avocado

Transforms the provided for-comprehension to it's parallel version. Example usage:

parallelize {
 for {
   a <- doStuff1
   b <- doStuff2(a)
   c <- doStuff3
   d <- doStuff4(a)
 } yield combine(a, b, c, d)
}

The above code will be transformed to code essentially equivalent to:

for {
 a <- doStuff1
 (b, c, d) <- doStuff2(a).zip(doStuff3).zip(doStuff4(a))
} yield combine(a, b, c, d)

The transformed code will use the provided implicit avocado.AvocADO instance for method calls such as map, flatMap and zip. Potential for parallelism is introduced in places where zip calls are used. So in order to utilize this method in a sensible way, avocado.AvocADO.zip should initialize parallel calls. Though this method should also be safe for sequential operations.

Transforms the provided for-comprehension to it's parallel version. Example usage:

ado {
 for {
   a <- doStuff1
   b <- doStuff2(a)
   c <- doStuff3
   d <- doStuff4(a)
 } yield combine(a, b, c, d)
}

The above code will be transformed to code essentially equivalent to:

for {
 a <- doStuff1
 (b, c, d) <- doStuff2(a).zip(doStuff3).zip(doStuff4(a))
} yield combine(a, b, c, d)

The transformed code will use the provided implicit avocado.AvocADO instance for method calls such as map, flatMap and zip. Potential for parallelism is introduced in places where zip calls are used. So in order to utilize this method in a sensible way, avocado.AvocADO.zip should initialize parallel calls. Though this method should also be safe for sequential operations.