Hi Takenori,
I can't tell for sure without knowing what kind of data you have and how
much you have.You can use the random partitioner and use the concept of
metadata row that stores the row key, as for example like below
{metadata_row}: key1 | key2 | key3
key1:column1 | column2
When you do
Hi Manoj,
Thanks for your advise.
More or less, basically we do the same. As you pointed out, we now face
with many cases that can not be solved by data modeling, and which are
reaching to 100 millions of columns.
We can split them down to multiple pieces of metadata rows, but that will
bring
It can handle some millions of columns, but not more like 10M. I mean, a
request for such a row concentrates on a particular node, so the
performance degrades.
I also had idea for semi-ordered partitioner - instead of single MD5, to
have two MD5's.
works for us with wide row with about 40-50 M,
my five cents -
token and key are not same. it was like this long time ago (single MD5
assumed single key)
if you want ordered, you probably can arrange your data in a way so you can
get it in ordered fashion.
for example long ago, i had single column family with single key and about
2-3 M
Hi Nick,
token and key are not same. it was like this long time ago (single MD5
assumed single key)
True. That reminds me of making a test with the latest 1.2 instead of our
current 1.0!
if you want ordered, you probably can arrange your data in a way so you
can get it in ordered fashion.
