1、Yarn资源调度器
Yarn是一个资源调度平台，负责为运算程序提供服务器运算资源，相当于一个分布式的操作系统平台，而MapReduce等运算程序则相当于运行于操作系统之上的应用程序
1.1 Yarn基础架构
YARN主要由ResourceManager、NodeManager、ApplicationMaster和Container等组件构成
1.2 Yarn工作机制
1.3 Yarn调度器和调度算法
目前，Hadoop作业调度器主要有三种：FIFO、容量（Capacity Scheduler）和公平（Fair Scheduler）。Apache Hadoop3.1.3默认的资源调度器是Capacity Scheduler(具体设置详见：yarn-default.xml文件)，CDH框架默认调度器是Fair Scheduler
先进先出调度器（FIFO）
FIFO调度器（First In First Out）：单队列，根据提交作业的先后顺序，先来先服务
容量调度器（Capacity Scheduler）
Capacity Scheduler 是 Yahoo 开发的多用户调度器
公平调度器（Fair Scheduler）
Fair Schedulere 是 Facebook 开发的多用户调度器
公平调度器设计目标是：在时间尺度上，所有作业获得公平的资源。某一
时刻一个作业应获资源和实际获取资源的差距叫"缺额"。调度器会优先为缺额大的作业分配资源
DRF策略：DRF（Dominant Resource Fairness），我们之前说的资源，都是单一标准，例如只考虑内存（也是Yarn默认的情况）。但是很多时候我们资源有很多种，例如内存，CPU，网络带宽等，这样我们很难衡量两个应用应该分配的资源比例。
1.4 Yarn 常用命令
# yarn状态的查询，除了可以在hadoop103:8088页面查看外，还可以通过命令操作
# 先运行
myhadoop.sh start
hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output
# =======================yarn application查看任务==============
# 列出所有Application
yarn application -list
# 根据Application状态过滤：yarn application -list -appStates （所有状态：ALL、NEW、NEW_SAVING、SUBMITTED、ACCEPTED、RUNNING、FINISHED、FAILED、KILLED）
yarn application -list -appStates FINISHED
# Kill掉Application
yarn application -kill application_1612577921195_0001
# ====================yarn logs查看日志======================
# 查询Application日志：yarn logs -applicationId <ApplicationId>
yarn logs -applicationId application_1612577921195_0001
# 查询Container日志：yarn logs -applicationId <ApplicationId> -containerId <ContainerId>
yarn logs -applicationId application_1612577921195_0001 -containerId container_1612577921195_0001_01_000001
# ====================yarn applicationattempt查看尝试运行的任务=====
# 列出所有Application尝试的列表：yarn applicationattempt -list <ApplicationId>
yarn applicationattempt -list application_1612577921195_0001
# 打印ApplicationAttemp状态：yarn applicationattempt -status <ApplicationAttemptId>
yarn applicationattempt -status appattempt_1612577921195_0001_000001
# =====================yarn container查看容器===============
# 列出所有Container：yarn container -list <ApplicationAttemptId>
yarn container -list appattempt_1612577921195_0001_000001
# 打印Container状态： yarn container -status <ContainerId>
# 注：只有在任务跑的途中才能看到container的状态
yarn container -status container_1612577921195_0001_01_000001
# ==========================yarn rmadmin更新配置==============
# 加载队列配置：yarn rmadmin -refreshQueues
yarn rmadmin -refreshQueues
# =======================yarn queue查看队列====================
# 打印队列信息：yarn queue -status <QueueName>
yarn queue -status default
1.5 Yarn 生产环境核心参数
2、Yarn 案例实操
注：调整下列参数之前尽量拍摄 Linux 快照，否则后续的案例，还需要重写准备集群
2.1 Yarn生产环境核心参数配置案例
需求：从1G数据中，统计每个单词出现次数。服务器3台，每台配置4G内存，4核CPU，4线程。需求分析：1G / 128m = 8个MapTask；1个ReduceTask；1个mrAppMaster，平均每个节点运行10个 / 3台 ≈ 3个任务（4 3 3），所以要改yarn-site.xml配置参数如下
<!-- 选择调度器，默认容量 -->
<property>
<description>The class to use as the resource scheduler.</description>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
</property>
<!-- ResourceManager处理调度器请求的线程数量,默认50；如果提交的任务数大于50，可以增加该值，但是不能超过3台 * 4线程 = 12线程（去除其他应用程序实际不能超过8） -->
<property>
<description>Number of threads to handle scheduler interface.</description>
<name>yarn.resourcemanager.scheduler.client.thread-count</name>
<value>8</value>
</property>
<!-- 是否让yarn自动检测硬件进行配置，默认是false，如果该节点有很多其他应用程序，建议手动配置。如果该节点没有其他应用程序，可以采用自动 -->
<property>
<description>Enable auto-detection of node capabilities such as
memory and CPU.
</description>
<name>yarn.nodemanager.resource.detect-hardware-capabilities</name>
<value>false</value>
</property>
<!-- 是否将虚拟核数当作CPU核数，默认是false，采用物理CPU核数 -->
<property>
<description>Flag to determine if logical processors(such as
hyperthreads) should be counted as cores. Only applicable on Linux
when yarn.nodemanager.resource.cpu-vcores is set to -1 and
yarn.nodemanager.resource.detect-hardware-capabilities is true.
</description>
<name>yarn.nodemanager.resource.count-logical-processors-as-cores</name>
<value>false</value>
</property>
<!-- 虚拟核数和物理核数乘数，默认是1.0 -->
<property>
<description>Multiplier to determine how to convert phyiscal cores to
vcores. This value is used if yarn.nodemanager.resource.cpu-vcores
is set to -1(which implies auto-calculate vcores) and
yarn.nodemanager.resource.detect-hardware-capabilities is set to true. The number of vcores will be calculated as number of CPUs * multiplier.
</description>
<name>yarn.nodemanager.resource.pcores-vcores-multiplier</name>
<value>1.0</value>
</property>
<!-- NodeManager使用内存数，默认8G，修改为4G内存 -->
<property>
<description>Amount of physical memory, in MB, that can be allocated
for containers. If set to -1 and
yarn.nodemanager.resource.detect-hardware-capabilities is true, it is
automatically calculated(in case of Windows and Linux).
In other cases, the default is 8192MB.
</description>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>4096</value>
</property>
<!-- nodemanager的CPU核数，不按照硬件环境自动设定时默认是8个，修改为4个 -->
<property>
<description>Number of vcores that can be allocated
for containers. This is used by the RM scheduler when allocating
resources for containers. This is not used to limit the number of
CPUs used by YARN containers. If it is set to -1 and
yarn.nodemanager.resource.detect-hardware-capabilities is true, it is
automatically determined from the hardware in case of Windows and Linux.
In other cases, number of vcores is 8 by default.</description>
<name>yarn.nodemanager.resource.cpu-vcores</name>
<value>4</value>
</property>
<!-- 容器最小内存，默认1G -->
<property>
<description>The minimum allocation for every container request at theRM in MBs. Memory requests lower than this will be set to the value of this property. Additionally, a node manager that is configured to have less memory than this value will be shut down by the resource manager.
</description>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>1024</value>
</property>
<!-- 容器最大内存，默认8G，修改为2G -->
<property>
<description>The maximum allocation for every container request at the RM in MBs. Memory requests higher than this will throw an InvalidResourceRequestException.
</description>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>2048</value>
</property>
<!-- 容器最小CPU核数，默认1个 -->
<property>
<description>The minimum allocation for every container request at the RM in terms of virtual CPU cores. Requests lower than this will be set to the value of this property. Additionally, a node manager that is configured to have fewer virtual cores than this value will be shut down by the resource manager.
</description>
<name>yarn.scheduler.minimum-allocation-vcores</name>
<value>1</value>
</property>
<!-- 容器最大CPU核数，默认4个，修改为2个 -->
<property>
<description>The maximum allocation for every container request at the RM in terms of virtual CPU cores. Requests higher than this will throw an
InvalidResourceRequestException.</description>
<name>yarn.scheduler.maximum-allocation-vcores</name>
<value>2</value>
</property>
<!-- yarn对物理内存默认打开，建议打开 -->
<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>true</value>
</property>
<!-- 虚拟内存检查，默认打开，修改为关闭 -->
<property>
<description>Whether virtual memory limits will be enforced for
containers.</description>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
<!-- 虚拟内存和物理内存设置比例,默认2.1 -->
<property>
<description>Ratio between virtual memory to physical memory when setting memory limits for containers. Container allocations are expressed in terms of physical memory, and virtual memory usage is allowed to exceed this allocation by this ratio.
</description>
<name>yarn.nodemanager.vmem-pmem-ratio</name>
<value>2.1</value>
</property>
如果集群的硬件资源不一致，要每个NodeManager单独配置
# 重启集群
sbin/stop-yarn.sh
sbin/start-yarn.sh
# 执行WordCount程序
hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output
# http://hadoop103:8088/cluster/apps
2.2 容量调度器多队列提交案例
- 需求1：default队列占总内存的40%，最大资源容量占总资源60%，hive队列占总内存的60%，最大资源容量占总资源80%
- 需求2：配置队列优先级
在capacity-scheduler.xml中配置如下
<!--为新加队列添加必要属性-->
<!-- 指定多队列，增加hive队列 -->
<property>
<name>yarn.scheduler.capacity.root.queues</name>
<value>default,hive</value>
<description>
The queues at the this level (root is the root queue).
</description>
</property>
<!-- 降低default队列资源额定容量为40%，默认100% -->
<property>
<name>yarn.scheduler.capacity.root.default.capacity</name>
<value>40</value>
</property>
<!-- 降低default队列资源最大容量为60%，默认100% -->
<property>
<name>yarn.scheduler.capacity.root.default.maximum-capacity</name>
<value>60</value>
</property>
<!------------------------为新加队列添加必要属性------------------------->
<!-- 指定hive队列的资源额定容量 -->
<property>
<name>yarn.scheduler.capacity.root.hive.capacity</name>
<value>60</value>
</property>
<!-- 用户最多可以使用队列多少资源，1表示 -->
<property>
<name>yarn.scheduler.capacity.root.hive.user-limit-factor</name>
<value>1</value>
</property>
<!-- 指定hive队列的资源最大容量 -->
<property>
<name>yarn.scheduler.capacity.root.hive.maximum-capacity</name>
<value>80</value>
</property>
<!-- 启动hive队列 -->
<property>
<name>yarn.scheduler.capacity.root.hive.state</name>
<value>RUNNING</value>
</property>
<!-- 哪些用户有权向队列提交作业 -->
<property>
<name>yarn.scheduler.capacity.root.hive.acl_submit_applications</name>
<value>*</value>
</property>
<!-- 哪些用户有权操作队列，管理员权限（查看/杀死） -->
<property>
<name>yarn.scheduler.capacity.root.hive.acl_administer_queue</name>
<value>*</value>
</property>
<!-- 哪些用户有权配置提交任务优先级 -->
<property>
<name>yarn.scheduler.capacity.root.hive.acl_application_max_priority</name>
<value>*</value>
</property>
<!-- 任务的超时时间设置：yarn application -appId appId -updateLifetime Timeout
参考资料：https://blog.cloudera.com/enforcing-application-lifetime-slas-yarn/ -->
<!-- 如果application指定了超时时间，则提交到该队列的application能够指定的最大超时时间不能超过该值。
-->
<property>
<name>yarn.scheduler.capacity.root.hive.maximum-application-lifetime</name>
<value>-1</value>
</property>
<!-- 如果application没指定超时时间，则用default-application-lifetime作为默认值 -->
<property>
<name>yarn.scheduler.capacity.root.hive.default-application-lifetime</name>
<value>-1</value>
</property>
分发配置文件，重启Yarn或者执行yarn rmadmin -refreshQueues刷新队列，就可以看到两条队列，然后像Hive提交任务
# 向Hive队列提交任务
hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -D mapreduce.job.queuename=hive /input /output
# 打jar包的方式,默认的任务提交都是提交到default队列的。如果希望向其他队列提交任务，需要在Driver中声明
public class WcDrvier {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
conf.set("mapreduce.job.queuename","hive");
//1. 获取一个Job实例
Job job = Job.getInstance(conf);
。。。 。。。
//6. 提交Job
boolean b = job.waitForCompletion(true);
System.exit(b ? 0 : 1);
}
}
最后说一下任务优先级，容量调度器，支持任务优先级的配置，在资源紧张时，优先级高的任务将优先获取资源。默认情况，Yarn将所有任务的优先级限制为0，若想使用任务的优先级功能，须开放该限制，修改yarn-site.xml文件，增加以下参数
<property>
<name>yarn.cluster.max-application-priority</name>
<value>5</value>
</property>
# 分发配置，并重启Yarn
xsync yarn-site.xml
sbin/stop-yarn.sh
sbin/start-yarn.sh
# 模拟资源紧张环境，可连续提交以下任务，直到新提交的任务申请不到资源为止
hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 5 2000000
# 再次重新提交优先级高的任务
hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi -D mapreduce.job.priority=5 5 2000000
# 也可以通过以下命令修改正在执行的任务的优先级
# yarn application -appID <ApplicationID> -updatePriority 优先级
yarn application -appID application_1611133087930_0009 -updatePriority 5
2.3 公平调度器案例
配置文件参考资料：https://hadoop.apache.org/docs/r3.1.3/hadoop-yarn/hadoop-yarn-site/FairScheduler.html
任务队列放置规则参考资料：https://blog.cloudera.com/untangling-apache-hadoop-yarn-part-4-fair-scheduler-queue-basics/
创建两个队列，分别是test和atguigu（以用户所属组命名）。期望实现以下效果：若用户提交任务时指定队列，则任务提交到指定队列运行；若未指定队列，test用户提交的任务到root.group.test队列运行，atguigu提交的任务到root.group.atguigu队列运行（注：group为用户所属组）。公平调度器的配置涉及到两个文件，一个是yarn-site.xml，另一个是公平调度器队列分配文件fair-scheduler.xml（文件名可自定义）。
修改yarn-site.xml文件，加入以下参数
<property>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
<description>配置使用公平调度器</description>
</property>
<property>
<name>yarn.scheduler.fair.allocation.file</name>
<value>/opt/module/hadoop-3.1.3/etc/hadoop/fair-scheduler.xml</value>
<description>指明公平调度器队列分配配置文件</description>
</property>
<property>
<name>yarn.scheduler.fair.preemption</name>
<value>false</value>
<description>禁止队列间资源抢占</description>
</property>
配置fair-scheduler.xml
<?xml version="1.0"?>
<allocations>
<!-- 单个队列中Application Master占用资源的最大比例,取值0-1 ，企业一般配置0.1 -->
<queueMaxAMShareDefault>0.5</queueMaxAMShareDefault>
<!-- 单个队列最大资源的默认值 test atguigu default -->
<queueMaxResourcesDefault>4096mb,4vcores</queueMaxResourcesDefault>
<!-- 增加一个队列test -->
<queue name="test">
<!-- 队列最小资源 -->
<minResources>2048mb,2vcores</minResources>
<!-- 队列最大资源 -->
<maxResources>4096mb,4vcores</maxResources>
<!-- 队列中最多同时运行的应用数，默认50，根据线程数配置 -->
<maxRunningApps>4</maxRunningApps>
<!-- 队列中Application Master占用资源的最大比例 -->
<maxAMShare>0.5</maxAMShare>
<!-- 该队列资源权重,默认值为1.0 -->
<weight>1.0</weight>
<!-- 队列内部的资源分配策略 -->
<schedulingPolicy>fair</schedulingPolicy>
</queue>
<!-- 增加一个队列atguigu -->
<queue name="atguigu" type="parent">
<!-- 队列最小资源 -->
<minResources>2048mb,2vcores</minResources>
<!-- 队列最大资源 -->
<maxResources>4096mb,4vcores</maxResources>
<!-- 队列中最多同时运行的应用数，默认50，根据线程数配置 -->
<maxRunningApps>4</maxRunningApps>
<!-- 队列中Application Master占用资源的最大比例,maxAMShare只能用于叶子队列 -->
<!-- 该队列资源权重,默认值为1.0 -->
<weight>1.0</weight>
<!-- 队列内部的资源分配策略 -->
<schedulingPolicy>fair</schedulingPolicy>
</queue>
<!-- 任务队列分配策略,可配置多层规则,从第一个规则开始匹配,直到匹配成功 -->
<queuePlacementPolicy>
<!-- 提交任务时指定队列,如未指定提交队列,则继续匹配下一个规则; false表示：如果指定队列不存在,不允许自动创建-->
<rule name="specified" create="false"/>
<!-- 提交到root.group.username队列,若root.group不存在,不允许自动创建；若root.group.user不存在,允许自动创建 -->
<rule name="nestedUserQueue" create="true">
<rule name="primaryGroup" create="false"/>
</rule>
<!-- 最后一个规则必须为reject或者default。Reject表示拒绝创建提交失败，default表示把任务提交到default队列 -->
<rule name="reject" />
</queuePlacementPolicy>
</allocations>
分发并测试提交
xsync yarn-site.xml
xsync fair-scheduler.xml
sbin/stop-yarn.sh
sbin/start-yarn.sh
# 提交任务时指定队列，按照配置规则，任务会到指定的root.test队列
hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi -Dmapreduce.job.queuename=root.test 1 1
# 提交任务时不指定队列，按照配置规则，任务会到root.atguigu.atguigu队列
hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 1 1
2.4 Yarn的Tool接口案例
自己写的jar包期望可以动态传参，结果报错，误认为是第一个输入参数，解决方法编写 Yarn 的 Tool 接口，首先编写maven项目，导包
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>3.1.3</version>
</dependency>
</dependencies>
//创建类WordCount并实现Tool接口
public class WordCount implements Tool {
private Configuration conf;
@Override
public int run(String[] args) throws Exception {
Job job = Job.getInstance(conf);
job.setJarByClass(WordCountDriver.class);
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
return job.waitForCompletion(true) ? 0 : 1;
}
@Override
public void setConf(Configuration conf) {
this.conf = conf;
}
@Override
public Configuration getConf() {
return conf;
}
public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private Text outK = new Text();
private IntWritable outV = new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
String[] words = line.split(" ");
for (String word : words) {
outK.set(word);
context.write(outK, outV);
}
}
}
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable outV = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
outV.set(sum);
context.write(key, outV);
}
}
}
//新建WordCountDriver
public class WordCountDriver {
private static Tool tool;
public static void main(String[] args) throws Exception {
// 1. 创建配置文件
Configuration conf = new Configuration();
// 2. 判断是否有tool接口
switch (args[0]){
case "wordcount":
tool = new WordCount();
break;
default:
throw new RuntimeException(" No such tool: "+ args[0] );
}
// 3. 用Tool执行程序
// 用这个方法可以过滤-D
// Arrays.copyOfRange 将老数组的元素放到新数组里面
int run = ToolRunner.run(conf, tool, Arrays.copyOfRange(args, 1, args.length));
System.exit(run);
}
}
在HDFS上准备输入文件，假设为/input目录，向集群提交该Jar包yarn jar YarnDemo.jar com.atguigu.yarn.WordCountDriver wordcount /input /output注意此时提交的3个参数，第一个用于生成特定的Tool，第二个和第三个为输入输出目录。此时如果我们希望加入设置参数，可以在wordcount后面添加参数，例如：yarn jar YarnDemo.jar com.atguigu.yarn.WordCountDriver wordcount -Dmapreduce.job.queuename=root.test /input /output1