AllWindowedStream

Applies the given aggregation function to each window. The aggregation function is called for each element, aggregating values incrementally and keeping the state to one accumulator per window.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given aggregation function.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given aggregation function.

Applies the given window function to each window. The window function is called for each evaluation of the window. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given aggregation function.

Sets the allowed lateness to a user-specified value. If not explicitly set, the allowed lateness is 0L. Setting the allowed lateness is only valid for event-time windows. If a value different than 0 is provided with a processing-time org.apache.flink.streaming.api.windowing.assigners.WindowAssigner, then an exception is thrown.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Note that this function requires that all data in the windows is buffered until the window is evaluated, as the function provides no means of pre-aggregation.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Note that this function requires that all data in the windows is buffered until the window is evaluated, as the function provides no means of pre-aggregation.

Sets the Evictor that should be used to evict elements from a window before emission.

Note: When using an evictor window performance will degrade significantly, since pre-aggregation of window results cannot be used.

Applies an aggregation that that gives the maximum of the elements in the window at the given position.

Applies an aggregation that that gives the maximum of the elements in the window at the given field.

Applies an aggregation that that gives the maximum element of the window by the given position. When equality, returns the first.

Applies an aggregation that that gives the maximum element of the window by the given field. When equality, returns the first.

Applies an aggregation that that gives the minimum of the elements in the window at the given position.

Applies an aggregation that that gives the minimum of the elements in the window at the given field.

Applies an aggregation that that gives the minimum element of the window by the given position. When equality, returns the first.

Applies an aggregation that that gives the minimum element of the window by the given field. When equality, returns the first.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Note that this function requires that all data in the windows is buffered until the window is evaluated, as the function provides no means of pre-aggregation.

Applies a reduce function to the window. The window function is called for each evaluation of the window for each key individually. The output of the reduce function is interpreted as a regular non-windowed stream.

This window will try and pre-aggregate data as much as the window policies permit. For example, tumbling time windows can perfectly pre-aggregate the data, meaning that only one element per key is stored. Sliding time windows will pre-aggregate on the granularity of the slide interval, so a few elements are stored per key (one per slide interval). Custom windows may not be able to pre-aggregate, or may need to store extra values in an aggregation tree.

Applies a reduce function to the window. The window function is called for each evaluation of the window for each key individually. The output of the reduce function is interpreted as a regular non-windowed stream.

This window will try and pre-aggregate data as much as the window policies permit. For example, tumbling time windows can perfectly pre-aggregate the data, meaning that only one element per key is stored. Sliding time windows will pre-aggregate on the granularity of the slide interval, so a few elements are stored per key (one per slide interval). Custom windows may not be able to pre-aggregate, or may need to store extra values in an aggregation tree.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given pre-aggregation reducer.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given pre-aggregation reducer.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given pre-aggregation reducer.

Applies the given window function to each window. The window function is called for each evaluation of the window for each key individually. The output of the window function is interpreted as a regular non-windowed stream.

Arriving data is pre-aggregated using the given pre-aggregation reducer.

Send late arriving data to the side output identified by the given OutputTag. Data is considered late after the watermark has passed the end of the window plus the allowed lateness set using allowedLateness.

You can get the stream of late data using DataStream.getSideOutput on the DataStream resulting from the windowed operation with the same OutputTag.

Applies an aggregation that sums the elements in the window at the given position.

Applies an aggregation that sums the elements in the window at the given field.

Sets the Trigger that should be used to trigger window emission.