Query Execution Optimization for Broadcast Join using Replicated-Reads Strategy – George Wang, Ahana

Query Execution Optimization for Broadcast Join using Replicated-Reads Strategy – George Wang, Ahana

Today presto supports broadcast join by having a worker to fetch data from a small data source to build a hash table and then sending the entire data over the network to all other workers for hash lookup probed by large data source. This can be optimized by a new query execution strategy as source data from small tables is pulled directly by all workers which is known as replicated reads from dimension tables. This feature comes with a nice caching property given that all worker nodes N are now participating in scanning the data from remote sources. The table scan operation for dimension tables is cacheable per all worker nodes. In addition, there will be better resource utilization because the presto scheduler can now reduce the number plan fragment to execute as the same workers run tasks in parallel within a single stage to reduce data shuffles.

Presto Authorization with Apache Ranger – Reetika Agrawal, Ahana & William Brooks, Privacera

Presto Authorization with Apache Ranger – Reetika Agrawal, Ahana & William Brooks, Privacera

Apache Ranger has been the user’s choice to support authorization in various data platforms from small-scale to enterprise-grade production environments. At Ahana, engineers are working on the Presto-Ranger integration, aiming to support global fine-grained data access control across all catalogs for Presto, while also providing auditing and monitoring of user access. We would like to collaborate with the Privacera and share our learnings, what we developed so far, and also hope to shed light on the future work of the Ranger Presto Plugin with Apache Ranger committer.