Exploring New Frontiers: How Apache Flink, Apache Hudi and Presto Power New Insights and Gold Nuggets at Scale

Exploring New Frontiers: How Apache Flink, Apache Hudi and Presto Power New Insights and Gold Nuggets at Scale

Danny Chan & Sagar Sumit, Onehouse – In this talk, attendees will walk away with: – The current challenges of analytics on transactional data systems with data streams at scale – How the Hudi unlocks incremental processing on the lake – How Presto allows ad-hoc queries that support data exploration on Flink data – How you can leverage Flink, Hudi and Presto to build incremental materialized views

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.