For a new shared Spark platform, how would you choose between YARN and Kubernetes as the cluster manager?

## What a cluster manager actually supplies Spark itself does not schedule machines. It asks a cluster manager for containers, receives them, launches a driver and executors inside them, and gets out of the way. So the choice is not about Spark's performance — Catalyst, the shuffle and the execution model are identical — but about five surrounding concerns: dependency delivery, multi-tenancy and quota, security and identity, shuffle durability, and who operates the thing. ## YARN YARN is the incumbent wherever Hadoop already is. - **Multi-tenancy** is mature: hierarchical queues under the Capacity or Fair scheduler, per-queue capacity and limits, node labels to steer workloads to particular hardware, preemption policies. Twenty teams sharing one cluster is the problem YARN was built for. - **Data locality** is real when NodeManagers sit on the same machines as HDFS DataNodes; Spark can schedule tasks near their blocks. - **Security** is Kerberos: `spark-submit` obtains delegation tokens for HDFS, Hive and HBase and renews them for long-running applications. - **Shuffle** has a first-class answer — the external shuffle service as a NodeManager auxiliary service — which makes dynamic allocation and executor loss cheap. - **Logs** are aggregated by the platform and retrievable with `yarn logs -applicationId`. The cost is the dependency model. Executors run whatever JVM and Python the node has, so upgrading a Python version or a native library is a cluster-wide change negotiated across every team. Fat jars, `--archives` with a packed virtualenv, and careful `--jars` hygiene are the workarounds, and they get old. ## Kubernetes With `--master k8s://https://api-server:6443`, `spark-submit` creates a driver pod from your image and the driver creates executor pods. - **Dependencies become per-job.** The container image pins the JVM, Python, native libraries and connectors. Two teams can run incompatible stacks on the same nodes on the same day. For platforms serving many teams, this is usually the deciding advantage. - **Isolation and quota** come from namespaces, ResourceQuota, LimitRange and RBAC service accounts rather than queues. It is workable but flatter than YARN's hierarchy; scheduling fairness across many pending Spark applications often needs an additional batch scheduler, and "first job grabs everything" is a real failure mode to design against. - **Elasticity** is better when the underlying nodes autoscale, and pods make spot/preemptible capacity natural — provided you enable graceful decommissioning so evictions do not force stage recomputes. - **Shuffle is the gap.** There is no NodeManager to host an auxiliary service, so dynamic allocation depends on `spark.dynamicAllocation.shuffleTracking.enabled` (weaker scale-down) or on adopting a remote/disaggregated shuffle service. Executor-local disk also needs deliberate provisioning rather than being assumed. - **Locality** usually disappears: storage is an object store reached over the network, which changes tuning priorities (larger files, better predicate pushdown) more than it changes the deploy decision. - **Debugging changes shape**: `kubectl logs` on the driver pod, `kubectl describe` for image-pull and scheduling failures, and executor pods that vanish on termination unless you keep them. ## Standalone and the rest Spark's standalone manager is the lightest option: a master and workers, `spark://host:7077`, and per-application core allocation via `--total-executor-cores`. It is a fine choice for a single dedicated cluster owned by one team, and a poor one for multi-tenant fairness or security. `local[*]` remains the right "cluster manager" for tests. Mesos was deprecated in Spark 3.2 and should not anchor a new platform. ## How to actually decide 1. **Where does the rest of the company run?** If every other service is on Kubernetes with established CI, image registries, RBAC and observability, running Spark there means one operational model instead of two. That argument usually beats every technical nuance below it. 2. **How much Hadoop is already load-bearing?** HDFS with Kerberos, Hive metastore integration, existing queue policies and years of tuning are expensive to move; keeping YARN for those workloads while new jobs go to Kubernetes is a legitimate two-platform interim. 3. **How heterogeneous are the dependencies?** Many teams with conflicting Python and library versions push hard toward images. 4. **How shuffle-heavy and how elastic are the workloads?** Shuffle-heavy jobs plus aggressive scale-down plus Kubernetes means you owe a shuffle answer up front, not later. 5. **Who is on call?** Pick the platform your team can debug at 3am. ## Migration reality The migration is not a flag change. `--master` and deploy mode are the easy part; the work is building base images, moving from delegation tokens to service accounts and cloud IAM, replacing queue policies with quotas and a batch scheduler, re-establishing log and history retention, and re-tuning for object storage instead of local HDFS. Plan it per workload class, run both for a period, and keep the same submission wrapper so job authors see one interface.