On 22/09/14 17:06, Joe Gordon wrote:
On Mon, Sep 22, 2014 at 9:58 AM, Zane Bitter <zbit...@redhat.com> wrote:

On 22/09/14 10:11, Gordon Sim wrote:

On 09/19/2014 09:13 PM, Zane Bitter wrote:

SQS offers very, very limited guarantees, and it's clear that the reason
for that is to make it massively, massively scalable in the way that
e.g. S3 is scalable while also remaining comparably durable (S3 is
supposedly designed for 11 nines, BTW).

Zaqar, meanwhile, seems to be promising the world in terms of
guarantees. (And then taking it away in the fine print, where it says
that the operator can disregard many of them, potentially without the
user's knowledge.)

On the other hand, IIUC Zaqar does in fact have a sharding feature
("Pools") which is its answer to the massive scaling question.


There are different dimensions to the scaling problem.


Many thanks for this analysis, Gordon. This is really helpful stuff.

  As I understand it, pools don't help scaling a given queue since all the
messages for that queue must be in the same pool. At present traffic
through different Zaqar queues are essentially entirely orthogonal
streams. Pooling can help scale the number of such orthogonal streams,
but to be honest, that's the easier part of the problem.


But I think it's also the important part of the problem. When I talk about
scaling, I mean 1 million clients sending 10 messages per second each, not
10 clients sending 1 million messages per second each.

When a user gets to the point that individual queues have massive
throughput, it's unlikely that a one-size-fits-all cloud offering like
Zaqar or SQS is _ever_ going to meet their needs. Those users will want to
spin up and configure their own messaging systems on Nova servers, and at
that kind of size they'll be able to afford to. (In fact, they may not be
able to afford _not_ to, assuming per-message-based pricing.)


Running a message queue that has a high guarantee of not loosing a message
is hard and SQS promises exactly that, it *will* deliver your message. If a
use case can handle occasionally dropping messages then running your own MQ
makes more sense.

SQS is designed to handle massive queues as well, while I haven't found any
examples of queues that have 1 million messages/second being sent or
received  30k to 100k messages/second is not unheard of [0][1][2].

[0] https://www.youtube.com/watch?v=zwLC5xmCZUs#t=22m53s
[1] http://java.dzone.com/articles/benchmarking-sqs
[2]
http://www.slideshare.net/AmazonWebServices/massive-message-processing-with-amazon-sqs-and-amazon-dynamodb-arc301-aws-reinvent-2013-28431182

Thanks for digging those up, that's really helpful input. I think number [1] kind of summed up part of what I'm arguing here though:

"But once your requirements get above 35k messages per second, chances are you need custom solutions anyway; not to mention that while SQS is cheap, it may become expensive with such loads."

  There is also the possibility of using the sharding capabilities of the
underlying storage. But the pattern of use will determine how effective
that can be.

So for example, on the ordering question, if order is defined by a
single sequence number held in the database and atomically incremented
for every message published, that is not likely to be something where
the databases sharding is going to help in scaling the number of
concurrent publications.

Though sharding would allow scaling the total number messages on the
queue (by distributing them over multiple shards), the total ordering of
those messages reduces it's effectiveness in scaling the number of
concurrent getters (e.g. the concurrent subscribers in pub-sub) since
they will all be getting the messages in exactly the same order.

Strict ordering impacts the competing consumers case also (and is in my
opinion of limited value as a guarantee anyway). At any given time, the
head of the queue is in one shard, and all concurrent claim requests
will contend for messages in that same shard. Though the unsuccessful
claimants may then move to another shard as the head moves, they will
all again try to access the messages in the same order.

So if Zaqar's goal is to scale the number of orthogonal queues, and the
number of messages held at any time within these, the pooling facility
and any sharding capability in the underlying store for a pool would
likely be effective even with the strict ordering guarantee.


IMHO this is (or should be) the goal - support enormous numbers of
small-to-moderate sized queues.


If 50,000 messages per second doesn't count as small-to-moderate then Zaqar
does not fulfill a major SQS use case.

It's not a drop-in replacement, but as I mentioned you can recreate the SQS semantics exactly *and* get the scalability benefits of that approach by sharding at the application level and then round-robin polling.

As I also mentioned, this is pretty easy to implement, and is only required for really big applications that are more likely to be written by developers who already Know What They're Doing(TM). While the reverse (emulating Zaqar semantics, i.e. FIFO, in SQS) is tricky, error-prone, and conceivably required by or at least desirable for all kinds of beginner-level applications. (It's also pretty useful for a lot of use cases in OpenStack itself, where OpenStack services are sending messages to the user.)

  If scaling the number of communicants on a given communication channel
is a goal however, then strict ordering may hamper that. If it does, it
seems to me that this is not just a policy tweak on the underlying
datastore to choose the desired balance between ordering and scale, but
a more fundamental question on the internal structure of the queue
implementation built on top of the datastore.


I agree with your analysis, but I don't think this should be a goal.

Note that the user can still implement this themselves using
application-level sharding - if you know that in-order delivery is not
important to you, then randomly assign clients to a queue and then poll all
of the queues in the round-robin. This yields _exactly_ the same semantics
as SQS.


The reverse is true of SQS - if you want FIFO then you have to implement
re-ordering by sequence number in your application. (I'm not certain, but
it also sounds very much like this situation is ripe for losing messages
when your client dies.)

So the question is: in which use case do we want to push additional
complexity into the application? The case where there are truly massive
volumes of messages flowing to a single point? Or the case where the
application wants the messages in order?

I'd suggest both that the former applications are better able to handle
that extra complexity and that the latter applications are probably more
common. So it seems that the Zaqar team made a good decision.


If Zaqar is supposed to be comparable to amazon SQS, then it has picked the
wrong choice.

It has certainly picked a different choice. It seems like a choice that is friendlier to beginners and simple applications, while shifting some complexity to larger applications without excluding them as a use case. That's certainly not an invalid choice.

It isn't OpenStack's job to be cloning AWS services after all... we can definitely address the same problems better when we see the opportunity. We should, of course, think very carefully about all the consequences, intended and unintended, of changing a model that is already proven in the field and the market, so I'm very glad this discussion is happening. But after digging into it, the choice doesn't seem "wrong" to me.

cheers,
Zane.

(Aside: it follows that Zaqar probably should have a maximum throughput
quota for each queue; or that it should report usage information in such a
way that the operator could sometimes bill more for a single queue than
they would for the same amount of usage spread across multiple queues; or
both.)

  I also get the impression, perhaps wrongly, that providing the strict
ordering guarantee wasn't necessarily an explicit requirement, but was
simply a property of the underlying implementation(?).


I wasn't involved, but I expect it was a bit of both (i.e. it is a
chicken/egg question).

cheers,
Zane.


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