add Dfs algorithm

nebula-algorithm/src/main/resources/application.conf

@@ -144,6 +144,12 @@
        root:"10"
    }
 
+   # DFS parameter
+   dfs:{
+       maxIter:5
+       root:"10"
+   }
+
    # HanpAlgo parameter
    hanp:{
        hopAttenuation:0.1

nebula-algorithm/src/main/scala/com/vesoft/nebula/algorithm/Main.scala

@@ -13,6 +13,7 @@ import com.vesoft.nebula.algorithm.config.{
   CcConfig,
   CoefficientConfig,
   Configs,
+  DfsConfig,
   HanpConfig,
   JaccardConfig,
   KCoreConfig,
@@ -30,6 +31,7 @@ import com.vesoft.nebula.algorithm.lib.{
   ClusteringCoefficientAlgo,
   ConnectedComponentsAlgo,
   DegreeStaticAlgo,
+  DfsAlgo,
   GraphTriangleCountAlgo,
   HanpAlgo,
   JaccardAlgo,
@@ -204,6 +206,10 @@ object Main {
           val bfsConfig = BfsConfig.getBfsConfig(configs)
           BfsAlgo(spark, dataSet, bfsConfig)
         }
+        case "dfs" => {
+          val dfsConfig = DfsConfig.getDfsConfig(configs)
+          DfsAlgo(spark, dataSet, dfsConfig)
+        }
         case "jaccard" => {
           val jaccardConfig = JaccardConfig.getJaccardConfig(configs)
           JaccardAlgo(spark, dataSet, jaccardConfig)

nebula-algorithm/src/main/scala/com/vesoft/nebula/algorithm/config/AlgoConfig.scala

@@ -183,6 +183,22 @@ object BfsConfig {
   }
 }
 
+/**
+  * dfs
+  */
+case class DfsConfig(maxIter: Int, root: Long)
+object DfsConfig {
+  var maxIter: Int = _
+  var root: Long   = _
+
+  def getDfsConfig(configs: Configs): DfsConfig = {
+    val dfsConfig = configs.algorithmConfig.map
+    maxIter = dfsConfig("algorithm.dfs.maxIter").toInt
+    root = dfsConfig("algorithm.dfs.root").toLong
+    DfsConfig(maxIter, root)
+  }
+}
+
 /**
   * Hanp
   */

nebula-algorithm/src/main/scala/com/vesoft/nebula/algorithm/lib/DfsAlgo.scala

@@ -0,0 +1,47 @@
+/* Copyright (c) 2022 vesoft inc. All rights reserved.
+ *
+ * This source code is licensed under Apache 2.0 License.
+ */
+
+package com.vesoft.nebula.algorithm.lib
+
+import com.vesoft.nebula.algorithm.config.{AlgoConstants, BfsConfig, DfsConfig}
+import com.vesoft.nebula.algorithm.utils.NebulaUtil
+import org.apache.spark.graphx.{EdgeDirection, Graph, VertexId}
+import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.sql.types.{DoubleType, LongType, StringType, StructField, StructType}
+
+import scala.collection.mutable
+
+object DfsAlgo {
+  var iterNums = 0
+
+  def apply(spark: SparkSession, dataset: Dataset[Row], dfsConfig: DfsConfig): DataFrame = {
+    val graph: Graph[None.type, Double] = NebulaUtil.loadInitGraph(dataset, false)
+    val bfsVertices                     = dfs(graph, dfsConfig.root, mutable.Seq.empty[VertexId])(dfsConfig.maxIter)
+
+    val schema = StructType(List(StructField("dfs", LongType, nullable = false)))
+
+    val rdd = spark.sparkContext.parallelize(bfsVertices.toSeq, 1).map(row => Row(row))
+    val algoResult = spark.sqlContext
+      .createDataFrame(rdd, schema)
+
+    algoResult.repartition(1)
+  }
+
+  def dfs(g: Graph[None.type, Double], vertexId: VertexId, visited: mutable.Seq[VertexId])(
+      maxIter: Int): mutable.Seq[VertexId] = {
+    if (visited.contains(vertexId)) {
+      visited
+    } else {
+      if (iterNums > maxIter) {
+        return visited
+      }
+      val newVisited = visited :+ vertexId
+      val neighbors  = g.collectNeighbors(EdgeDirection.Out).lookup(vertexId).flatten
+      iterNums = iterNums + 1
+      neighbors.foldLeft(newVisited)((visited, neighbor) => dfs(g, neighbor._1, visited)(maxIter))
+    }
+  }
+
+}

nebula-algorithm/src/test/scala/com/vesoft/nebula/algorithm/lib/DfsAlgoSuite.scala

@@ -0,0 +1,23 @@
+/* Copyright (c) 2022 vesoft inc. All rights reserved.
+ *
+ * This source code is licensed under Apache 2.0 License.
+ */
+
+package scala.com.vesoft.nebula.algorithm.lib
+
+import com.vesoft.nebula.algorithm.config.{BfsConfig, DfsConfig}
+import com.vesoft.nebula.algorithm.lib.{BfsAlgo, DfsAlgo}
+import org.apache.spark.sql.SparkSession
+import org.junit.Test
+
+class DfsAlgoSuite {
+  @Test
+  def bfsAlgoSuite(): Unit = {
+    val spark         = SparkSession.builder().master("local").getOrCreate()
+    val data          = spark.read.option("header", true).csv("src/test/resources/edge.csv")
+    val dfsAlgoConfig = new DfsConfig(5, 3)
+    val result        = DfsAlgo.apply(spark, data, dfsAlgoConfig)
+    result.show()
+    assert(result.count() == 4)
+  }
+}