Hi, Josh,
Thanks for walking me through this. This is my first stab at it:
public class RowSummingCombiner extends WrappingIterator {
Key lastKey;
long sum;
public Key getTopKey() {
if (lastKey == null)
return super.getTopKey();
return lastKey;
}
public Value getTopValue() {
lastKey = null;
return new Value(Long.toString(sum).getBytes());
}
public boolean hasTop() {
return lastKey != null || super.hasTop();
}
public void next() throws IOException {
while (super.hasTop()) {
lastKey = super.getTopKey();
if (!lastKey.isDeleted()) {
sum += Long.parseLong(super.getTopValue().toString());
}
super.next();
}
}
public SortedKeyValueIterator<Key,Value> deepCopy(IteratorEnvironment
env) {
RowSummingCombiner instance = new RowSummingCombiner();
instance.setSource(getSource().deepCopy(env));
return instance;
}
}
I restrict the scanner to the single CF/CQ that I'm interested in
summing. The biggest disadvantage is that I can't utilize any of the
logic in the Combiner class hierarchy for value decoding etc. because
the logic to "combine" based on the common (row, cf, cq, vis) tuple is
baked in at the top level of that hierarchy and I don't see an easy way
to plug in new behaviour. But, each instance of the RowSummingCombiner
returns its own sum, and then my client just has to add up a handful of
values. Is this what you were getting at?
Regards,
-Russ
On Wed, Mar 19, 2014 at 3:51 PM, Josh Elser <[email protected]
<mailto:[email protected]>> wrote:
Ummm, you got the gist of it (I may have misspoke in what I
initially said).
What my first thought was to make an iterator that will filter down
to the columns that you want. It doesn't look like we have an
iterator that will efficiently do this for you included in the core
(although, I know I've done something similar in the past like
this). This iterator would scan the rows on your table returning
just the columns you want.
000200001ccaac30 meta:size [] 1807
000200001cdaac30 meta:size [] 656
000200001cfaac30 meta:size [] 565
Then, we could put the summing combiner on top of that iterator to
sum those and get back a single key. The row in the key you return
should be the last row you included in the sum. This way, if a retry
happens under the hood by the batchscanner, you'll resume where you
left off and won't double-count things.
(you could even do things like sum a maximum of N rows before
returning back some intermediate count to better parallelize things)
000200001cfaac30 meta:size [] 3028
So, each "ScanSession" (what the batchscanner is doing underneath
the hood) would return you a value which your client would do a
final summation.
The final stack would be {(data from accumulo) > SKVI to project
columns > summing combiner} > final summation, where {...} denotes
work done server-side. This is one of those things that really
shines with the Accumulo API.
On 3/19/14, 6:40 PM, Russ Weeks wrote:
Hi, Josh,
Thanks very much for your response. I think I get what you're
saying,
but it's kind of blowing my mind.
Are you saying that if I first set up an iterator that took my
key/value
pairs like,
000200001ccaac30 meta:size [] 1807
000200001ccaac30 meta:source [] data2
000200001cdaac30 meta:filename [] doc02985453
000200001cdaac30 meta:size [] 656
000200001cdaac30 meta:source [] data2
000200001cfaac30 meta:filename [] doc04484522
000200001cfaac30 meta:size [] 565
000200001cfaac30 meta:source [] data2
000200001dcaac30 meta:filename [] doc03342958
And emitted something like,
0 meta:size [] 1807
0 meta:size [] 656
0 meta:size [] 565
And then applied a SummingCombiner at a lower priority than that
iterator, then... it should work, right?
I'll give it a try.
Regards,
-Russ
On Wed, Mar 19, 2014 at 3:33 PM, Josh Elser
<[email protected] <mailto:[email protected]>
<mailto:[email protected] <mailto:[email protected]>>>
wrote:
Russ,
Remember about the distribution of data across multiple
nodes in
your cluster by tablet.
A tablet, at the very minimum, will contain one row. Any
way to say
that same thing is that a row will never be split across
multiple
tablets. The only guarantee you get from Accumulo here is
that you
can use a combiner to do you combination across one row.
However, when you combine (pun not intended) another SKVI
with the
Combiner, you can do more merging of that intermediate
"combined
value" from each row before returning back to the client.
You can
think of this approach as doing a multi-level summation.
This still requires one final sum on the client side, but
you should
get quite the reduction with this approach over doing the
entire sum
client side. You sum the meta:size column in parallel
across parts
of the table (server-side) and then client-side you sum the
sums
from each part.
I can sketch this out in more detail if it's not clear. HTH
On 3/19/14, 6:18 PM, Russ Weeks wrote:
The accumulo manual states that combiners can be
applied to
values which
share the same rowID, column family, and column
qualifier. Is
there any
way to adjust this behaviour? I have rows that look
like,
000200001ccaac30 meta:size [] 1807
000200001ccaac30 meta:source [] data2
000200001cdaac30 meta:filename [] doc02985453
000200001cdaac30 meta:size [] 656
000200001cdaac30 meta:source [] data2
000200001cfaac30 meta:filename [] doc04484522
000200001cfaac30 meta:size [] 565
000200001cfaac30 meta:source [] data2
000200001dcaac30 meta:filename [] doc03342958
and I'd like to sum up all the values of meta:size
across all
rows. I
know I can scan the sizes and sum them on the client
side, but I was
hoping there would be a way to do this inside my
cluster. Is
mapreduce
my only option here?
Thanks,
-Russ
