Github user fhueske commented on the issue:
https://github.com/apache/flink/pull/3574
Hi @huawei-flink, let me explain the idea of using `MapState` and its
benefits in more detail.
I'll start with the way that a `ListState` works. With `ListState` we can
get efficient access to the head element of the list. However, when updating
the `ListState`, we cannot remove individual elements but have to clear the
complete state and reinsert all elements that should remain. Hence we always
need to deserialize and serialize all elements of a `ListState`.
With the `MapState` approach, we would put the elements in a map which is
keyed on their processing timestamp. Since multiple records can arrive within
the same millisecond, we use a `List[Row]` as value type for the map. To
process a new row, we have to find the "oldest" row (i.e., the one with the
smallest timestamp) to retract it from the accumulator. With `ListState` this
is trivial, it is the head element. With `MapState` we have to iterate over the
keys and find the smallest one (smallest processing timestamp). This requires
to deserialize all keys, but these are only `Long` values and not complete
rows. With the smallest key, we can get the `List[Row]` value and take the
first Row from the list and retract it from the accumulator. When updating the
state, we only update the `List[Row]` value of the smallest key (or possible
remove it if the `List[Row]` became empty).
So the benefit of using `MapState` of `ListState` is that we only read `n`
Long (+ read/write 1 `List[Row]`) instead of reading and writing `n` Row values.
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