Hi all,
Another query around data Modelling.
We have a existing table with below structure:
Table(PK,CK, col1,col2, col3, col4,col5)
Now each Pk here have 1k - 10k Clustering keys. Each PK has size from 10MB
to 80MB. We have overall 100+ millions partitions. Also we have set
levelled compactions
How are you running repair? -pr? Or -st/-et?
4.0 gives you real incremental repair which helps. Splitting the table won’t
make reads faster. It will increase the potential parallelization of
compaction.
> On Jun 30, 2022, at 7:04 AM, MyWorld wrote:
>
>
> Hi all,
>
> Another query around d
Hi Jeff,
We are running repair with -pr option.
You are right it would have no or very minimal impact on read (considering
the fact now data has to be read from 2 levels instead of 3). But my guess
there is no negative impact of this model2.
On Thu, Jun 30, 2022, 7:41 PM Jeff Jirsa wrote:
> Ho
I have noticed this "running a weekly repair and compaction job".
What do you mean weekly compaction job? Have you disabled the
auto-compaction on the table and is relying on weekly scheduled
compactions? Or running weekly major compactions? Neither of these
sounds right.
On 30/06/2022 15:03
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We run major compaction once in a week
On Thu, Jun 30, 2022, 8:14 PM Bowen Song wrote:
> I have noticed this "running a weekly repair and compaction job".
>
> What do you mean weekly compaction job? Have you disabled the
> auto-compaction on the table and is relying on weekly scheduled
> compact
And why do you do that?
On 30/06/2022 16:35, MyWorld wrote:
We run major compaction once in a week
On Thu, Jun 30, 2022, 8:14 PM Bowen Song wrote:
I have noticed this "running a weekly repair and compaction job".
What do you mean weekly compaction job? Have you disabled the
auto-
Hi,
We did do compaction job every week in the past to keep the disk space used
under control as we had mainly data in the table that needs to expire with TTL
and were also using levelled compaction.
In our case we had different TTL’s in the same table and the partitions were
spread over multip
