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Hadoop生产调优手册(下)

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前言:

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第8章 MapReduce生产经验

8.1 MapReduce跑的慢的原因

MapReduce程序效率的瓶颈在于两点:

1)计算机性能

CPU、内存、磁盘、网络

2)I/O操作优化

(1)数据倾斜

(2)Map运行时间太长,导致Reduce等待过久

(3)小文件过多

8.2 MapReduce常用调优参数8.3 MapReduce数据倾斜问题

1)数据倾斜现象

数据频率倾斜——某一个区域的数据量要远远大于其他区域。

数据大小倾斜——部分记录的大小远远大于平均值。

2)减少数据倾斜的方法

(1)首先检查是否空值过多造成的数据倾斜

生产环境,可以直接过滤掉空值;如果想保留空值,就自定义分区,将空值加随机数打散。最后再二次聚合。

(2)能在map阶段提前处理,最好先在Map阶段处理。如:Combiner、MapJoin

(3)设置多个reduce个数

第9章 Hadoop-Yarn生产经验

9.1 常用的调优参数

1)调优参数列表

(1)Resourcemanager相关

yarn.resourcemanager.scheduler.client.thread-count ResourceManager处理调度器请求的线程数量

yarn.resourcemanager.scheduler.class 配置调度器

(2)Nodemanager相关

yarn.nodemanager.resource.memory-mb NodeManager使用内存数

yarn.nodemanager.resource.system-reserved-memory-mb NodeManager为系统保留多少内存,和上一个参数二者取一即可

yarn.nodemanager.resource.cpu-vcores NodeManager使用CPU核数

yarn.nodemanager.resource.count-logical-processors-as-cores 是否将虚拟核数当作CPU核数

yarn.nodemanager.resource.pcores-vcores-multiplier 虚拟核数和物理核数乘数,例如:4核8线程,该参数就应设为2

yarn.nodemanager.resource.detect-hardware-capabilities 是否让yarn自己检测硬件进行配置

yarn.nodemanager.pmem-check-enabled 是否开启物理内存检查限制container

yarn.nodemanager.vmem-check-enabled 是否开启虚拟内存检查限制container

yarn.nodemanager.vmem-pmem-ratio 虚拟内存物理内存比例

(3)Container容器相关

yarn.scheduler.minimum-allocation-mb 容器最小内存

yarn.scheduler.maximum-allocation-mb 容器最大内存

yarn.scheduler.minimum-allocation-vcores 容器最小核数

yarn.scheduler.maximum-allocation-vcores 容器最大核数

第10章 Hadoop综合调优

10.1 Hadoop小文件优化方法10.1.1 Hadoop小文件弊端

HDFS上每个文件都要在NameNode上创建对应的元数据,这个元数据的大小约为150byte,这样当小文件比较多的时候,就会产生很多的元数据文件,一方面会大量占用NameNode的内存空间另一方面就是元数据文件过多,使得寻址索引速度变慢。

小文件过多,在进行MR计算时,会生成过多切片,需要启动过多的MapTask。每个MapTask处理的数据量小,导致MapTask的处理时间比启动时间还小,白白消耗资源。

10.1.2 Hadoop小文件解决方案

1)在数据采集的时候,就将小文件或小批数据合成大文件再上传HDFS(数据源头)

2)Hadoop Archive(存储方向)

是一个高效的将小文件放入HDFS块中的文件存档工具,能够将多个小文件打包成一个HAR文件,从而达到减少NameNode的内存使用

3)CombineTextInputFormat(计算方向)

CombineTextInputFormat用于将多个小文件在切片过程中生成一个单独的切片或者少量的切片。

4)开启uber模式,实现JVM重用(计算方向)

默认情况下,每个Task任务都需要启动一个JVM来运行,如果Task任务计算的数据量很小,我们可以让同一个Job的多个Task运行在一个JVM中,不必为每个Task都开启一个JVM。

(1)未开启uber模式,在/input路径上上传多个小文件并执行wordcount程序

[root@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output2

(2)观察控制台

2021-02-14 16:13:50,607 INFO mapreduce.Job: Job job_1613281510851_0002 running in uber mode : false

(3)观察

(4)开启uber模式,在mapred-site.xml中添加如下配置

<!-- 开启uber模式,默认关闭 --><property><name>mapreduce.job.ubertask.enable</name><value>true</value></property><!-- uber模式中最大的mapTask数量,可向下修改 --><property><name>mapreduce.job.ubertask.maxmaps</name><value>9</value></property><!-- uber模式中最大的reduce数量,可向下修改 --><property><name>mapreduce.job.ubertask.maxreduces</name><value>1</value></property><!-- uber模式中最大的输入数据量,默认使用dfs.blocksize 的值,可向下修改 --><property><name>mapreduce.job.ubertask.maxbytes</name><value></value></property>

(5)分发配置

[root@hadoop102 hadoop]$ xsync mapred-site.xml

(6)再次执行wordcount程序

[root@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output2

(7)观察控制台

2021-02-14 16:28:36,198 INFO mapreduce.Job: Job job_1613281510851_0003 running in uber mode : true

(8)观察

10.2 测试MapReduce计算性能

使用Sort程序评测MapReduce

注:一个虚拟机不超过150G磁盘尽量不要执行这段代码

(1)使用RandomWriter来产生随机数,每个节点运行10个Map任务,每个Map产生大约1G大小的二进制随机数

[root@hadoop102 mapreduce]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar randomwriter random-data

(2)执行Sort程序

[root@hadoop102 mapreduce]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar sort random-data sorted-data

(3)验证数据是否真正排好序了

[root@hadoop102 mapreduce]$hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-3.1.3-tests.jar testmapredsort -sortInput random-data -sortOutput sorted-data
10.3 企业开发场景案例10.3.1 需求

(1)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。

(2)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)

10.3.2 HDFS参数调优

(1)修改:hadoop-env.sh

export HDFS_NAMENODE_OPTS="-Dhadoop.security.logger=INFO,RFAS -Xmx1024m"export HDFS_DATANODE_OPTS="-Dhadoop.security.logger=ERROR,RFAS -Xmx1024m"

(2)修改hdfs-site.xml

<!-- NameNode有一个工作线程池,默认值是10 --><property><name>dfs.namenode.handler.count</name><value>21</value></property>

(3)修改core-site.xml

<!-- 配置垃圾回收时间为60分钟 --><property><name>fs.trash.interval</name><value>60</value></property>

(4)分发配置

[root@hadoop102 hadoop]$ xsync hadoop-env.sh hdfs-site.xml core-site.xml
10.3.3 MapReduce参数调优

(1)修改mapred-site.xml

<!-- 环形缓冲区大小,默认100m --><property><name>mapreduce.task.io.sort.mb</name><value>100</value></property><!-- 环形缓冲区溢写阈值,默认0.8 --><property><name>mapreduce.map.sort.spill.percent</name><value>0.80</value></property><!-- merge合并次数,默认10个 --><property><name>mapreduce.task.io.sort.factor</name><value>10</value></property><!-- maptask内存,默认1g; maptask堆内存大小默认和该值大小一致mapreduce.map.java.opts --><property><name>mapreduce.map.memory.mb</name><value>-1</value><description>The amount of memory to request from the scheduler for each map task. If this is not specified or is non-positive, it is inferred from mapreduce.map.java.opts and mapreduce.job.heap.memory-mb.ratio. If java-opts are also not specified, we set it to 1024.</description></property><!-- matask的CPU核数,默认1个 --><property><name>mapreduce.map.cpu.vcores</name><value>1</value></property><!-- matask异常重试次数,默认4次 --><property><name>mapreduce.map.maxattempts</name><value>4</value></property><!-- 每个Reduce去Map中拉取数据的并行数。默认值是5 --><property><name>mapreduce.reduce.shuffle.parallelcopies</name><value>5</value></property><!-- Buffer大小占Reduce可用内存的比例,默认值0.7 --><property><name>mapreduce.reduce.shuffle.input.buffer.percent</name><value>0.70</value></property><!-- Buffer中的数据达到多少比例开始写入磁盘,默认值0.66。 --><property><name>mapreduce.reduce.shuffle.merge.percent</name><value>0.66</value></property><!-- reducetask内存,默认1g;reducetask堆内存大小默认和该值大小一致mapreduce.reduce.java.opts --><property><name>mapreduce.reduce.memory.mb</name><value>-1</value><description>The amount of memory to request from the scheduler for each reduce task. If this is not specified or is non-positive, it is inferredfrom mapreduce.reduce.java.opts and mapreduce.job.heap.memory-mb.ratio.If java-opts are also not specified, we set it to 1024.</description></property><!-- reducetask的CPU核数,默认1个 --><property><name>mapreduce.reduce.cpu.vcores</name><value>2</value></property><!-- reducetask失败重试次数,默认4次 --><property><name>mapreduce.reduce.maxattempts</name><value>4</value></property><!-- 当MapTask完成的比例达到该值后才会为ReduceTask申请资源。默认是0.05 --><property><name>mapreduce.job.reduce.slowstart.completedmaps</name><value>0.05</value></property><!-- 如果程序在规定的默认10分钟内没有读到数据,将强制超时退出 --><property><name>mapreduce.task.timeout</name><value>600000</value></property>

(2)分发配置

[root@hadoop102 hadoop]$ xsync mapred-site.xml
10.3.4 Yarn参数调优

(1)修改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 asmemory 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 ashyperthreads) should be counted as cores. Only applicable on Linuxwhen yarn.nodemanager.resource.cpu-vcores is set to -1 andyarn.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 tovcores. This value is used if yarn.nodemanager.resource.cpu-vcoresis set to -1(which implies auto-calculate vcores) andyarn.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 allocatedfor containers. If set to -1 andyarn.nodemanager.resource.detect-hardware-capabilities is true, it isautomatically 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 allocatedfor containers. This is used by the RM scheduler when allocatingresources for containers. This is not used to limit the number ofCPUs used by YARN containers. If it is set to -1 andyarn.nodemanager.resource.detect-hardware-capabilities is true, it isautomatically 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 the RM 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 anInvalidResourceRequestException.</description><name>yarn.scheduler.maximum-allocation-vcores</name><value>2</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 forcontainers.</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>

(2)分发配置

[root@hadoop102 hadoop]$ xsync yarn-site.xml
10.3.5 执行程序

(1)重启集群

[root@hadoop102 hadoop-3.1.3]$ sbin/stop-yarn.sh[root@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh

(2)执行WordCount程序

[root@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output

(3)观察Yarn任务执行页面

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