support id mapping for algos and add examples

README-CN.md

@@ -51,9 +51,6 @@ nebula-algorithm 是一款基于 [GraphX](https://spark.apache.org/graphx/) 的
     ```
     ${SPARK_HOME}/bin/spark-submit --master <mode> --class com.vesoft.nebula.algorithm.Main nebula-algorithm-3.0-SNAPSHOT.jar -p application.conf
     ```
-    * 使用限制
-
-    Nebula Algorithm 算法包未自动对字符串 id 进行编码，因此采用第一种方式执行图算法时，边的源点和目标点必须是整数（Nebula Space 的 vid_type 可以是 String 类型，但数据必须是整数）。
 * 使用方法2：调用 nebula-algorithm 算法接口
 
    在 `nebula-algorithm` 的 `lib` 库中提供了10+种常用图计算算法，可通过编程调用的形式调用算法。
@@ -75,7 +72,8 @@ nebula-algorithm 是一款基于 [GraphX](https://spark.apache.org/graphx/) 的
    val prConfig = new PRConfig(5, 1.0)
    val prResult = PageRankAlgo.apply(spark, data, prConfig, false)
    ```
-   * 如果你的节点 id 是 String 类型，可以参考 PageRank 的 [Example](https://github.com/vesoft-inc/nebula-algorithm/blob/master/example/src/main/scala/com/vesoft/nebula/algorithm/PageRankExample.scala) 。 
+   * 如果你的节点 id 是 String 类型，可以参考 [examples](https://github.com/vesoft-inc/nebula-algorithm/tree/master/example/src/main/scala/com/vesoft/nebula/algorithm/DegreeStaticExample.scala).
+
    该 Example 进行了 id 转换，将 String 类型 id 编码为 Long 类型的 id ， 并在算法结果中将 Long 类型 id 解码为原始的 String 类型 id 。
 
     其他算法的调用方法见[测试示例](https://github.com/vesoft-inc/nebula-algorithm/tree/master/nebula-algorithm/src/test/scala/com/vesoft/nebula/algorithm/lib) 。

README.md

@@ -60,10 +60,6 @@ You could submit the entire spark application or invoke algorithms in `lib` libr
     ${SPARK_HOME}/bin/spark-submit --master <mode> --class com.vesoft.nebula.algorithm.Main nebula-algorithm-3.0—SNAPSHOT.jar -p application.conf
     ```
 
-   * Limitation
-
-    Due to Nebula Algorithm jar does not encode string id, thus during the algorithm execution, the source and target of edges must be in Type Int (The `vid_type` in Nebula Space could be String, while data must be in Type Int).
-
 * Option2: Call nebula-algorithm interface
 
    Now there are 10+ algorithms provided in `lib` from `nebula-algorithm`, which could be invoked in a programming fashion as below:
@@ -87,7 +83,7 @@ You could submit the entire spark application or invoke algorithms in `lib` libr
    val prResult = PageRankAlgo.apply(spark, data, prConfig, false)
    ```
 
-   If your vertex ids are Strings, see [Pagerank Example](https://github.com/vesoft-inc/nebula-algorithm/blob/master/example/src/main/scala/com/vesoft/nebula/algorithm/PageRankExample.scala) for how to encoding and decoding them.
+   If your vertex ids are Strings, please set the algo config with encodeId = true. see [examples](https://github.com/vesoft-inc/nebula-algorithm/tree/master/example/src/main/scala/com/vesoft/nebula/algorithm/DegreeStaticExample.scala)
 
     For examples of other algorithms, see [examples](https://github.com/vesoft-inc/nebula-algorithm/tree/master/example/src/main/scala/com/vesoft/nebula/algorithm)
    > Note: The first column of DataFrame in the application represents the source vertices, the second represents the target vertices and the third represents edges' weight.

example/pom.xml

@@ -182,7 +182,7 @@
         <dependency>
             <groupId>com.vesoft</groupId>
             <artifactId>nebula-algorithm</artifactId>
-            <version>3.0.0</version>
+            <version>3.0-SNAPSHOT</version>
         </dependency>
     </dependencies>
 </project>

example/src/main/scala/com/vesoft/nebula/algorithm/DegreeStaticExample.scala

@@ -6,7 +6,8 @@
 package com.vesoft.nebula.algorithm
 
 import com.facebook.thrift.protocol.TCompactProtocol
-import com.vesoft.nebula.algorithm.lib.{DegreeStaticAlgo}
+import com.vesoft.nebula.algorithm.config.DegreeStaticConfig
+import com.vesoft.nebula.algorithm.lib.DegreeStaticAlgo
 import org.apache.spark.SparkConf
 import org.apache.spark.sql.{DataFrame, SparkSession}
 
@@ -22,15 +23,22 @@ object DegreeStaticExample {
       .config(sparkConf)
       .getOrCreate()
 
-    // val csvDF     = ReadData.readCsvData(spark)
     // val nebulaDF  = ReadData.readNebulaData(spark)
     val journalDF = ReadData.readLiveJournalData(spark)
-
     degree(spark, journalDF)
+
+    val csvDF = ReadData.readStringCsvData(spark)
+    degreeForStringId(spark, csvDF)
   }
 
   def degree(spark: SparkSession, df: DataFrame): Unit = {
     val degree = DegreeStaticAlgo.apply(spark, df)
     degree.show()
   }
+
+  def degreeForStringId(spark: SparkSession, df: DataFrame): Unit = {
+    val degreeConfig = new DegreeStaticConfig(true)
+    val degree       = DegreeStaticAlgo.apply(spark, df, degreeConfig)
+    degree.show()
+  }
 }

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

@@ -113,7 +113,9 @@
    }
 
     # Vertex degree statistics parameter
-    degreestatic: {}
+    degreestatic: {
+        encodeId:false
+    }
 
    # KCore parameter
    kcore:{
@@ -123,7 +125,9 @@
    }
 
    # Trianglecount parameter
-   trianglecount:{}
+   trianglecount:{
+        encodeId:false
+   }
 
    # graphTriangleCount parameter
    graphtrianglecount:{}
@@ -189,6 +193,7 @@
    # JaccardAlgo parameter
    jaccard:{
        tol: 1.0
+       encodeId:false
    }
  }
 }

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

@@ -5,6 +5,7 @@
 
 package com.vesoft.nebula.algorithm.config
 
+import com.vesoft.nebula.algorithm.config.JaccardConfig.encodeId
 import org.apache.spark.graphx.VertexId
 
 case class PRConfig(maxIter: Int, resetProb: Double, encodeId: Boolean = false)
@@ -110,24 +111,30 @@ object LouvainConfig {
 /**
   * degree static
   */
-case class DegreeStaticConfig(degree: Boolean,
-                              inDegree: Boolean,
-                              outDegree: Boolean,
-                              encodeId: Boolean = false)
+case class DegreeStaticConfig(encodeId: Boolean = false)
 
 object DegreeStaticConfig {
-  var degree: Boolean    = false
-  var inDegree: Boolean  = false
-  var outDegree: Boolean = false
-  var encodeId: Boolean  = false
+  var encodeId: Boolean = false
 
   def getDegreeStaticConfig(configs: Configs): DegreeStaticConfig = {
     val degreeConfig = configs.algorithmConfig.map
-    degree = ConfigUtil.getOrElseBoolean(degreeConfig, "algorithm.degreestatic.degree", false)
-    inDegree = ConfigUtil.getOrElseBoolean(degreeConfig, "algorithm.degreestatic.indegree", false)
-    outDegree = ConfigUtil.getOrElseBoolean(degreeConfig, "algorithm.degreestatic.outdegree", false)
     encodeId = ConfigUtil.getOrElseBoolean(degreeConfig, "algorithm.degreestatic.encodeId", false)
-    DegreeStaticConfig(degree, inDegree, outDegree, encodeId)
+    DegreeStaticConfig(encodeId)
+  }
+}
+
+/**
+  * graph triangle count
+  */
+case class TriangleConfig(encodeId: Boolean = false)
+
+object TriangleConfig {
+  var encodeId: Boolean = false
+  def getTriangleConfig(configs: Configs): TriangleConfig = {
+    val triangleConfig = configs.algorithmConfig.map
+    encodeId =
+      ConfigUtil.getOrElseBoolean(triangleConfig, "algorithm.trianglecount.encodeId", false)
+    TriangleConfig(encodeId)
   }
 }
 
@@ -321,14 +328,16 @@ object Node2vecConfig {
 /**
   * Jaccard
   */
-case class JaccardConfig(tol: Double)
+case class JaccardConfig(tol: Double, encodeId: Boolean = false)
 
 object JaccardConfig {
-  var tol: Double = _
+  var tol: Double       = _
+  var encodeId: Boolean = false
   def getJaccardConfig(configs: Configs): JaccardConfig = {
     val jaccardConfig = configs.algorithmConfig.map
     tol = jaccardConfig("algorithm.jaccard.tol").toDouble
-    JaccardConfig(tol)
+    encodeId = ConfigUtil.getOrElseBoolean(jaccardConfig, "algorithm.jaccard.encodeId", false)
+    JaccardConfig(tol, encodeId)
   }
 }
 

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

@@ -50,9 +50,9 @@ object BfsAlgo {
       .orderBy(col(AlgoConstants.BFS_RESULT_COL))
 
     if (bfsConfig.encodeId) {
-      DecodeUtil.convertAlgoId2StringId(algoResult, encodeIdDf)
+      DecodeUtil.convertAlgoId2StringId(algoResult, encodeIdDf).coalesce(1)
     } else {
-      algoResult
+      algoResult.coalesce(1)
     }
   }
 

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

@@ -5,8 +5,8 @@
 
 package com.vesoft.nebula.algorithm.lib
 
-import com.vesoft.nebula.algorithm.config.AlgoConstants
-import com.vesoft.nebula.algorithm.utils.NebulaUtil
+import com.vesoft.nebula.algorithm.config.{AlgoConstants, DegreeStaticConfig}
+import com.vesoft.nebula.algorithm.utils.{DecodeUtil, NebulaUtil}
 import org.apache.log4j.Logger
 import org.apache.spark.graphx.{Graph, VertexRDD}
 import org.apache.spark.rdd.RDD
@@ -22,9 +22,18 @@ object DegreeStaticAlgo {
   /**
     * run the pagerank algorithm for nebula graph
     */
-  def apply(spark: SparkSession, dataset: Dataset[Row]): DataFrame = {
+  def apply(spark: SparkSession,
+            dataset: Dataset[Row],
+            degreeConfig: DegreeStaticConfig = new DegreeStaticConfig): DataFrame = {
+    var encodeIdDf: DataFrame = null
 
-    val graph: Graph[None.type, Double] = NebulaUtil.loadInitGraph(dataset, false)
+    val graph: Graph[None.type, Double] = if (degreeConfig.encodeId) {
+      val (data, encodeId) = DecodeUtil.convertStringId2LongId(dataset, false)
+      encodeIdDf = encodeId
+      NebulaUtil.loadInitGraph(data, false)
+    } else {
+      NebulaUtil.loadInitGraph(dataset, false)
+    }
 
     val degreeResultRDD = execute(graph)
 
@@ -38,7 +47,11 @@ object DegreeStaticAlgo {
     val algoResult = spark.sqlContext
       .createDataFrame(degreeResultRDD, schema)
 
-    algoResult
+    if (degreeConfig.encodeId) {
+      DecodeUtil.convertAlgoId2StringId(algoResult, encodeIdDf)
+    } else {
+      algoResult
+    }
   }
 
   def execute(graph: Graph[None.type, Double]): RDD[Row] = {

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

@@ -6,7 +6,9 @@
 package com.vesoft.nebula.algorithm.lib
 
 import com.vesoft.nebula.algorithm.config.JaccardConfig
+import com.vesoft.nebula.algorithm.utils.{DecodeUtil, NebulaUtil}
 import org.apache.log4j.Logger
+import org.apache.spark.graphx.Graph
 import org.apache.spark.ml.feature.{
   CountVectorizer,
   CountVectorizerModel,
@@ -29,7 +31,16 @@ object JaccardAlgo {
     */
   def apply(spark: SparkSession, dataset: Dataset[Row], jaccardConfig: JaccardConfig): DataFrame = {
 
-    val jaccardResult: RDD[Row] = execute(spark, dataset, jaccardConfig.tol)
+    var encodeIdDf: DataFrame = null
+    var data: DataFrame       = dataset
+
+    if (jaccardConfig.encodeId) {
+      val (encodeData, encodeId) = DecodeUtil.convertStringId2LongId(dataset, false)
+      encodeIdDf = encodeId
+      data = encodeData
+    }
+
+    val jaccardResult: RDD[Row] = execute(spark, data, jaccardConfig.tol)
 
     val schema = StructType(
       List(
@@ -38,7 +49,13 @@ object JaccardAlgo {
         StructField("similarity", DoubleType, nullable = true)
       ))
     val algoResult = spark.sqlContext.createDataFrame(jaccardResult, schema)
-    algoResult
+
+    if (jaccardConfig.encodeId) {
+      DecodeUtil.convertIds2String(algoResult, encodeIdDf, "srcId", "dstId")
+    } else {
+      algoResult
+    }
+
   }
 
   def execute(spark: SparkSession, dataset: Dataset[Row], tol: Double): RDD[Row] = {

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

@@ -5,8 +5,8 @@
 
 package com.vesoft.nebula.algorithm.lib
 
-import com.vesoft.nebula.algorithm.config.AlgoConstants
-import com.vesoft.nebula.algorithm.utils.NebulaUtil
+import com.vesoft.nebula.algorithm.config.{AlgoConstants, TriangleConfig}
+import com.vesoft.nebula.algorithm.utils.{DecodeUtil, NebulaUtil}
 import org.apache.log4j.Logger
 import org.apache.spark.graphx.{Graph, VertexRDD}
 import org.apache.spark.graphx.lib.TriangleCount
@@ -24,9 +24,19 @@ object TriangleCountAlgo {
     *
     * compute each vertex's triangle count
     */
-  def apply(spark: SparkSession, dataset: Dataset[Row]): DataFrame = {
+  def apply(spark: SparkSession,
+            dataset: Dataset[Row],
+            triangleConfig: TriangleConfig = new TriangleConfig): DataFrame = {
 
-    val graph: Graph[None.type, Double] = NebulaUtil.loadInitGraph(dataset, false)
+    var encodeIdDf: DataFrame = null
+
+    val graph: Graph[None.type, Double] = if (triangleConfig.encodeId) {
+      val (data, encodeId) = DecodeUtil.convertStringId2LongId(dataset, false)
+      encodeIdDf = encodeId
+      NebulaUtil.loadInitGraph(data, false)
+    } else {
+      NebulaUtil.loadInitGraph(dataset, false)
+    }
 
     val triangleResultRDD = execute(graph)
 
@@ -38,7 +48,11 @@ object TriangleCountAlgo {
     val algoResult = spark.sqlContext
       .createDataFrame(triangleResultRDD, schema)
 
-    algoResult
+    if (triangleConfig.encodeId) {
+      DecodeUtil.convertAlgoId2StringId(algoResult, encodeIdDf)
+    } else {
+      algoResult
+    }
   }
 
   def execute(graph: Graph[None.type, Double]): RDD[Row] = {

nebula-algorithm/src/main/scala/com/vesoft/nebula/algorithm/utils/DecodeUtil.scala

@@ -73,4 +73,21 @@ object DecodeUtil {
       .drop(algoProp)
       .withColumnRenamed(ORIGIN_ID_COL, algoProp)
   }
+
+  def convertIds2String(dataframe: DataFrame,
+                        encodeId: DataFrame,
+                        srcCol: String,
+                        dstCol: String): DataFrame = {
+    encodeId
+      .join(dataframe)
+      .where(col(ENCODE_ID_COL) === col(srcCol))
+      .drop(ENCODE_ID_COL)
+      .drop(srcCol)
+      .withColumnRenamed(ORIGIN_ID_COL, srcCol)
+      .join(encodeId)
+      .where(col(dstCol) === col(ENCODE_ID_COL))
+      .drop(ENCODE_ID_COL)
+      .drop(dstCol)
+      .withColumnRenamed(ORIGIN_ID_COL, dstCol)
+  }
 }

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

@@ -5,13 +5,15 @@
 
 package com.vesoft.nebula.algorithm.lib
 
+import com.vesoft.nebula.algorithm.config.DegreeStaticConfig
 import org.apache.spark.sql.SparkSession
 import org.junit.Test
 
 class DegreeStaticAlgoSuite {
   @Test
   def degreeStaticAlgoSuite(): Unit = {
-    val spark  = SparkSession.builder().master("local").getOrCreate()
+    val spark =
+      SparkSession.builder().master("local").config("spark.sql.shuffle.partitions", 5).getOrCreate()
     val data   = spark.read.option("header", true).csv("src/test/resources/edge.csv")
     val result = DegreeStaticAlgo.apply(spark, data)
     assert(result.count() == 4)
@@ -20,5 +22,9 @@ class DegreeStaticAlgoSuite {
       assert(row.get(2).toString.toInt == 4)
       assert(row.get(3).toString.toInt == 4)
     })
+
+    val config       = DegreeStaticConfig(true)
+    val encodeResult = DegreeStaticAlgo.apply(spark, data, config)
+    assert(result.count() == 4)
   }
 }

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

@@ -14,10 +14,14 @@ import org.junit.Test
 class HanpSuite {
   @Test
   def hanpSuite() = {
-    val spark      = SparkSession.builder().master("local").getOrCreate()
+    val spark =
+      SparkSession.builder().master("local").config("spark.sql.shuffle.partitions", 5).getOrCreate()
     val data       = spark.read.option("header", true).csv("src/test/resources/edge.csv")
     val hanpConfig = new HanpConfig(0.1, 10, 1.0)
     val result     = HanpAlgo.apply(spark, data, hanpConfig, false)
     assert(result.count() == 4)
+
+    val encodeHanpConfig = new HanpConfig(0.1, 10, 1.0, true)
+    assert(HanpAlgo.apply(spark, data, encodeHanpConfig, false).count() == 4)
   }
 }