spark学习-66-源代码：schedulerBackend和taskScheduler的创建(4)-yarn

最新推荐文章于 2022-07-19 16:27:18 发布
最新推荐文章于 2022-07-19 16:27:18 发布
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分类专栏： 大数据-spark 文章标签： spark 源代码 yarn
本文为博主九师兄（QQ:541711153 欢迎来探讨技术）原创文章，未经允许博主不允许转载。
本文链接：https://blog.youkuaiyun.com/qq_21383435/article/details/78906846
本文深入探讨了在Spark中，当设置为yarn模式时，如何进行任务调度。通过分析代码，揭示了如何根据masterUrl匹配选择外部调度器，并重点介绍了YarnClusterManager在yarn-cluster和yarn-client模式下的不同实现，特别是YarnClientSchedulerBackend的start方法在yarn-client模式中的关键作用。

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1。在下面代码中，指定了yarn模式运行，但是它是怎么调度的呢？


PARK_HOME/bin/spark-submit --name "lcc_sparkSql_check" --master　yarn　--class HbaseDataCheck.HbaseDataCheck　/home/lcc/
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op3.2\jars\RoaringBitmap-0.9.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-collection-compat_2.12-2.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-compiler-2.12.10.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-library-2.12.10.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-parser-combinators_2.12-1.1.2.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-reflect-2.12.10.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\scala-xml_2.12-1.2.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\shapeless_2.12-2.3.3.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\shims-0.9.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\slf4j-api-1.7.30.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\slf4j-log4j12-1.7.30.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\snakeyaml-1.24.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\snappy-java-1.1.8.2.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-catalyst_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-core_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-graphx_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-hive-thriftserver_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-hive_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-kubernetes_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-kvstore_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-launcher_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-mesos_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-mllib-local_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-mllib_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-network-common_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-network-shuffle_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-repl_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-sketch_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-sql_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-streaming_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-tags_2.12-3.1.1-tests.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-tags_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-unsafe_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spark-yarn_2.12-3.1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire-macros_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire-platform_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire-util_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\spire_2.12-0.17.0-M1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\ST4-4.0.4.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\stax-api-1.0.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\stax2-api-3.1.4.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\stream-2.9.6.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\super-csv-2.2.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\threeten-extra-1.5.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\token-provider-1.0.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\transaction-api-1.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\univocity-parsers-2.9.1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\velocity-1.5.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\woodstox-core-5.0.3.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\xbean-asm7-shaded-4.15.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\xz-1.5.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\zjsonpatch-0.3.0.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\zookeeper-3.4.14.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\zstd-jni-1.4.8-1.jar;D:\spark\spark-3.1.1-bin-hadoop3.2\jars\arrow-vector-2.0.0.jar" car.LoadModelRideHailing Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 25/06/08 17:05:07 INFO SparkContext: Running Spark version 3.1.1 25/06/08 17:05:07 INFO ResourceUtils: ============================================================== 25/06/08 17:05:07 INFO ResourceUtils: No custom resources configured for spark.driver. 25/06/08 17:05:07 INFO ResourceUtils: ============================================================== 25/06/08 17:05:07 INFO SparkContext: Submitted application: LoadModelRideHailing 25/06/08 17:05:07 INFO ResourceProfile: Default ResourceProfile created, executor resources: Map(cores -> name: cores, amount: 1, script: , vendor: , memory -> name: memory, amount: 1024, script: , vendor: , offHeap -> name: offHeap, amount: 0, script: , vendor: ), task resources: Map(cpus -> name: cpus, amount: 1.0) 25/06/08 17:05:07 INFO ResourceProfile: Limiting resource is cpu 25/06/08 17:05:07 INFO ResourceProfileManager: Added ResourceProfile id: 0 25/06/08 17:05:07 INFO SecurityManager: Changing view acls to: wyatt 25/06/08 17:05:07 INFO SecurityManager: Changing modify acls to: wyatt 25/06/08 17:05:07 INFO SecurityManager: Changing view acls groups to: 25/06/08 17:05:07 INFO SecurityManager: Changing modify acls groups to: 25/06/08 17:05:07 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(wyatt); groups with view permissions: Set(); users with modify permissions: Set(wyatt); groups with modify permissions: Set() 25/06/08 17:05:07 INFO Utils: Successfully started service 'sparkDriver' on port 59361. 25/06/08 17:05:07 INFO SparkEnv: Registering MapOutputTracker 25/06/08 17:05:07 INFO SparkEnv: Registering BlockManagerMaster 25/06/08 17:05:08 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information 25/06/08 17:05:08 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up 25/06/08 17:05:08 INFO SparkEnv: Registering BlockManagerMasterHeartbeat 25/06/08 17:05:08 INFO DiskBlockManager: Created local directory at C:\Users\wyatt\AppData\Local\Temp\blockmgr-8fe065e2-024c-4e2f-8662-45d2fe3de444 25/06/08 17:05:08 INFO MemoryStore: MemoryStore started with capacity 1899.0 MiB 25/06/08 17:05:08 INFO SparkEnv: Registering OutputCommitCoordinator 25/06/08 17:05:08 INFO Utils: Successfully started service 'SparkUI' on port 4040. 25/06/08 17:05:08 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://windows10.microdone.cn:4040 25/06/08 17:05:08 INFO Executor: Starting executor ID driver on host windows10.microdone.cn 25/06/08 17:05:08 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 59392. 25/06/08 17:05:08 INFO NettyBlockTransferService: Server created on windows10.microdone.cn:59392 25/06/08 17:05:08 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy 25/06/08 17:05:08 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, windows10.microdone.cn, 59392, None) 25/06/08 17:05:08 INFO BlockManagerMasterEndpoint: Registering block manager windows10.microdone.cn:59392 with 1899.0 MiB RAM, BlockManagerId(driver, windows10.microdone.cn, 59392, None) 25/06/08 17:05:08 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, windows10.microdone.cn, 59392, None) 25/06/08 17:05:08 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, windows10.microdone.cn, 59392, None) Exception in thread "main" java.lang.IllegalArgumentException: 测试数据中不包含 features 列，请检查数据！ at car.LoadModelRideHailing$.main(LoadModelRideHailing.scala:23) at car.LoadModelRideHailing.main(LoadModelRideHailing.scala) 进程已结束，退出代码为 1 package car import org.apache.spark.ml.classification.{LogisticRegressionModel, RandomForestClassificationModel} import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.sql.{SparkSession, functions => F} object LoadModelRideHailing { def main(args: Array[String]): Unit = { val spark = SparkSession.builder() .master("local[3]") .appName("LoadModelRideHailing") .getOrCreate() spark.sparkContext.setLogLevel("Error") // 使用经过特征工程处理后的测试数据 val TestData = spark.read.option("header", "true").csv("C:\\Users\\wyatt\\Documents\\ride_hailing_test_data.csv") // 将 label 列转换为数值类型 val testDataWithNumericLabel = TestData.withColumn("label", F.col("label").cast("double")) // 检查 features 列是否存在 if (!testDataWithNumericLabel.columns.contains("features")) { throw new IllegalArgumentException("测试数据中不包含 features 列，请检查数据！") } // 修正后的模型路径（确保文件夹存在且包含元数据） val LogisticModel = LogisticRegressionModel.load("C:\\Users\\wyatt\\Documents\\ride_hailing_logistic_model") // 示例路径 val LogisticPre = LogisticModel.transform(testDataWithNumericLabel) val LogisticAcc = new MulticlassClassificationEvaluator() .setLabelCol("label") .setPredictionCol("prediction") .setMetricName("accuracy") .evaluate(LogisticPre) println("逻辑回归模型后期数据准确率：" + LogisticAcc) // 随机森林模型路径同步修正 val RandomForest = RandomForestClassificationModel.load("C:\\Users\\wyatt\\Documents\\ride_hailing_random_forest_model") // 示例路径 val RandomForestPre = RandomForest.transform(testDataWithNumericLabel) val RandomForestAcc = new MulticlassClassificationEvaluator() .setLabelCol("label") .setPredictionCol("prediction") .setMetricName("accuracy") .evaluate(RandomForestPre) println("随机森林模型后期数据准确率：" + RandomForestAcc) spark.stop() } }
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