SETUP - You should have a basic understanding of Haskell (what data types, functions and type classes are) and have a working GHC installation. Clone and build these exercises

The default tool that many programmers immediately reach for when they wish to parse data (but don't want to deal with tools like Lex and Yacc) is regular expressions.

Of course, whenever you mention regular expressions, someone will bring up Jamie Zawinski. And now you have two problems.

However, regular expressions have many problems:

• They aren't composable
• They're typically represented as Strings rather than code making them harder to debug
• Often only interpreted at runtime leading to runtime failures (no static type checking!)
• They can't be used to parse HTML

The preferred approach in functional programming languages (also available in non-functional languages as well!) is parser combinators, which provide a way of writing your parser in code, thus being able to use all your existing tooling to do so.

In this workshop we will build up our own Parser Combinator library from scratch (and hopefully make sense of the dreaded M-word whilst we're at it!) and improve upon it, to see that there is nothing mystical about parsing in data.

Many people are familiar with Haskell stream processing libraries such as pipes or conduit. However, they can be a bit daunting to newcomers: they contain both input and output types in the same representation and they require all these new operators rather than using well-known function composition.

The streaming library by Michael Thompson takes a simpler approach to represent how to stream data through your code. It offers a more familiar API to anyone used to using lists, whilst still offering a lot of power and flexibility. It also compares very well in terms of performance: conduit's fusion framework tends to use a similar structure!

In this workshop we will have an overview of the streaming ecosystem and how it compares to pipes and conduit, and start using it to efficiently process large amounts of data.

If attending this workshop, you may wish to download the exercises (and fetch any dependencies) beforehand.