Spark on yarn concept understanding

Question

I am trying to understand how spark runs on YARN cluster/client. I have the following question in my mind.

1. Is it necessary that spark is installed on all the nodes in yarn cluster? I think it should because worker nodes in cluster execute a task and should be able to decode the code(spark APIs) in spark application sent to cluster by the driver?

2. It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster". Why does client node have to install Hadoop when it is sending the job to cluster?

Answer 1

Adding to other answers.

1. Is it necessary that spark is installed on all the nodes in yarn cluster?

No , If the spark job is scheduling in YARN(either client or cluster mode). Spark installation needed in many nodes only for standalone mode .

These are the visualisations of spark app deployment modes.

Spark Standalone Cluster

In cluster mode driver will be sitting in one of the Spark Worker node whereas in client mode it will be within the machine which launched the job .

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YARN cluster mode

YARN client mode

This table offers a concise list of differences between these modes:

pics source

2. It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster". Why does client node have to install Hadoop when it is sending the job to cluster?

Hadoop installation is not mandatory but configurations (not all) are!. We can call them as Gateway nodes . It's for two main reasons.

• The configuration contained in HADOOP_CONF_DIR directory will be distributed to the YARN cluster so that all containers used by the application use the same configuration.
• In YARN mode the ResourceManager's address is picked up from the Hadoop configuration( yarn-default.xml ). Thus, the --master parameter is yarn .

**Update: (2017-01-04)**

Spark 2.0+ no longer requires a fat assembly jar for production deployment. source

Answer 2

We are running spark jobs on YARN (we use HDP 2.2).

We don't have spark installed on the cluster. We only added the Spark assembly jar to the HDFS.

For example to run the Pi example:

./bin/spark-submit \
  --verbose \
  --class org.apache.spark.examples.SparkPi \
  --master yarn-cluster \
  --conf spark.yarn.jar=hdfs://master:8020/spark/spark-assembly-1.3.1-hadoop2.6.0.jar \
  --num-executors 2 \
  --driver-memory 512m \
  --executor-memory 512m \
  --executor-cores 4 \
  hdfs://master:8020/spark/spark-examples-1.3.1-hadoop2.6.0.jar 100

--conf spark.yarn.jar=hdfs://master:8020/spark/spark-assembly-1.3.1-hadoop2.6.0.jar - This config tell the yarn from were to take the spark assembly. If you don't use it, it will upload the jar from were you run spark-submit .

About your second question: The client node doesn't not need Hadoop installed. It only needs the configuration files. You can copy the directory from your cluster to your client.

Answer 3

1 - Spark if following s slave/master architecture. So on your cluster, you have to install a spark master and N spark slaves. You can run spark in a standalone mode. But using Yarn architecture will give you some benefits. There is a very good explanation of it here : http://blog.cloudera.com/blog/2014/05/apache-spark-resource-management-and-yarn-app-models/

2- It is necessary if you want to use Yarn or HDFS for example, but as i said before you can run it in standalone mode.

Answer 4

Let me try to cut glues and make it short for impatient.

6 components : 1. client, 2. driver, 3. executors, 4. application master, 5. workers, and 6. resource manager; 2 deploy modes ; and 2 resource (cluster) management .

Here's the relation:

Client

Nothing special, is the one submitting spark app.

Worker, executors

Nothing special, one worker holds one or more executors.

Master, & resource (cluster) manager

(no matter client or cluster mode)

• in yarn , resource manager and master sit in two different nodes;
• in standalone , resource manager == master, same process in the same node.

Driver

• in client mode , sits with client
• in yarn - cluster mode , sits with master (in this case, client process exits after submission of app)
• in standalone - cluster mode , sits with one worker

Voilà!