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 Nebula Graph.
           space: basketballplayer
           # Edge types in Nebula Graph. When there are multiple labels, the data of multiple edges will be merged.
           labels: ["serve"]
           # The property name of each edge type in Nebula Graph. 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 Nebula Graph, 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 Nebula Graph 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 Nebula Graph.
           space:nb
           # The name of the tag in Nebula Graph. 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 pagerank, louvain, connectedcomponent,
       # labelpropagation, shortestpaths, degreestatic, kcore,
       # stronglyconnectedcomponent, trianglecount, betweenness,
       executeAlgo: pagerank
   
       # PageRank
       pagerank: {
           maxIter: 10
           resetProb: 0.15  # The default value is 0.15
       }
   
       # Louvain
       louvain: {
           maxIter: 20
           internalIter: 10
           tol: 0.5
       }
   
      # ConnectedComponent/StronglyConnectedComponent
      connectedcomponent: {
          maxIter: 20
      }
   
      # LabelPropagation
      labelpropagation: {
          maxIter: 20
      }
   
       # ShortestPath
       shortestpaths: {
           # several vertices to compute the shortest path to all vertices.
           landmarks: "1"
       }
   
       # DegreeStatic
       degreestatic: {}
   
       # KCore
       kcore:{
           maxIter:10
           degree:1
       }
   
       # TriangleCount
       trianglecount:{}
   
       # BetweennessCentrality
       betweenness:{
           maxIter:5
       }
   }
   }
   

2. Submit the graph computing task.

   ${SPARK_HOME}/bin/spark-submit --master <mode> --class com.vesoft.nebula.algorithm.Main <nebula-algorithm-2.5.1.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-2.5.1.jar -p /root/nebula-algorithm/src/main/resources/application.conf