Hi Gabriele, use (or extend) the window operator provided by Flink is a
better idea. A window operator in Flink manages two types of state:
- Window state: accumlate data for windows, and provide data to window
function when a window comes to its end time.
- Timer state: store the end tim
unction pattern, which is important for large windows.
>>
>> Best,
>> Zhanghao Chen
>> --
>> *From:* Gabriele Mencagli
>> *Sent:* Monday, March 4, 2024 19:38
>> *To:* user@flink.apache.org
>> *Subject:* Question about time-based operators
ich is important for large windows.
>
> Best,
> Zhanghao Chen
> --
> *From:* Gabriele Mencagli
> *Sent:* Monday, March 4, 2024 19:38
> *To:* user@flink.apache.org
> *Subject:* Question about time-based operators with RocksDB backend
>
&g
an be satisfied with the
reduce/aggregate function pattern, which is important for large windows.
Best,
Zhanghao Chen
From: Gabriele Mencagli
Sent: Monday, March 4, 2024 19:38
To: user@flink.apache.org
Subject: Question about time-based operators with RocksDB ba
Dear Flink Community,
I am using Flink with the DataStream API and operators implemented using
RichedFunctions. I know that Flink provides a set of window-based
operators with time-based semantics and tumbling/sliding windows.
By reading the Flink documentation, I understand that there is the
