Hi Arvid,
I sent a separate mail titled - Session Windows - not working as expected (
to the user community )
All other details are here if you need, closing this thread.
Please have a look when you have a few minutes, much appreciated.
Regards,
Swagat
On Thu, May 6, 2021 at 1:50 AM Swagat Mis
Hi Arvid,
I sent a separate mail titled - Session Windows - not working as expected
closing this thread.
Please have a look when you have a few minutes, much appreciated.
Regards,
Swagat
On Wed, May 5, 2021 at 7:24 PM Swagat Mishra wrote:
> Hi Arvid,
>
> Tried a small POC to reproduce the b
Thanks Arvid.
very helpful.
On Thu, Apr 29, 2021 at 5:46 PM Arvid Heise wrote:
> Hi Swagat,
>
> 1. Where the data primarily resides depends on the chosen state backend
> [1]. In most cases, it's written to some file with a memory cache. It's
> possible to query the state [2] but not with SQL. I
Hi Swagat,
1. Where the data primarily resides depends on the chosen state backend
[1]. In most cases, it's written to some file with a memory cache. It's
possible to query the state [2] but not with SQL. In fact, it's so basic
that we decided to drop the feature in the future to make room for a m
Hi Arvid,
On 2 - I was referring to stateful functions as an alternative to windows,
but in this particular use case, its not fitting in exactly I think, though
a solution can be built around it.
On the overall approach here what's the right way to use Flink SQL:
Every event has the transaction
1. It always depends on the data volume per user. A million user is not
much if you compare it to the biggest Flink installations (Netflix,
Alibaba, PInterest, ...). However, for a larger scale and scalability, I'd
recommend to use rocksDB state backend. [1]
2. Are you referring to statefun? I'd s
1. What if there are a very high number of users, like a million customers
won't the service crash? Is it advisable to hold the data in memory.
2. What if state-functions are used to calculate the value ? How will this
approach differ from the one proposed below.
Regards,
Swagat
On Wed, Apr 21,
Hi Sunitha,
the approach you are describing sounds like you want to use a session
window. [1] If you only want to count them if they happen at the same hour
then, you want to use a tumbling window.
Your datastream approach looks solid.
For SQL, there is also a session (and tumbling) window [2].
