目录

1 需求

2 HDFS参数调优

（1）修改：hadoop-env.sh

（2）修改hdfs-site.xml

（3）修改core-site.xml

（4）分发配置

3 MapReduce参数调优

（1）修改mapred-site.xml

（2）分发配置

4 Yarn参数调优

（1）修改yarn-site.xml配置参数如下

（2）分发配置

5 执行程序

（1）重启集群

（2）执行WordCount程序

（3）观察Yarn任务执行页面

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

dfs.namenode.handler.count21

（3）修改core-site.xml

fs.trash.interval60

（4）分发配置

xsync hadoop-env.sh hdfs-site.xml core-site.xml

3 MapReduce参数调优

（1）修改mapred-site.xml

mapreduce.task.io.sort.mb100mapreduce.map.sort.spill.percent0.80mapreduce.task.io.sort.factor10mapreduce.map.memory.mb-1The 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.mapreduce.map.cpu.vcores1mapreduce.map.maxattempts4mapreduce.reduce.shuffle.parallelcopies5mapreduce.reduce.shuffle.input.buffer.percent0.70mapreduce.reduce.shuffle.merge.percent0.66mapreduce.reduce.memory.mb-1The 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.mapreduce.reduce.cpu.vcores2mapreduce.reduce.maxattempts4mapreduce.job.reduce.slowstart.completedmaps0.05mapreduce.task.timeout600000

（2）分发配置

xsync mapred-site.xml

4 Yarn参数调优

（1）修改yarn-site.xml配置参数如下

The class to use as the resource scheduler.yarn.resourcemanager.scheduler.classorg.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulerNumber of threads to handle scheduler interface.yarn.resourcemanager.scheduler.client.thread-count8Enable auto-detection of node capabilities such asmemory and CPU.yarn.nodemanager.resource.detect-hardware-capabilitiesfalseFlag 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.yarn.nodemanager.resource.count-logical-processors-as-coresfalseMultiplier 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.yarn.nodemanager.resource.pcores-vcores-multiplier1.0Amount 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.yarn.nodemanager.resource.memory-mb4096Number 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.yarn.nodemanager.resource.cpu-vcores4The 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.yarn.scheduler.minimum-allocation-mb1024The maximum allocation for every container request at the RM  in MBs. Memory requests higher than this will throw an    InvalidResourceRequestException.yarn.scheduler.maximum-allocation-mb2048The 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.yarn.scheduler.minimum-allocation-vcores1The maximum allocation for every container request at the RM  in terms of virtual CPU cores. Requests higher than this will throw anInvalidResourceRequestException.yarn.scheduler.maximum-allocation-vcores2Whether virtual memory limits will be enforced forcontainers.yarn.nodemanager.vmem-check-enabledfalseRatio 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.yarn.nodemanager.vmem-pmem-ratio2.1

（2）分发配置

xsync yarn-site.xml

5 执行程序

（1）重启集群

sbin/stop-yarn.shsbin/start-yarn.sh

（2）执行WordCount程序

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