18, 2023 11:30
To: user@cassandra.apache.org
Subject: Re: Materialized View inconsistency issue
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, 300, 60) USING TTL 10;
>
> Use TTL of e.g. 1 month? So rows would start to disappear automatically.
> If somebody visited me 2 months ago and then it disappears next I would not
> care at all. A user who visited me 2 months ago is basically equal to a
> user who has never visited me.
care
at all. A user who visited me 2 months ago is basically equal to a user who has
never visited me.
From: Regis Le Bretonnic
Sent: Friday, August 18, 2023 9:47
To: user@cassandra.apache.org
Subject: Re: Materialized View inconsistency issue
You don&#x
tors_by_user_id where user_id = Joe;
>
> So I get Karen and Julia
>
> And then for each such visitor I do
>
> select visit_date from visits where user_id = Joe and visitor_id = Julia
> limit 1
>
> ____
> From: Regis Le Bretonnic
> Se
Sent: Tuesday, August 15, 2023 17:49
To: user@cassandra.apache.org
Subject: Re: Materialized View inconsistency issue
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Hi Josh...
A long (and almost private) message to explain how we fix materialized
views.
Let me first explain our use case... I work for an european dating website.
Users can received visits from other users (typically when someone looks at
a member profile page), and we want to inform them for e
When it comes to denormalization in Cassandra today your options are to either
do it yourself in your application layer or rely on Materialized Views to do it
for you at the server layer. Neither are production-ready approaches out of the
box (which is one of the biggest flaws in the "provide it
Hi surbhi ,
There are 2 drawbacks associated with MV.
1. Inconsistent view
2. The lock it takes on the base table. This gets worse when you have huge
number of clustering keys in a specific partition.
It's better you re-design a seperate table and let your API do a parallel
write on both.
Regards
Thanks everyone.
On Wed, 9 Aug 2023 at 01:00, Regis Le Bretonnic
wrote:
>
> Hi Surbhi
>
> We do use cassandra materialized views even if not recommended.
> There are known issues you have to make with. Despite of them, we still use
> VM.
> What we observe is :
> * there are inconsistency issue
Hi Surbhi
We do use cassandra materialized views even if not recommended.
There are known issues you have to make with. Despite of them, we still use
VM.
What we observe is :
* there are inconsistency issues but few. Most of them are rows that
should not exist in the MV...
* we made a spark scrip
That’s correct, yes. There is no current or upcoming version of Apache Cassandra in which materialized views are expected to be considered production-ready and maintain full consistency with their base table at this time.The feature is classified as “experimental” to indicate that this behavior is
>From 4.0.11's cassandra.yaml
*## EXPERIMENTAL FEATURES
### Enables materialized view creation on this
node.# Materialized views are considered experimental and are not
recommended for production use.enable_materialized_views: false*
So I thin
Hi,
We get complaints about Materialized View inconsistency issues.
We are on 3.11.5 and on 3.11.5 Materialized Views were not production ready.
We are ok to upgrade.
On which version of cassandra MVs doesnt have inconsistency issues?
Thanks
Surbhi
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