I was curious to know if there are benchmarks around on comparison between Spark on Yarn compared to Kubernetes.
This question arose because traditionally in Google Cloud we have been using Spark on Dataproc clusters. Dataproc <https://cloud.google.com/dataproc> provides Spark, Hadoop plus others (optional install) for data and analytic processing. It is PaaS Now they have GKE clusters as well and also introduced Apache Spark with Cloud Dataproc on Kubernetes <https://cloud.google.com/blog/products/data-analytics/modernize-apache-spark-with-cloud-dataproc-on-kubernetes> which allows one to submit Spark jobs to k8s using Dataproc stub as a platform to submit the job as below from cloud console or local gcloud dataproc jobs submit pyspark --cluster="dataproc-for-gke" gs://bucket/testme.py --region="europe-west2" --py-files gs://bucket/DSBQ.zip Job [e5fc19b62cf744f0b13f3e6d9cc66c19] submitted. Waiting for job output... At the moment it is a struggle to see what merits using k8s instead of dataproc bar notebooks etc. Actually there is not much literature around with PySpark on k8s. For me Spark on bare metal is the preferred option as I cannot see how one can pigeon hole Spark into a container and make it performant but I may be totally wrong. Thanks view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction.
