I would try to avoid 100's on MB's per row. It will take longer to compact and repair. 

10's is fine. Take a look at in_memory_compaction_limit and thrift_frame_size in the yaml file for some guidance.

Aaron Morton
Freelance Developer

On 2/05/2012, at 6:00 AM, Aaron Turner wrote:

On Tue, May 1, 2012 at 10:20 AM, Tim Wintle <timwintle@gmail.com> wrote:
I believe that the general design for time-series schemas looks
something like this (correct me if I'm wrong):

(storing time series for X dimensions for Y different users)


But I've not found much advice on calculating optimal bucket sizes (i.e.
optimal number of columns per row), and how that decision might be
affected by compression (or how significant the performance differences
between the two options might be).

Are the calculations here are still considered valid (proportionally) in
1.X, with the changes to SSTables, or is it significantly different?


Tens or a few hundred MB per row seems reasonable.  You could do
thousands/MB if you wanted to, but that can make things harder to

Depending on the size of your data, you may find that the overhead of
each column becomes significant; far more then the per-row overhead.
Since all of my data is just 64bit integers, I ended up taking a days
worth of values (288/day @ 5min intervals) and storing it as a single
column as a vector.  Hence I have two CF's:

StatsDaily  -- each row == 1 day, each column = 1 stat @ 5min intervals
StatsDailyVector -- each row == 1 year, each column = 288 stats @ 1
day intervals

Every night a job kicks off and converts each row's worth of
StatsDaily into a column in StatsDailyVector.  By doing it 1:1 this
way, I also reduce the number of tombstones I need to write in
StatsDaily since I only need one tombstone for the row delete, rather
then 288 for each column deleted.

I don't use compression.

Aaron Turner
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