Managed Service for Apache Spark (formerly Dataproc on Compute Engine)

## Announcement Announcement **Managed Service for Apache Spark** (formerly Dataproc on Compute Engine) Announcing the following cluster features: * [Flexible VMs](https://docs.cloud.google.com/dataproc/docs/concepts/configuring-clusters/flexible-vms): Minimize stockouts and improve machine obtainability by defining prioritized lists for your master, primary, and secondary worker VM types. Managed Spark for Apache Spark utilizes these lists to create your cluster, and selects the optimal VM type based on current capacity, quotas, and existing reservations. * [Cluster Scheduled Stop](https://docs.cloud.google.com/dataproc/docs/concepts/configuring-clusters/scheduled-stop): Optimize cost and maintain your cluster configuration by stopping clusters after a specified idle period, at a specified future time, or after a specified period from cluster creation or a cluster update request. All custom cluster configurations are restored once you restart the cluster. * [Zero-scale clusters](https://docs.cloud.google.com/dataproc/docs/guides/create-zero-scale-cluster#:%7E:text=Dataproc%20zero%2Dscale%20clusters%20provide,be%20scaled%20down%20to%20zero%2E): Reduce costs by creating only secondary workers, scaling them down to zero when they are not in use.