Use index rows named for time intervals that contain columns named for
the row keys of the base data rows from each interval.
b
On Wed, Jun 2, 2010 at 8:32 AM, David Boxenhorn wrote:
> How do I handle giant sets of ordered data, e.g. by timestamps, which I want
> to access by range?
ured data to stick under a column (named by the
> >>>> timestamp), then you can serialize and unserialize it yourself, or you
> >>>> can use a supercolumn. It's effectively the same thing. Cassandra
> >>>> only provides the super column s
s the super column support as a convenience layer as it is
>>>> currently implemented. That may change in the future.
>>>>
>>>> You didn't make clear in your question why a standard column would be
>>>> less suitable. I presumed you had layered st
idn't make clear in your question why a standard column would be
>>> less suitable. I presumed you had layered structure within the
>>> timestamp, hence my response.
>>> How would you logically partition your dataset according to natural
>>> application boundar
ou had layered structure within the
>> timestamp, hence my response.
>> How would you logically partition your dataset according to natural
>> application boundaries? This will answer most of your question.
>> If you have a dataset which can't be partitioned into a reason
can't be partitioned into a reasonable
> size row, then you may want to use OPP and key concatenation.
>
> What do you mean by giant?
>
> On Wed, Jun 2, 2010 at 10:32 AM, David Boxenhorn
> wrote:
> > How do I handle giant sets of ordered data, e.g. by timestamps, which
0 at 10:32 AM, David Boxenhorn wrote:
> How do I handle giant sets of ordered data, e.g. by timestamps, which I want
> to access by range?
>
> I can't put all the data into a supercolumn, because it's loaded into memory
> at once, and it's too much data.
>
> Am
's a tradeoff here between
row width and read speed. Reading 1000 columns as a continuous slice
from a single row will be very fast but reading 1000 columns as slices
from 10 keys won't be as fast.
Ben
On Wed, Jun 2, 2010 at 11:32 AM, David Boxenhorn wrote:
> How do I handle giant se
How do I handle giant sets of ordered data, e.g. by timestamps, which I want
to access by range?
I can't put all the data into a supercolumn, because it's loaded into memory
at once, and it's too much data.
Am I forced to use an order-preserving partitioner? I don't want th
