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.

Top 10 Reasons to Use & Contribute to Presto – Steven Mih, Ahana

Top 10 Reasons to Use & Contribute to Presto – Steven Mih, Ahana

Presto is complicated with many intricacies. Ahana Cloud is the only managed service for Presto on AWS that simplifies Presto, bringing its power to platform teams of any size or skill set. In this session we’ll give you a quick overview of Ahana Cloud, including managing multiple Presto clusters seamlessly, querying a range of data sources, as well as just-released capabilities.