Run1 scala functional programming for data processing

Functional Data Transformation

Map, Filter, FlatMap

tokens.map(token => token.copy(value = token.value.toUpperCase))
tokens.filter(token => token.tokenType == StringToken)
tokens.flatMap(token => // transform to multiple elements

For-Comprehensions

• Alternative to nested map/flatMap: more readable syntax

for {
  token <- tokens
  if token.value.nonEmpty
} yield token.copy(metadata = token.metadata + ("processed" -> "true"))

Batch Processing Patterns

Processing Lists of Inputs

def tokenizeBatch(inputs: List[String]): List[List[Token]] = {
  inputs.map(tokenize)
}

Handling Options

Option(value).map(_.toInt).getOrElse(0)
Try { riskyOperation }.toOption
value match {
  case Some(x) => // handle
  case None => // handle
}

Immutability and Copy Patterns

Using Case Class copy

token.copy(metadata = token.metadata + ("key" -> "value"))

Avoiding Mutable Collections

• Use immutable List, Map, Set from Scala stdlib
• Chain operations rather than accumulating state

Higher-Order Functions

Functions as Parameters

def process(tokens: List[Token], transform: Token => Token): List[Token] = {
  tokens.map(transform)
}

Composition

val pipeline = tokenize _ andThen filterEmpty andThen enrichMetadata