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example/src/main/scala/com/vesoft/nebula/algorithm/AlgoPerformanceTest.scala
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| /* Copyright (c) 2022 vesoft inc. All rights reserved. | ||
| * | ||
| * This source code is licensed under Apache 2.0 License. | ||
| */ | ||
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| package com.vesoft.nebula.algorithm | ||
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| import com.vesoft.nebula.connector.connector.{NebulaDataFrameReader} | ||
| import com.facebook.thrift.protocol.TCompactProtocol | ||
| import com.vesoft.nebula.algorithm.config.{CcConfig, LPAConfig, LouvainConfig, PRConfig} | ||
| import com.vesoft.nebula.algorithm.lib.{ | ||
| ConnectedComponentsAlgo, | ||
| LabelPropagationAlgo, | ||
| LouvainAlgo, | ||
| PageRankAlgo | ||
| } | ||
| import com.vesoft.nebula.connector.{NebulaConnectionConfig, ReadNebulaConfig} | ||
| import org.apache.spark.SparkConf | ||
| import org.apache.spark.sql.{DataFrame, SparkSession} | ||
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| object AlgoPerformanceTest { | ||
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| def main(args: Array[String]): Unit = { | ||
| val sparkConf = new SparkConf() | ||
| .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") | ||
| .registerKryoClasses(Array[Class[_]](classOf[TCompactProtocol])) | ||
| val spark = SparkSession | ||
| .builder() | ||
| .config(sparkConf) | ||
| .getOrCreate() | ||
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| val df = readNebulaData(spark) | ||
| lpa(spark, df) | ||
| louvain(spark, df) | ||
| pagerank(spark, df) | ||
| wcc(spark, df) | ||
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| } | ||
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| def readNebulaData(spark: SparkSession): DataFrame = { | ||
| val start = System.currentTimeMillis() | ||
| val config = | ||
| NebulaConnectionConfig | ||
| .builder() | ||
| .withMetaAddress("127.0.0.0.1:9559") | ||
| .withTimeout(6000) | ||
| .withConenctionRetry(2) | ||
| .build() | ||
| val nebulaReadEdgeConfig: ReadNebulaConfig = ReadNebulaConfig | ||
| .builder() | ||
| .withSpace("twitter") | ||
| .withLabel("FOLLOW") | ||
| .withNoColumn(true) | ||
| .withLimit(20000) | ||
| .withPartitionNum(120) | ||
| .build() | ||
| val df: DataFrame = | ||
| spark.read.nebula(config, nebulaReadEdgeConfig).loadEdgesToDF() | ||
| df.cache() | ||
| df.count() | ||
| println(s"read data cost time ${(System.currentTimeMillis() - start)}") | ||
| df | ||
| } | ||
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| def lpa(spark: SparkSession, df: DataFrame): Unit = { | ||
| val start = System.currentTimeMillis() | ||
| val lpaConfig = LPAConfig(10) | ||
| val lpa = LabelPropagationAlgo.apply(spark, df, lpaConfig, false) | ||
| lpa.write.csv("hdfs://127.0.0.1:9000/tmp/lpa") | ||
| println(s"lpa compute and save cost ${System.currentTimeMillis() - start}") | ||
| } | ||
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| def pagerank(spark: SparkSession, df: DataFrame): Unit = { | ||
| val start = System.currentTimeMillis() | ||
| val pageRankConfig = PRConfig(10, 0.85) | ||
| val pr = PageRankAlgo.apply(spark, df, pageRankConfig, false) | ||
| pr.write.csv("hdfs://127.0.0.1:9000/tmp/pagerank") | ||
| println(s"pagerank compute and save cost ${System.currentTimeMillis() - start}") | ||
| } | ||
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| def wcc(spark: SparkSession, df: DataFrame): Unit = { | ||
| val start = System.currentTimeMillis() | ||
| val ccConfig = CcConfig(20) | ||
| val cc = ConnectedComponentsAlgo.apply(spark, df, ccConfig, false) | ||
| cc.write.csv("hdfs://127.0.0.1:9000/tmp/wcc") | ||
| println(s"wcc compute and save cost ${System.currentTimeMillis() - start}") | ||
| } | ||
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| def louvain(spark: SparkSession, df: DataFrame): Unit = { | ||
| val start = System.currentTimeMillis() | ||
| val louvainConfig = LouvainConfig(10, 5, 0.5) | ||
| val louvain = LouvainAlgo.apply(spark, df, louvainConfig, false) | ||
| louvain.write.csv("hdfs://127.0.0.1:9000/tmp/louvain") | ||
| println(s"louvain compute and save cost ${System.currentTimeMillis() - start}") | ||
| } | ||
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| } |