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Learning Scala for Spark, or, what's up with that triple equals?

I began to learn Scala specifically to work with Spark. The sheer number of language features in Scala can be overwhelming, so, I find it useful to learn Scala features one by one, in context of specific use cases. In a sense I’m treating Scala like a DSL for writing Spark jobs.

Let’s pick apart a simple fragment of Spark-Scala code: dataFrame.filter($"age" === 21).

There are a few things going on here:

  • The $"age" creates a Spark Column object referencing the column named age within in a dataframe. The $ operator is defined in an implicit class StringToColumn. Implicit classes are a similar concept to C# extension methods or mixins in other dynamic languages. The $ operator is like a method added on to the StringContext class.

  • The triple equals operator === is normally the Scala type-safe equals operator, analogous to the one in Javascript. Spark overrides this with a method in Column to create a new Column object that compares the Column to the left with the object on the right, returning a boolean. Because double-equals (==) cannot be overridden, Spark must use the triple equals.

  • The dataFrame.filter method takes an argument of Column, which defines the comparison to apply to the rows in the DataFrame. Only rows that match the condition will be included in the resulting DataFrame.

Note that the actual comparison is not performed when the above line of code executes! Spark methods like filter and select – including the Column objects passed in–are lazy. You can think of a DataFrame like a query builder pattern, where each call builds up a plan for what Spark will do later when a call like show or write is called. It’s similar in concept to something like IQueryable in LINQ, where foo.Where(row => row.Age == 21) builds up a plan and an expression tree that is later translated to SQL when rows must be fetched, e.g., when ToList() is called.

Written on September 24, 2017