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From Colin Clark <co...@clark.ws>
Subject Re: Data model for streaming a large table in real time.
Date Sun, 08 Jun 2014 02:13:40 GMT
Not if you add another column to the partition key; source for example.

I would really try to stay away from the ordered partitioner if at all
possible.

What ingestion rates are you expecting, in size and speed.

--
Colin
320-221-9531


On Jun 7, 2014, at 9:05 PM, Kevin Burton <burton@spinn3r.com> wrote:


Thanks for the feedback on this btw.. .it's helpful.  My notes below.

On Sat, Jun 7, 2014 at 5:14 PM, Colin Clark <colin@clark.ws> wrote:

> No, you're not-the partition key will get distributed across the cluster
> if you're using random or murmur.
>

Yes… I'm aware.  But in practice this is how it will work…

If we create bucket b0, that will get hashed to h0…

So say I have 50 machines performing writes, they are all on the same time
thanks to ntpd, so they all compute b0 for the current bucket based on the
time.

That gets hashed to h0…

If h0 is hosted on node0 … then all writes go to node zero for that 1
second interval.

So all my writes are bottlenecking on one node.  That node is *changing*
over time… but they're not being dispatched in parallel over N nodes.  At
most writes will only ever reach 1 node a time.



> You could also ensure that by adding another column, like source to ensure
> distribution. (Add the seconds to the partition key, not the clustering
> columns)
>
> I can almost guarantee that if you put too much thought into working
> against what Cassandra offers out of the box, that it will bite you later.
>
>
Sure.. I'm trying to avoid the 'bite you later' issues. More so because I'm
sure there are Cassandra gotchas to worry about.  Everything has them.
 Just trying to avoid the land mines :-P


> In fact, the use case that you're describing may best be served by a
> queuing mechanism, and using Cassandra only for the underlying store.
>

Yes… that's what I'm doing.  We're using apollo to fan out the queue, but
the writes go back into cassandra and needs to be read out sequentially.


>
> I used this exact same approach in a use case that involved writing over a
> million events/second to a cluster with no problems.  Initially, I thought
> ordered partitioner was the way to go too.  And I used separate processes
> to aggregate, conflate, and handle distribution to clients.
>


Yes. I think using 100 buckets will work for now.  Plus I don't have to
change the partitioner on our existing cluster and I'm lazy :)


>
> Just my two cents, but I also spend the majority of my days helping people
> utilize Cassandra correctly, and rescuing those that haven't.
>
>
Definitely appreciate the feedback!  Thanks!

-- 

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