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.

How Carbon uses PrestoDB in the Cloud with Ahana to Power its Real-time Customer Dashboards

How Carbon uses PrestoDB in the Cloud with Ahana to Power its Real-time Customer Dashboards

Carbon is a real-time revenue management platform that consolidates revenue and audience analytics, data management, and yield operations into a single solution. Real-time analytics is super critical – their customers rely on real-time data to make revenue decisions. After facing issues around performance, visibility & ease of use, and serverless pricing model with AWS Athena, the team moved to a managed service for PrestoDB in the cloud – Ahana Cloud – to power their customer-facing dashboards. In this session, Jordan will discuss some of the reasons the team moved from AWS Athena to a managed PrestoDB on Intel-optimized AWS instances. He will also dive into their current architecture that includes an Ahana-managed Hive Metastore along with Apache ORC file format and an S3-based data lake. Last, he’ll share some performance benchmarks and talk about what’s next for PrestoDB at Carbon.