Hadoop 调优

Yarn

CPU 信息查看

查看 cpu 型号：

cat /proc/cpuinfo | grep name | cut -f2 -d: | uniq -c

查看物理 CPU 数量:

cat /proc/cpuinfo| grep "physical id"| sort| uniq| wc -l

查看每个物理 cpu 中 核心数(core 数)：

cat /proc/cpuinfo | grep "cpu cores" | uniq

查看总的逻辑 cpu 数（processor 数）：

cat /proc/cpuinfo| grep "processor"| wc -l

生产环境核心参数

配置文件为 yarn-site.xml.

<!-- 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>40</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-->
<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>98304</value>
</property>

<!-- nodemanager的CPU核数，不按照硬件环境自动设定时默认是8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>12</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-->
<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>49152</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-->
<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>6</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>

容量调度器队列

配置文件为 capacity-scheduler.xml.

<!-- 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>

<!-- 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>

配置 Hive 任务指定 Hive 队列：

<!-- hive-3.1.2/conf/hive-site.xml -->
<property>
    <name>mapreduce.job.queuename</name>
    <value>hive</value>
</property>

Hive

Hive on Spark

针对 hive on spark 的配置，可以在两个地方，一个是在 Hive conf 目录中创建一个 spark-defaults.conf 文件进行配置；也可以直接在 Hive 的配置文件 hive-site.xml 进行配置，比如：

set spark.master=<Spark Master URL>
set spark.eventLog.enabled=true;
set spark.eventLog.dir=<Spark event log folder (must exist)>
set spark.executor.memory=512m;             
set spark.serializer=org.apache.spark.serializer.KryoSerializer;

属性	配置建议
spark.executor.cores	通常建议设置为 5、6 或者 7 ；还要参考yarn.nodemanager.resource.cpu-vcores的数量，如果为 19 ，则配置为 6 比较合适。
spark.executor.memory	yarn.nodemanager.resource.memory-mb * (spark.executor.cores / yarn.nodemanager.resource.cpu-vcores)
spark.yarn.executor.memoryOverhead	15-20% of spark.executor.memory
spark.executor.instances	Depends on spark.executor.memory + spark.yarn.executor.memoryOverhead

详情可参考 Hive on Spark: Getting Started - Apache Hive - Apache Software Foundation