Cloudera Docs

Virtual Warehouse auto-scaling

The way Hive Virtual Warehouse performs auto-scaling is different from the Impala Virtual Warehouse. Learning how the auto-scaling works helps you choose the type of Virtual Warehouse and correctly configure the auto-scaling to handle your jobs.

Key features describes auto-scaling in Cloudera and its benefits. You need to know specifics about how auto-scaling works in the type of Virtual Warehouse you choose. The number of queries running concurrently and the query complexity determine how Hive Virtual Warehouse auto-scaling works. A BI-type query does not require the same resources and scaling process as an ETL-type query. A complex ETL-type query requires intensive data scanning. You can configure the auto-scaling method used by the Hive Virtual Warehouse based on the number of concurrently running queries or the size of data scanned for a query. There are two methods of Hive Virtual Warehouse autoscaling:

  • Concurrency for BI-type queries

  • Isolation for ETL-type queries

Impala Virtual Warehouse auto-scaling is designed for concurrently running BI queries to make resources available for queued queries. Like Hive auto-scaling methods, coordinators and executors control Impala auto-scaling.

Workload Aware Auto-Scaling (WAAS) is available for Impala Virtual Warehouses as a technical preview. Previewing this feature requires an entitlement. Contact your account team if you are interested in previewing the feature.

Understanding executors

In AWS environments, an executor is an Elastic Compute Cloud (EC2) r5d.4xlarge instance for Hive and Impala compute nodes. For more information, see "AWS resources created for Data Warehouse". In Azure environments, an executor is an Azure E16 v3 instance. For more information, see "Virtual Warehouse sizing requirements for public cloud environments".

The default number of executors per executor group is based on the number of executor nodes contained by the original size of the Virtual Warehouse you configured during creation. For example, if you created the Virtual Warehouse and configured the MEDIUM size, which has 20 executor nodes, then each executor group contains 20 executors.

When you stop running queries, resources scale down, and executors are released. When all executor groups are scaled back, executors are idle, and after a configured period of idle time (AutoSuspend Timeout) elapses, the Virtual Warehouse is suspended. If a query is executed on a suspended Virtual Warehouse, the coordinator queues the query. When the auto-scaler detects a queued query, it immediately adds an executor group that can run the query on the Virtual Warehouse.