Apache Hadoop 2.0.2-alpha

本文介绍了HDFS Federation的设计理念,包括其两层结构:Namespace和Block Storage。深入探讨了多个NameNode/命名空间如何提高水平扩展性和性能,以及如何实现应用间的隔离。此外,还详细说明了配置、格式化、集群管理和更新HDFS版本的具体步骤。

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原文出处http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/Federation.html

HDFSF分为2层 namespace和block storage

Namespace

         由目录,文件和快组成

         支持所有的namespace 文件相关的操作,例如增,删,改,查文件夹和文件

Block storage

         Blockmanager

提供数据节点的集群的登记和心跳。

                   报告和维护datanode

                   支持快的例如增删改查等的操作

                   管理复本空间(包括被复制的,复制的,以及删除的复本)

         Storage

                   Datanode为Storage提供存储块到本地文件系统,然后允许读写操作

以前的hdfs 架构,只允许一个集群存在一个namenode 。单个的namenode 管理所有的名字空间。hdfs Federation  突破了以前架构的限制,,增加多个的namenode/namespace.

 

 

Multiple Namenodes/Namespaces

 

为了水平的扩展name service . Federation 采用了多个独立的namenodes/namespaces.

Namenodes独立运行,不需要其他的namenode交互。Datanodes被所有的namenodes作为常用存储 使用。每个datanode  在集群中的所有namenode中都注册,datanodes每个一段时间发送心跳消息到namenodes 然后获取命令

Block Pool

一个blockpool是一组namespace 相同的block. 集群中的block pools公用所有的Datanodes  的存储块。他与其他blockpools中的blockpool 是相互独立的。每个namespace都可以为产生新的块,任何一个namenode的故障都不会影响集群中的其他namenode

       一个命名空间跟他的blockpool成为命名空间的卷。他是管理中的自包含单元,当一个namenode或者namespace被删除,对应的block pool也会被删除。集群升级的时候,每个namespace作为一个独立的单元来升级 。

 

ClusterID

       ClusterID 用来识别集群中的nodes,当namenode被格式化,这个标记被复制或者自动生成,。这个ID用于格式化其他namenodes

 

主要优点

Namespace 的可扩展性:

HDFS集群的存储可以水平扩展,但是namespace不可以。。小文件使用的大量增长得益于namespace扩展

 

性能: HDFS的吞吐量受限于以前的架构,增加更多的namenode可以水平扩展hdfs的吞吐量

Isolation (隔离):单个的namenode在多用户使用的环境中,无法做到隔离性,一个实验可以证明,重载namenode然后关键生产被减缓。使用多个namenod,不同类的应用和不同的用户被分配到不同的namespace

 

Federation Configuration(配置)

Federation 的配置 是兼容旧版本的,他允许不做任何配置修改的 单个的namenode运行。

为了支持单个配置文件,namenode跟相应的secondarynode/checkpoint/backup节点要用NameServiceID作为前缀,然后加到相同的配置文件中

 

配置

步骤1

 dfs.nameservices 配置NameServiceIDs,当datanode检测集群中的所有namenodes会使用到这个配置

所有的namenode必须要配置相应的SecondaryNamenode, BackupNode的配置如下表

 

Daemon

Configuration Parameter

Namenode

dfs.namenode.rpc-address dfs.namenode.servicerpc-address dfs.namenode.http-address dfs.namenode.https-address dfs.namenode.keytab.file dfs.namenode.name.dirdfs.namenode.edits.dir dfs.namenode.checkpoint.dir dfs.namenode.checkpoint.edits.dir

Secondary Namenode

dfs.namenode.secondary.http-address dfs.secondary.namenode.keytab.file

BackupNode

dfs.namenode.backup.address dfs.secondary.namenode.keytab.file

 

 

 

配置下面的参数,例子如下

 

<configuration>
  <property>
    <name>dfs.nameservices</name>
    <value>ns1,ns2</value>
  </property>
  <property>
    <name>dfs.namenode.rpc-address.ns1</name>
    <value>nn-host1:rpc-port</value>
  </property>
  <property>
    <name>dfs.namenode.http-address.ns1</name>
    <value>nn-host1:http-port</value>
  </property>
  <property>
    <name>dfs.namenode.secondaryhttp-address.ns1</name>
    <value>snn-host1:http-port</value>
  </property>
  <property>
    <name>dfs.namenode.rpc-address.ns2</name>
    <value>nn-host2:rpc-port</value>
  </property>
  <property>
    <name>dfs.namenode.http-address.ns2</name>
    <value>nn-host2:http-port</value>
  </property>
  <property>
    <name>dfs.namenode.secondaryhttp-address.ns2</name>
    <value>snn-host2:http-port</value>
  </property>

  .... Other common configuration ...
</configuration>

FormattingNamenodes格式化namenodes

格式化一个新的namenode

$HADOOP_PREFIX_HOME/bin/hdfsnamenode -format [-clusterId <cluster_id>]

选择一个集群ID以区别于你环境中的其他集群,如果不提供他会自己生成

 

格式化增加的namenode

$HADOOP_PREFIX_HOME/bin/hdfs namenode -format -clusterId <cluster_id>

clusterId必须跟前面的clusterId一样,否则新的namenode就不能被加到集群中来

 

更新HDFS版本到0.23

第一步:

更新版本到0.23,同时通过下面命令配置集群ID

$HADOOP_PREFIX_HOME/bin/hdfs start namenode --config $HADOOP_CONF_DIR  -upgrade -clusterId <cluster_ID>

如果cluster_ID没有给,他会自动生成

第二步:

增加一个新的namenode到已经存在的hdfs中

增加 dfs.nameservices配置

    使用NameServiceID前缀修改配置(dfs.namenode.rpc-address.ns1

    增加一个namenode相关配置(包括snn,backupnode)

    配置文件传播到集群中所有的节点

    更新所有的节点

         $HADOOP_PREFIX_HOME/bin/hdfs dfadmin -refreshNameNode <datanode_host_name>:<datanode_rpc_port>
上面的命令,必须对所有的数据节点在集群中运行。

 

   

管理集群:

启动与停止:

$HADOOP_PREFIX_HOME/bin/start-dfs.sh
$HADOOP_PREFIX_HOME/bin/stop-dfs.sh

上述命令可以在集群中的任何节点运行

 

Balancer(均衡器)

均衡器被修改成能在多namenodes的集群中工作。

我们使用如下命令运行均衡器:

"$HADOOP_PREFIX"/bin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script "$bin"/hdfs start balancer [-policy <policy>]

 

Policy可以是:

Node 是缺省策略, datanode级别的均衡,跟以前的架构策略是一样的

Blockpoolblock pool级别的均衡,并且在datanode级别的均衡

 

注意均衡只均衡数据,而不会均衡namespace

 

 

Decommissioning(从集群中去除datanode)

与以前的版本设计一样,需要去除的节点需要写到namenode的exclude文件中 。每个节点在 Block Pool分别解除那些节点。当所有的namenode完成这一过程,那么datanode可以认为已经去除了。

解除步骤:

第一步:

把设置去除节点的文件部署到所有的namenodes上,使用下面的命令

"$HADOOP_PREFIX"/bin/distributed-exclude.sh<exclude_file>

第二步:

刷新所有的namenodes 去获取exclude文件的设置

"$HADOOP_PREFIX"/bin/refresh-namenodes.sh

上述命令使用hdfs的配置去检测所有namenode的配置,刷新所有的namenode,去读关于exclude文件的信息。

 

Cluster Web Console

跟namenode 状态的web页面一样, 集群的Web Console被用于监视整个集群的状态

http://<any_nn_host:port>/dfsclusterhealth.jsp

所有的namenode都可以在这个页面被查看到

 

这个页面提供以下信息:

         1集群的概况,包括 文件数量,快数量,总配置存储容量,集群的可用和已用容量信息

 

2提供namenode的列表和概况,包括文件数,块数量,缺失快,该namenode上活动和非活动的datanode,他还提供一个可供访问NAMENODE WEB页面的链接

3 他还提供了移除的数据节点的状态

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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() } }
06-09
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