Yarn-Client Mode in Apache Spark
YARN (Yet Another Resource Negotiator) is one of the cluster managers available for Apache Spark, introduced in Hadoop 2.x. It allocates resources and schedules jobs across a cluster dynamically. Spark leverages YARN for resource management and job scheduling, as a distributed computing framework on top of Hadoop’s HDFS (Hadoop Distributed File System).
When running Spark on YARN, you generally have two modes to choose from: YARN-Client mode and YARN-Cluster mode. In this explanation, we’ll dive into what YARN-Client mode is and how it operates.
Definition
In YARN-Client mode, the driver program (the main program that coordinates all the executors) runs on the machine where you trigger your Spark application (your local machine, for example). The executors, on the other hand, run on the YARN nodes within the cluster. This setup is different from YARN-Cluster mode, where both the driver and executors run on the YARN cluster.
Architecture
Here’s a brief breakdown of the key components and their roles in YARN-Client mode:
- Driver Program: Runs locally on the client machine. It is responsible for creating the SparkContext, sending tasks to executors, and collecting the output of the computations.
- Resource Manager: Manages the resources in the cluster, scheduling jobs, and allocating resources as needed.
- Node Manager: Monitors the resources on a single node and reports to the Resource Manager.
- Application Master: Coordinates the job execution and resource allocation for a specific Spark application. It runs on a YARN node.
- Executors: Run the Spark tasks and hold a subset of the data being processed. These executors run on YARN nodes in the cluster.
Advantages
Simplicity: YARN-Client mode is simpler to set up and can be useful for interactive applications where the driver needs to quickly access the results.
Debugging: Since the driver runs on your local machine, it’s easier to debug, and you can have a more interactive development experience.
Disadvantages
Scalability: Since the driver runs locally, it may become a bottleneck if you run very large jobs requiring a lot of memory or computational resources.
Fault Tolerance: If the machine running the driver fails, the entire job will fail, as even though the executors run on the cluster, they depend on the driver for task scheduling and coordination.
Example
Here’s an example of how to submit a Spark application in YARN-Client mode using the `spark-submit` command:
spark-submit --deploy-mode client \
--master yarn \
--num-executors 2 \
--executor-memory 4G \
--executor-cores 2 \
my_spark_application.py
In this example:
- –deploy-mode client: Specifies that the driver program should run in client mode.
- –master yarn: Indicates that YARN is the cluster manager.
- –num-executors: Specifies the number of executors to use.
- –executor-memory: Allocates memory for each executor.
- –executor-cores: Specifies the number of CPU cores to use per executor.
This setup is beneficial for scenarios like development, debugging, and interactive analytics, but less ideal for large-scale production jobs due to the limitations mentioned above.