pyflink.table.Table.over_window#

Table.over_window(*over_windows: pyflink.table.window.OverWindow) → pyflink.table.table.OverWindowedTable[source]#

Defines over-windows on the records of a table.

An over-window defines for each record an interval of records over which aggregation functions can be computed.

Example:

>>> from pyflink.table.expressions import col, lit
>>> tab.over_window(Over.partition_by(col('c')).order_by(col('rowtime')) \
...     .preceding(lit(10).seconds).alias("ow")) \
...     .select(col('c'), col('b').count.over(col('ow'), col('e').sum.over(col('ow'))))

Note

Computing over window aggregates on a streaming table is only a parallel operation if the window is partitioned. Otherwise, the whole stream will be processed by a single task, i.e., with parallelism 1.

Note

Over-windows for batch tables are currently not supported.

Parameters: over_windows – over windows created from Over.
Returns: A over windowed table.