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 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:
-
Read the graph data of DataFrame from the NebulaGraph database using the NebulaGraph Spark Connector.
-
Transform the graph data of DataFrame to the GraphX graph.
-
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¶
-
Clone the repository
nebula-algorithm
.$ git clone -b v3.0.0 https://github.com/vesoft-inc/nebula-algorithm.git
-
Enter the directory
nebula-algorithm
.$ cd nebula-algorithm
-
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¶
How to use¶
Use algorithm interface (recommended)¶
The lib
repository provides 10 common graph algorithms.
-
Add dependencies to the file
pom.xml
.<dependency> <groupId>com.vesoft</groupId> <artifactId>nebula-algorithm</artifactId> <version>3.0.0</version> </dependency>
-
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.
-
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 } # ... } }
-
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