Spark source understanding withScope

When you understand the role of each operator by looking at the RDD source code, you can always see withScope, withScope what is it?

First you need to know a few things: scala currying, loan pattern

Scala currying

In scala, a currying function supports multiple parameter lists when applied, rather than just one. When the first call passes only the first argument, it returns a function value for the second call.

scala> def curriedSum(x: Int)(y: Int) = x + y
curriedSum: (x: Int)(y: Int)
scala> curriedSum(1)(2)
res1: Int = 3
scala> val add3 = curriedSum(3)_
add3: Int => Int = <function1>
scala> add3(4)
res2: Int = 7

curriedSum(3)_in_Is a placeholder, indicating that the second parameter is not passed first, and the return value is a function value.
We look at the RDD source code

/**
   * Execute a block of code in a scope such that all new RDDs created in this body will
   * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}.
   *
   * Note: Return statements are NOT allowed in the given body.
   */
  Private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](sc)(body) // Here is the currying

Loan pattern

Extract the public part (function body) into a method, and pass the non-public part through the function value.

/**
   * Execute the given body such that all RDDs created in this body will have the same scope.
   *
   * If nesting is allowed, any subsequent calls to this method in the given body will instantiate
   * child scopes that are nested within our scope. Otherwise, these calls will take no effect.
   *
   * Additionally, the caller of this method may optionally ignore the configurations and scopes
   * set by the higher level caller. In this case, this method will ignore the parent caller's
   * intention to disallow nesting, and the new scope instantiated will not have a parent. This
   * is useful for scoping physical operations in Spark SQL, for instance.
   *
   * Note: Return statements are NOT allowed in body.
   */
  private[spark] def withScope[T](
      sc: SparkContext,
      name: String,
      allowNesting: Boolean,
      ignoreParent: Boolean)(body: => T): T = {
    // Save the old scope to restore it later
    val scopeKey = SparkContext.RDD_SCOPE_KEY
    val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY
    val oldScopeJson = sc.getLocalProperty(scopeKey)
    val oldScope = Option(oldScopeJson).map(RDDOperationScope.fromJson)
    val oldNoOverride = sc.getLocalProperty(noOverrideKey)
    try {
      if (ignoreParent) {
        // Ignore all parent settings and scopes and start afresh with our own root scope
        sc.setLocalProperty(scopeKey, new RDDOperationScope(name).toJson)
      } else if (sc.getLocalProperty(noOverrideKey) == null) {
        // Otherwise, set the scope only if the higher level caller allows us to do so
        sc.setLocalProperty(scopeKey, new RDDOperationScope(name, oldScope).toJson)
      }
      // Optionally disallow the child body to override our scope
      if (!allowNesting) {
        sc.setLocalProperty(noOverrideKey, "true")
      }
             Body // non-public part
    } finally {
      // Remember to restore any state that was modified before exiting
      sc.setLocalProperty(scopeKey, oldScopeJson)
      sc.setLocalProperty(noOverrideKey, oldNoOverride)
    }
  }

Because each RDD operator method has a common part and a common parameter, so use the scope with the scope to encapsulate the public part code, use Cory to pass the common parameters first.
Then the non-public part code is passed in via the second parameter. Here the body parameter is the non-public part function value.

Understanding operator

/**
   * Return a new RDD by applying a function to all elements of this RDD.
   */
  def map[U: ClassTag](f: T => U): RDD[U] = withScope {
    val cleanF = sc.clean(f)
    new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF))
  }

Here is an example, the map method
In fact, this is equivalent to directly calling the withScope method, followed by the function literals inside the curly braces, which are parameters.
In scala, when a method has only one argument, curly brackets can be used instead of parentheses.