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From Jonathan Ellis <>
Subject Re: Questions about Cassandra reads
Date Tue, 12 Jul 2011 23:22:54 GMT
Thanks for the update, that is very useful!

On Tue, Jul 12, 2011 at 3:16 PM, Philippe <> wrote:
> Hi Jonathan,
> Thanks for the answer, I wanted to report on the improvements I got because
> someone else is bound to run into the same questions...
>> > C) I want to access a key that is at the 50th position in that table,
>> > Cassandra will seek position 0 and then do a sequential read of the file
>> > from there until it finds the key, right ?
>> Sequential read of the index file, not the data file.
> and then it will seek directly to the right position in the data file ?
>> > J) I've considered writing a partitioner that will chunk the rows
>> > together
>> > so that queries for "close" rows go to the same replica on the ring.
>> > Since
>> > the rows have close keys, they will be close together in the file and
>> > this
>> > will increase OS cache efficiency.
>> Sounds like ByteOrderedPartitioner to me.
> I indeed ended up using just that
>> > What do you think ?
>>  I think you should strongly consider denormalizing so that you can
>> read ranges from a single row instead.
> Yes, that's what I did : I took a hard look at the data and the acces
> pattern and sliced away at everything I could.
> Given that I am storing data in a quad tree and that I have strong locality
> in my read-pattern, I ended up using the morton (z-order) code as the key
> and using super-columns to only get the column groups I'm interested in.
> I gave some thought on how to balance the tree because I have 10 different
> levels of data in the quadtree and I am doing tricks with shifts to reuse
> the same prefixes in the keys.
> What I think is worth noting for others on the mailing list is that doing
> this resulted in a x50 to x100 increase in read performance and my IO is now
> down to virtually nothing (I can basically see the OS load up the pages in
> its cache).
> I also found out that one big multiget is more efficient that a couple range
> queries in my case.
> So
>  - instead of a steady rate of 280/350MB/s of disk reads I get 100MB/s every
> so often
>  - instead of seeing my cluster melt down at 3 concurrent clients, it's now
> speeding along just fine at 50 concurrent clients
> :)

Jonathan Ellis
Project Chair, Apache Cassandra
co-founder of DataStax, the source for professional Cassandra support

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