ryankert01 commented on code in PR #474:
URL: https://github.com/apache/yunikorn-site/pull/474#discussion_r1797077542


##########
docs/user_guide/workloads/run_spark.md:
##########
@@ -25,12 +25,86 @@ specific language governing permissions and limitations
 under the License.
 -->
 
+## Deploy Spark job with Spark Operator and Helm
+
+:::note
+Pre-requisites:
+- This tutorial assumes YuniKorn is 
[installed](../../get_started/get_started.md) under the namespace `yunikorn`
+- Use spark-operator version >= 2.0 to enable support for YuniKorn gang 
scheduling
+:::
+
+:::warning
+This installation involves installing YuniKorn and Spark operator, which may 
take a few minutes to complete. To check the status we can use `kubectl get 
pods -n yunikorn` and `kubectl get pods -n spark-operator`
+:::
+
+### Install YuniKorn
+
+A simple script to install YuniKorn under the namespace `yunikorn`, refer to 
[Get Started](../../get_started/get_started.md) for more details.
+
+```shell script
+helm repo add yunikorn https://apache.github.io/yunikorn-release
+helm repo update
+helm install yunikorn yunikorn/yunikorn --create-namespace --namespace yunikorn
+```
+
+### Install spark operator
+
+We should install `spark-operator` with 
`controller.batchScheduler.enable=true` and set 
`controller.batchScheduler.default=yunikorn`. It's optional to set the default 
scheduler to YuniKorn since you can specify it later on, but it's recommended 
to do so.  
+Also, note that our total allocated memory is `memory + memoryOverhead + 
spark.executor.pyspark.memory`, which will further propagate to the 
`yunikorn.apache.org/task-groups` annotation for subsequent use. 

Review Comment:
   once 
[spark-operator#2209](https://github.com/kubeflow/spark-operator/pull/2209) 
merged and released, it would be `memory + memoryOverhead + 
spark.executor.pyspark.memory + spark.memory.offHeap.size` opened a jira to 
track it: https://issues.apache.org/jira/browse/YUNIKORN-2919



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