Presto SQL Functions – Facebook
In this talk we will show how to use the recently introduced SQL function feature, how it works, and the ongoing work to support invoking arbitrary functions remotely with remote UDF server.
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.
Dynamic UDF Framework and its Applications – Rongrong Zhong, Alluxio & Yanbing Zhang, Bytedance
Presto supports dynamically registered User Defined Functions (UDFs) since 2020. Over the years, we used this framework to add support for SQL UDFs and remote / external UDFs. One common community request in the UDF domain is to support Hive UDFs. Many companies have legacy Hive pipelines, and engineers who are familiar with HQL and Hive UDFs. With remote UDF, one can implement Hive UDF support as UDFs running on the remote cluster. But since HiveUDFs are written in Java, we can also run them inside the engine. We extended the dynamic UDF framework to support Java UDFs, and used this new extension to add HiveUDF support in Presto. With this feature, users can directly use their familiar HiveUDFs and UDAFs in their Presto query.
(Chinese) Presto at Bytedance – Hive UDF Wrapper for Presto
Presto has been widely used at Bytedance in several ways such as in the data warehouse, BI tools, ads etc. And, the Presto team at Bytedance has also delivered many key features and optimizations such as the Hive UDF wrapper, coordinator, runtime filter and so on which extend Presto usages and enhance Presto stabilities. Nowadays, most companies will use both Hive (or Spark) and Presto together. But Presto UDFs have very different syntax and internal mechanisms compared with Hive UDFs. This restricts Presto usage while users need to maintain 2 kinds of functions. In this talk, we will present a way to execute Hive UDF/UDAF inside Presto.