Scala External DSL and Combinator Parsing Experimentation

Yesterday I was looking around on the inter-webs for a Scala project that was “iBatis” like, but more Scala oriented.  I found some cool stuff (ScalaQL, and scala-sql-dsl for example) but nothing quite what I was looking for.

I kind of chewed on what exactly it was that I was looking for and this is my 10k/foot feature list:

• Ability to define the underlying data store in a DSL that can be understood by Scala
• Ability to map Scala classes to JDBC results by binding to artifacts of the data store

This is more or less what iBatis accomplishes with xml (now annotations) and a bunch of reflection.  I’d like to see it done without XML and minimal reflection.

That sent me down the rabbit hole on how exactly one goes about defining a DSL in scala.  I re-read chapter 31 in Programming in Scala.  I also read chapter 11 in Programming Scala.  Then I took a stab at writing a parser combinator for a very, very small subset of the “CREATE TABLE” DDL syntax for SQL.

import scala.util.parsing.combinator._

class TableDdlParser extends JavaTokenParsers {
  
  def tables: Parser[Map[String, Any]] = rep(table) ^^ { Map() ++ _ }
  
  def table: Parser[(String,Any)] = 
    ("TABLE" ~ tableName ~ columns 
       ^^ { case "TABLE" ~ tableName ~ tableContents => (tableName,tableContents) })
  
  def tableName: Parser[String] = ident ^^ { case ident => ident }
  
  def columns: Parser[Map[String, Any]] = "("~> repsep(column, ",") <~")" ^^ { Map() ++ _ }
  
  def column: Parser[(String,Any)] = 
    columnName ~ dataType ^^ { case columnName ~ dataType => (columnName,dataType) }
    
  def columnName: Parser[String] = ident ^^ { case ident => ident }
  
  def dataType: Parser[Any] = "VARCHAR" | "INTEGER"
  
}

object TableDdlParserRunner extends TableDdlParser {
  
  def main(args: Array[String]) {
    val input = 
      """TABLE person (first_name VARCHAR, last_name VARCHAR, age INTEGER)
         TABLE place (city VARCHAR, state VARCHAR)"""
    println(parseAll(tables,input))
  }
 
}

Here is the output

[2.51] parsed: 
Map(person -> Map(first_name -> VARCHAR, 
                  last_name -> VARCHAR, 
                  age -> INTEGER), 
    place -> Map(city -> VARCHAR, 
                 state -> VARCHAR))

This experiment only supports two data types (VARCHAR and INTEGER), without any of the other metadata that is required for those types.  I just wanted a feel for how hard it would be to write and didn’t want to get bogged down in the details.  It turned out to be quite a bit easier than I thought it would be.

Next up is to figure out the best way to map this DDL information to a scala class (If I don’t get distracted by something else first).