26thSep2_Notes.txt

KubernetesPodOperator (K8sPodOperator)
What it is:
 An Airflow operator that creates a Kubernetes Pod directly in your cluster.
 You can run any container (Python, Bash, Spark, etc.), as long as it’s packaged into a
Docker image.
 Very flexible — you control Pod spec (CPU, memory, volumes, environment
variables).
Where to use it:
 When you want fine-grained control of how pods are created.
 For non-Spark workloads (ETL in Python, shell scripts, ML inference, etc.).
 When your Spark job is already containerized (e.g., using Spark-on-K8s operator,
Helm, or a prebuilt Spark Docker image).
 If you need IRSA, secrets, config maps mounting into the pod.
Good for:
 Running general-purpose jobs in Kubernetes.
 Triggering Spark jobs via a pod that runs spark-submit (custom container).
 Jobs that need custom dependencies pre-installed in the image.
Caution:
 You are responsible for writing the full container logic.
 Monitoring/logging may be less “native” compared to SparkSubmitOperator

SparkSubmitOperator
What it is:
 Airflow operator built for submitting Spark jobs.
 Runs spark-submit against a Spark cluster (standalone, YARN, Mesos, or Kubernetes).
 Assumes Spark is already installed or available in the environment.
Where to use it:
 When you already have a running Spark cluster (on YARN, EMR, Dataproc, or K8s).

 If you want tight integration with Spark (spark-submit arguments, configs, jars, etc.).
 When you don’t want to manage Pod specs manually — the Spark cluster handles
scheduling/executors.
Good for:
 Directly running Spark JARs, PySpark, SQL queries.
 Classic Spark deployments where spark-submit is the standard.
 Quick Spark job orchestration with Airflow.
Caution:
 Limited to Spark workloads only.
 Requires Spark to be accessible in Airflow workers (or cluster config available).
 Less control over Kubernetes pod spec compared to KubernetesPodOperator.

When to choose what?
 Use KubernetesPodOperator if:
o You run Spark-on-K8s with Helm or Operator (containerized Spark job).
o You need more Kubernetes control (volumes, IRSA, secrets).
o You also run non-Spark jobs in the same DAG.
 Use SparkSubmitOperator if:
o You already have Spark cluster (EMR, Dataproc, Standalone).
o You want direct spark-submit integration.
o You’re mostly running Spark JARs/scripts without needing Kubernetes
specifics.