You don't do a scan, you do a series of gets, which I believe you can batch
into one call.
last 5 days query in pseudocode
res1 = Get( hash("2014-04-29") + "2014-04-29")
res2 = Get( hash("2014-04-28") + "2014-04-28")
res3 = Get( hash("2014-04-27") + "2014-04-27")
res4 = Get( hash("2014-04-26") + "2014-04-26")
res5 = Get( hash("2014-04-25") + "2014-04-25")
For each result you look for the particular column or columns you are
interested in
Total_usa = res1.get("c:usa") + res2.get("c:usa") + res3.get("c:usa") + ...
Total_female_usa = res1.get("c:usa:sex:f") + ...
"What happens when we add more fields? Do we just keep adding in more column
qualifiers? If so, how would we filter across columns to get an aggregate
total?"
Yes. See total_usa vs. total_female_usa above. Basically you have to pre-store
every level of aggregation you care about.
-----Original Message-----
From: Software Dev [mailto:[email protected]]
Sent: Tuesday, April 29, 2014 4:36 PM
To: [email protected]
Subject: Re: Help with row and column design
> The downside is it still has a hotspot when inserting, but when
> reading a range of time it does not
How can you do a scan query between dates when you hash the date?
> Column qualifiers are just the collection of items you are aggregating
> on. Values are increments. In your case qualifiers might look like
> c:usa, c:usa:sex:m, c:usa:sex:f, c:italy:sex:m, c:italy:sex:f,
> c:italy,
What happens when we add more fields? Do we just keep adding in more column
qualifiers? If so, how would we filter across columns to get an aggregate total?
