One of the handy features that makes (Py)Spark more flexible than database tools like Hive even for just transforming tabular data is the ease of creating User Defined Functions (UDFs). However, one thing that still remains a little annoying is that you have to separately define a function and declare it as a UDF. With four lines of code you can clean those definitions right up.

Since I’ve started using Apache Spark, one of the frequent annoyances I’ve come up against is having an idea that would be very easy to implement in Pandas, but turns out to require a really verbose workaround in Spark. A recent example of this is doing a forward fill (filling null values with the last known non-null value).