yarnConfiguration
时间: 2024-07-04 14:01:06 浏览: 71
"yarnConfiguration"通常指的是YARN(Yet Another Resource Negotiator)集群管理器中的配置参数。YARN是Hadoop生态系统中的一个重要组件,它负责资源管理和调度工作负载在分布式计算环境中。
在YARN中,`yarnConfiguration`可以包括各种配置项,如内存分配、队列设置、应用程序策略等。这些配置允许管理员调整YARN的行为以适应特定的环境需求。以下是一些常见的配置示例:
1. `yarn.nodemanager.resource.memory-mb`:设置每个节点上的内存限制。
2. `yarn.scheduler.capacity.root.QueueA.capacity`:定义队列的资源份额。
3. `yarn.resourcemanager.address`:ResourceManager的主地址。
4. `yarn.resourcemanager.hostname`:ResourceManager运行的主机名。
要查看或修改这些配置,通常会在Hadoop的配置目录(如`/etc/hadoop/conf`)下的`yarn-site.xml`文件中进行。管理员可以通过编辑此文件,或使用`hadoop-config.sh`工具来调整YARN的配置。
请注意,具体配置可能因版本和环境而异。在实际操作时,建议查阅官方文档或根据实际需求进行配置。
相关问题
yarnconfiguration
YarnConfiguration是Hadoop YARN(Yet Another Resource Negotiator)的配置类。它提供了访问和管理YARN集群配置属性的方法。您可以使用YarnConfiguration类来设置和获取与YARN相关的配置属性,例如ResourceManager地址、节点管理器地址、队列配置等。
您可以通过以下步骤使用YarnConfiguration类:
1. 导入YarnConfiguration类:
```java
import org.apache.hadoop.yarn.conf.YarnConfiguration;
```
2. 创建YarnConfiguration实例:
```java
YarnConfiguration conf = new YarnConfiguration();
```
3. 设置或获取配置属性值:
```java
// 设置ResourceManager地址
conf.set("yarn.resourcemanager.address", "localhost:8032");
// 获取ResourceManager地址
String rmAddress = conf.get("yarn.resourcemanager.address");
```
通过YarnConfiguration,您可以自定义YARN集群的各种属性,以满足您的需求。它还提供了一些默认配置属性,您可以根据需要进行修改。
请注意,以上代码示例仅用于说明目的,并非完整的可运行代码。在实际使用中,您需要根据您的环境和要求进行适当的配置和使用。
yarn pyspark
Yarn is a cluster management tool used in Apache Hadoop for resource management and job scheduling. PySpark, on the other hand, is the Python API for Apache Spark, a fast and general-purpose cluster computing system. To use PySpark with Yarn, you need to set up and configure your Spark cluster to work with Yarn.
Here are the basic steps to run PySpark on Yarn:
1. Install Apache Spark: Download and install Apache Spark on your machine or cluster.
2. Configure Spark: Edit the `spark-defaults.conf` file in the Spark configuration directory. Set the `spark.master` property to `yarn` and specify other necessary configurations like memory allocation and number of executor cores.
3. Start Yarn: Make sure Yarn is running on your cluster. You can start it using the command `start-yarn.sh`.
4. Submit PySpark Job: Use the `spark-submit` command to submit your PySpark script to the Yarn cluster. For example:
```
spark-submit --master yarn --deploy-mode client my_script.py
```
Replace `my_script.py` with the path to your PySpark script.
This will submit your PySpark job to Yarn, and it will be executed on the cluster.
Note that these are just the basic steps, and there may be additional configurations depending on your specific setup and requirements. It's recommended to refer to the official Apache Spark documentation for detailed instructions on setting up PySpark with Yarn.