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.

Drag and Drop Query Builder for PrestoDB – Ravishankar Nair, PassionBytes

Drag and Drop Query Builder for PrestoDB – Ravishankar Nair, PassionBytes

You use multiple tools for databases, for example Azure Data Studio for SQLServer access, Toad or SQLDeveloper for Oracle access, MySQLWorkbench for MySQL databases. Imagine we have one tool and we can query any database, bring any table from any catalog to a single canvas! Now you join, the underlying PrestoDB compatible query is generated. Click a button, you get the profiled data, including distributions and correlations. An amazing tool in action.