Here’s the Jira for the proposal to remove BOP (and OPP), but you can see that there is no clear consensus and that the issue is still open:
CASSANDRA-6922 - Investigate if we can drop ByteOrderedPartitioner and OrderPreservingPartitioner in 3.0 https://issues.apache.org/jira/browse/CASSANDRA-6922 You can read the DataStax Cassandra doc for why “Using an ordered partitioner is not recommended”: http://www.datastax.com/documentation/cassandra/2.0/cassandra/architecture/architecturePartitionerBOP_c.html “Difficult load balancing... Sequential writes can cause hot spots... Uneven load balancing for multiple tables” -- Jack Krupansky From: Kevin Burton Sent: Saturday, June 7, 2014 1:27 PM To: user@cassandra.apache.org Subject: Re: Data model for streaming a large table in real time. I just checked the source and in 2.1.0 it's not deprecated. So it *might* be *being* deprecated but I haven't seen anything stating that. On Sat, Jun 7, 2014 at 8:03 AM, Colin <colpcl...@gmail.com> wrote: I believe Byteorderedpartitioner is being deprecated and for good reason. I would look at what you could achieve by using wide rows and murmur3partitioner. -- Colin 320-221-9531 On Jun 6, 2014, at 5:27 PM, Kevin Burton <bur...@spinn3r.com> wrote: We have the requirement to have clients read from our tables while they're being written. Basically, any write that we make to cassandra needs to be sent out over the Internet to our customers. We also need them to resume so if they go offline, they can just pick up where they left off. They need to do this in parallel, so if we have 20 cassandra nodes, they can have 20 readers each efficiently (and without coordination) reading from our tables. Here's how we're planning on doing it. We're going to use the ByteOrderedPartitioner . I'm writing with a primary key of the timestamp, however, in practice, this would yield hotspots. (I'm also aware that time isn't a very good pk in a distribute system as I can easily have a collision so we're going to use a scheme similar to a uuid to make it unique per writer). One node would take all the load, followed by the next node, etc. So my plan to stop this is to prefix a slice ID to the timestamp. This way each piece of content has a unique ID, but the prefix will place it on a node. The slide ID is just a byte… so this means there are 255 buckets in which I can place data. This means I can have clients each start with a slice, and a timestamp, and page through the data with tokens. This way I can have a client reading with 255 threads from 255 regions in the cluster, in parallel, without any hot spots. Thoughts on this strategy? -- Founder/CEO Spinn3r.com Location: San Francisco, CA Skype: burtonator blog: http://burtonator.wordpress.com … or check out my Google+ profile War is peace. Freedom is slavery. Ignorance is strength. Corporations are people. -- Founder/CEO Spinn3r.com Location: San Francisco, CA Skype: burtonator blog: http://burtonator.wordpress.com … or check out my Google+ profile War is peace. Freedom is slavery. Ignorance is strength. Corporations are people.
