SQL UDFs are simple yet powerful extensions to Spark SQL. As functions, they provide a layer of abstraction to simplify query construction – making SQL queries more readable and modularized. Unlike UDFs that are written in a non-SQL language, SQL UDFs are more lightweight for SQL users to create. SQL function bodies are transparent to the query optimizer thus making them more performant than external UDFs. SQL UDFs can be created as either temporary or permanent functions, be reused across multiple queries, sessions and users, and be access-controlled via Access Control Language (ACL). In this blog, we will walk you through some key use cases of SQL UDFs with examples.
I look forward to dealing with cardinality issues and performance tuning these things in 5 years.