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======
Window
======

Tumble Window
-------------

Tumbling windows are consecutive, non-overlapping
windows of a specified fixed length. For example, a tumbling window of 5 minutes size groups
elements in 5 minutes intervals.

Example:
::

    >>> from pyflink.table.expressions import col, lit
    >>> Tumble.over(lit(10).minutes) \
    ...       .on(col("rowtime")) \
    ...       .alias("w")

.. currentmodule:: pyflink.table.window

.. autosummary::
    :toctree: api/

    Tumble.over
    TumbleWithSize.on
    TumbleWithSizeOnTime.alias


Sliding Window
--------------

Sliding windows have a fixed size and slide by
a specified slide interval. If the slide interval is smaller than the window size, sliding
windows are overlapping. Thus, an element can be assigned to multiple windows.

For example, a sliding window of size 15 minutes with 5 minutes sliding interval groups
elements of 15 minutes and evaluates every five minutes. Each element is contained in three
consecutive window evaluations.

Example:
::

    >>> from pyflink.table.expressions import col, lit
    >>> Slide.over(lit(10).minutes) \
    ...      .every(lit(5).minutes) \
    ...      .on(col("rowtime")) \
    ...      .alias("w")

.. currentmodule:: pyflink.table.window

.. autosummary::
    :toctree: api/

    Slide.over
    SlideWithSize.every
    SlideWithSizeAndSlide.on
    SlideWithSizeAndSlideOnTime.alias


Session Window
--------------

The boundary of session windows are defined by
intervals of inactivity, i.e., a session window is closes if no event appears for a defined
gap period.

Example:
::

    >>> from pyflink.table.expressions import col, lit
    >>> Session.with_gap(lit(10).minutes) \\
    ...        .on(col("rowtime")) \\
    ...        .alias("w")


.. currentmodule:: pyflink.table.window

.. autosummary::
    :toctree: api/

    Session.with_gap
    SessionWithGap.on
    SessionWithGapOnTime.alias


Over Window
-----------

Similar to SQL, over window aggregates compute an
aggregate for each input row over a range of its neighboring rows.

Example:
::

    >>> from pyflink.table.expressions import col, UNBOUNDED_RANGE
    >>> Over.partition_by(col("a")) \
    ...     .order_by(col("rowtime")) \
    ...     .preceding(UNBOUNDED_RANGE) \
    ...     .alias("w")

.. currentmodule:: pyflink.table.window

.. autosummary::
    :toctree: api/

    Over.order_by
    Over.partition_by
    OverWindowPartitionedOrdered.alias
    OverWindowPartitionedOrdered.preceding
    OverWindowPartitionedOrderedPreceding.alias
    OverWindowPartitionedOrderedPreceding.following
    OverWindowPartitioned.order_by