I think it comes down to how much you benefit from row range scans, and how confident you are that going forward all data will continue to use random row keys.
I'm considering using BOP as a way of working around the non indexes super column limitation. In my current schema, row keys are random UUIDs, super column names are timestamps, and columns contain a snapshot in time of directory contents, and could be quite large. If instead I use row keys that are (uuid)-(timestamp), and use a standard column family, I can do a row range query and select only specific columns. I'm still evaluating if I can do this with BOP - ideally the token would just use the first 128 bits of the key, and I haven't found any documentation on how it compares keys of different length. Another trick with BOP is to use MD5(rowkey)-rowkey for data that has non uniform row keys. I think it's reasonable to use if most data is uniform and benefits from range scans, but a few things are added that aren't/don't. This trick does make the keys larger, which increases storage cost and IO load, so it's probably a bad idea if a significant subset of the data requires it. Disclaimer - I wrote that wiki article to fill in a documentation gap, since there were no examples of BOP and I wasted a lot of time before I noticed the hex byte array vs decimal distinction for specifying the initial tokens (which to be fair is documented, just easy to miss on a skim). I'm also new to cassandra, I'm just describing what makes sense to me "on paper". FWIW I confirmed that random UUIDs (type 4) row keys really do evenly distribute when using BOP. -Bryce On Mon, 19 Dec 2011 19:01:00 -0800 Drew Kutcharian <d...@venarc.com> wrote: > Hey Guys, > > I just came across > http://wiki.apache.org/cassandra/ByteOrderedPartitioner and it got me > thinking. If the row keys are java.util.UUID which are generated > randomly (and securely), then what type of partitioner would be the > best? Since the key values are already random, would it make a > difference to use RandomPartitioner or one can use > ByteOrderedPartitioner or OrderPreservingPartitioning as well and get > the same result? > > -- Drew >
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