Hadoop 3.x(生产调优手册)----【Hadoop综合调优】
- 1. Hadoop小文件优化方法
- 1. Hadoop小文件弊端
- 2. Hadoop小文件解决方法
- 2. 测试MapReduce计算性能
- 3. 企业开发场景案例
- 1. 需求
- 2. HDFS参数调优
- 3. MapReduce参数调优
- 4. Yarn参数调优
- 5. 执行程序
1. Hadoop小文件优化方法
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,使的寻址索引速度变慢。
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。
2. Hadoop小文件解决方法
- 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)
- 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 - 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 - 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
[fickler@hadoop102 ~]$ 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 http://hadoop103:8088/cluster
(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
[fickler@hadoop102 ~]$ xsync mapred-site.xml
(6)再次执行 wordcount 程序
[fickler@hadoop102 ~]$ 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 http://hadoop103:8088/cluster
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 来生产随机数,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
[fickler@hadoop102 ~]$ 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
[fickler@hadoop102 ~]$ 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
[fickler@hadoop102 ~]$ doop 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
3. 企业开发场景案例
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)
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
[fickler@hadoop102 ~]$ xsync hadoop-env.sh hdfs-site.xml core-site.xml
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 inferred from 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
[fickler@hadoop102 ~]$ xsync mapred-site.xml
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 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 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 allocated for containers. If set to -1 and yarn.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 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 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 an InvalidResourceRequestException.</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
[fickler@hadoop102 ~]$ xsync yarn-site.xml
5. 执行程序
(1)重启集群
[fickler@hadoop102 ~]$ sbin/stop-yarn.sh
[fickler@hadoop102 ~]$ sbin/start-yarn.sh
(2)执行 WordCount 程序
[fickler@hadoop102 ~]$ 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
http://hadoop103:8088/cluster/apps