On Mon, Sep 22, 2014 at 5:47 PM, Zane Bitter <zbit...@redhat.com> wrote:

> 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."

If you don't require the reliability guarantees that SQS provides then
perhaps. But I would be surprised to hear that a user can set up something
with this level of uptime for less:

"Amazon SQS runs within Amazon’s high-availability data centers, so queues
will be available whenever applications need them. To prevent messages from
being lost or becoming unavailable, all messages are stored redundantly
across multiple servers and data centers." [1]

>    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.)

I'm not convinced that is as simple to implement well as you make it out to
be, now every receiver has to poll N endpoints instead of 1. How would this
work with Long Polling? What is the impact on expanding the number of
connections? How do you make this auto scale? etc.

I don't know enough about the target audience to know if adding the FIFO
guarantee is a good trade off or not. But I don't follow your use case for
OpenStack service sending messages to the user; when do these need to be in

>    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.

To me this is less about valid or invalid choices. The Zaqar team is
comparing Zaqar to SQS, but after digging into the two of them, zaqar
barely looks like SQS. Zaqar doesn't guarantee what IMHO is the most
important parts of SQS: the message will be delivered and will never be
lost by SQS. Zaqar doesn't have the same scaling properties as SQS. Zaqar
is aiming for low latency per message, SQS doesn't appear to be. So if
Zaqar isn't SQS what is Zaqar and why should I use it?

[0] ACK is sent before message is stored on disk by default.

> 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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