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# Window Operations and Aggregations
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Operations that can be applied to windowed streams including built-in aggregations, custom window functions, and incremental aggregations.
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## Built-in Aggregations
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```scala { .api }
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class WindowedStream[T, K, W <: Window] {
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  def sum(position: Int): DataStream[T]
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  def sum(field: String): DataStream[T]
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  def min(position: Int): DataStream[T]
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  def max(position: Int): DataStream[T]
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  def minBy(position: Int): DataStream[T]
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  def maxBy(position: Int): DataStream[T]
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}
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class AllWindowedStream[T, W <: Window] {
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  def sum(position: Int): DataStream[T]
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  def min(position: Int): DataStream[T]
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  def max(position: Int): DataStream[T]
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  def minBy(position: Int): DataStream[T]
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  def maxBy(position: Int): DataStream[T]
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}
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```
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## Custom Window Functions
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```scala { .api }
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class WindowedStream[T, K, W <: Window] {
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  def apply[R: TypeInformation](function: WindowFunction[T, R, K, W]): DataStream[R]
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  def process[R: TypeInformation](function: ProcessWindowFunction[T, R, K, W]): DataStream[R]
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  def reduce(function: (T, T) => T): DataStream[T]
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  def aggregate[ACC: TypeInformation, R: TypeInformation](aggregateFunction: AggregateFunction[T, ACC, R]): DataStream[R]
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}
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```
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Window operations enable computing results over finite subsets of infinite streams using time or count-based boundaries.