dolpdinscheduler里面选项参数driver memory怎么配置

在 DolphinScheduler 中，可以通过以下两种方式来配置 Driver Memory：

1. 在提交 DAG 时通过设置参数的方式进行配置，具体的参数为：`-d` 或者 `--driver-memory`，例如：

   ds-submit --projectName=project1 --file=/tmp/test_dag.py --d=2g

2. 在 DolphinScheduler 的全局配置中进行配置，具体的步骤为：

- 登录 DolphinScheduler 的 Web 页面，进入 `System Management -> Configuration Management` 页面；
- 在页面中找到 `Task Configuration` 配置项，点击编辑按钮；
- 在弹出的编辑页面中，找到 `Driver Memory` 配置项，并进行相应的配置即可。

需要注意的是，如果在 DAG 提交时设置了 `-d` 或者 `--driver-memory` 参数，则该参数的优先级高于全局配置中的配置。

相关问题

spark driver memory

Spark driver memory refers to the amount of memory allocated to the driver program in a Spark cluster. The driver program is responsible for coordinating and managing the execution of Spark jobs, and it runs on the master node of the cluster.

The driver memory is important because it determines how much data the driver program can hold in memory at any given time. If the driver memory is too low, the driver program may run out of memory and fail to execute the Spark jobs. Therefore, it is important to allocate enough memory to the driver program to ensure smooth and efficient execution of Spark jobs.

The default value for driver memory in Spark is 1g, but it can be increased or decreased based on the size of the cluster and the requirements of the Spark jobs. It is recommended to allocate at least 1-2 GB of memory to the driver program for small to medium-sized clusters, and up to 4-8 GB for larger clusters with more intensive workloads.

spark on yarn driver memory

The driver memory in Spark on YARN refers to the amount of memory allocated to the driver program that coordinates the execution of Spark tasks across the cluster.

The driver program is responsible for maintaining the SparkContext, which represents the entry point to the Spark cluster, and for managing the execution of Spark jobs. The driver memory is used to store the metadata required by the SparkContext and to buffer the results of Spark tasks, such as intermediate results of map and reduce operations.

The amount of driver memory required depends on the size of the Spark job and the resources available on the YARN cluster. Typically, the driver memory should be set to a value that is large enough to accommodate the metadata and intermediate results of the Spark job, but not so large that it causes YARN to run out of memory.

The driver memory can be configured in the Spark configuration file using the spark.driver.memory property, which specifies the amount of memory in MB or GB. For example, to set the driver memory to 4 GB, you can add the following line to the spark-defaults.conf file:

spark.driver.memory 4g