Batch jobs have the characteristic of bulk creation and deletion, and the cloud provides strong elasticity. Therefore, batch jobs and the cloud makes a perfect match. In the cloud-native world, we can use Kubernetes and cluster autoscaler to reduce costs. But unlike microservices, batch jobs have higher requirements for the elasticity of the cluster, posing more challenges to cluster autoscaler. In our scenario, users will create up to 16,000 pods within a short period. When this batch of tasks is completed, the cluster needs to be quickly scaled down. In this talk, we will share some issues and solutions encountered using cluster autoscaler in batch creation and deletion scenarios. For example, why cluster is not successfully scaled up, why pod creation takes so much time, why idle nodes were not promptly deleted, and so on. By solving these issues, we are able to scale the cluster to 2,000 nodes in production.