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async-operations.md data-sources.md function-interfaces.md index.md keyed-streams.md output-operations.md scala-extensions.md stream-composition.md stream-environment.md stream-partitioning.md stream-transformations.md window-operations.md windowing.md

tile.json

Window Operations and Aggregations

Operations that can be applied to windowed streams including built-in aggregations, custom window functions, and incremental aggregations.

Built-in Aggregations

class WindowedStream[T, K, W <: Window] {
  def sum(position: Int): DataStream[T]
  def sum(field: String): DataStream[T]
  def min(position: Int): DataStream[T]
  def max(position: Int): DataStream[T]
  def minBy(position: Int): DataStream[T]
  def maxBy(position: Int): DataStream[T]
}

class AllWindowedStream[T, W <: Window] {
  def sum(position: Int): DataStream[T]
  def min(position: Int): DataStream[T]
  def max(position: Int): DataStream[T]
  def minBy(position: Int): DataStream[T]
  def maxBy(position: Int): DataStream[T]
}

Custom Window Functions

class WindowedStream[T, K, W <: Window] {
  def apply[R: TypeInformation](function: WindowFunction[T, R, K, W]): DataStream[R]
  def process[R: TypeInformation](function: ProcessWindowFunction[T, R, K, W]): DataStream[R]
  def reduce(function: (T, T) => T): DataStream[T]
  def aggregate[ACC: TypeInformation, R: TypeInformation](aggregateFunction: AggregateFunction[T, ACC, R]): DataStream[R]
}

Window operations enable computing results over finite subsets of infinite streams using time or count-based boundaries.