Hi Roland,
You state the following:
*"...talks about Channel since that was only needed to contain the
side-effecting replay nature of command sourced processors."*
A channel is also needed when sending events from an eventsourced processor
to another actor when one needs at-least-once delivery rather
than at-most-once:
def receiveRecover: Receive = {
case event: String => handleEvent(event)
}
def handleEvent(event: String) = {
// update state
// ...
// reliably deliver events
channel ! Deliver(Persistent(event), destination.path)
// alternatively something that encapsulates this pair
}
We are dependent on this very functionality for reliable coordination in
our apps and removing Channel leaves us worried.
*"...Your description below is rather terse, so it is not fully clear to me
how you are using Channel in this case and what a replacement should be,
can you elaborate?"*
As stated earlier, we have many eventsourced processors of same type, e.g.
10000 instances of "Earnings", that are loaded on demand by a supervisor
when a command enters into the domain. When an instance is loaded the
command is forwarded to this guy and when the command
results in domain events we acknowledge to sender in the persist callback
*but* also forward the persistent message to another actor, let's call it
"stream",
that needs to react to this. It ensures that a calculation is initiated by
delivering a message to a "change reactor" via a unique channel.
Furthermore "stream" also ensures that a message is delivered, via another
unique channel, to an EarningsAggregator that essentially replicates all of
those 10000 instances' events, because we need a global view in order to
maintain read models.
In receiveRecover we *also* forward replayed events to "stream" in order to
ensure that messages get delivered, via channels, to the respective
destinations,
in the case of a JVM crash.
During system start up we start all recently active (say last 24 hours)
Earnings instances to ensure that all calculations are triggered and that
our EarningsAggregator
gets the event, again in the name of crash paranoia. These instances shut
themselves down after a reasonable receive timeout in order to keep memory
footprint low.
This roughly happens by sending a "passivate me" message to their
supervisor which in turn ensures a proper shutdown and all of the
instance's messages are
processed by using a combination of poison pill, become, stash and
listening to terminated.
In the above I mention Earnings eventsourced processor, however we have
around 10 similar types under same supervisor that are similarly structured
and have the same
need of what a channel gives us.
Other usages of channels that we have are communication among processes.
Often here it is perfectly fine for us to use channels because it ensures
that something eventually
will happen, some eventsourced processor emits an event to reactor
(ordinary actor) via a channel (also during receiveRecover) that transforms
the event to a command in the
language of another eventsourced processor. All of such processes are
idempotent and maintaining an queue on both sides is just a lot of useless
busywork in most of our
use cases.
I have no idea of what a replacement of Channel would be because I do not
have an issue with it and think it is a good primitive that solves many of
our use cases in ways
that are satisfying and saves us a lot of work trying to re-implement the
same functionality.
*"...My motivation here is to not remove needed functionality without
improved replacement."*
That is good to know :)
On Tuesday, May 20, 2014 7:32:38 PM UTC+1, rkuhn wrote:
>
> Hi Alex,
>
> I have filed the ticket for Processor’s removal (
> https://github.com/akka/akka/issues/15230), which also talks about
> Channel since that was only needed to contain the side-effecting replay
> nature of command sourced processors. Your description below is rather
> terse, so it is not fully clear to me how you are using Channel in this
> case and what a replacement should be, can you elaborate?
>
> There is also the discussion ticket for reinventing PersistentChannel (
> https://github.com/akka/akka/issues/15231) which might be of interest in
> this context. My motivation here is to not remove needed functionality
> without improved replacement.
>
> Regards,
>
> Roland
>
> 9 maj 2014 kl. 12:15 skrev ahjohannessen <[email protected]<javascript:>
> >:
>
> Hi Roland,
>
> We use Channel in conjunction with Eventsourced Processor (EP) in our
> applications in receiveRecover.
> It would be sad to see it go away without a reasonable alternative.
>
> One scenario in our apps is that we use DDD/ES and have a lot of EPs of
> same type, e.g. 10000 instances,
> that are loaded on demand by a supervisor.
>
> In order to have a single view on all of these, we inject an actor that
> wraps a single channel / aggregator EP combo
> into these instances on creation. This makes it possible to react to
> changes, even in case of JVM crashes,
> in that family of EPs as well as having a global view of that family.
>
>
> On Friday, May 9, 2014 9:13:17 AM UTC+1, rkuhn wrote:
>>
>>
>> 9 maj 2014 kl. 09:58 skrev Martin Krasser <[email protected]>:
>>
>>
>> On 09.05.14 09:25, Roland Kuhn wrote:
>>
>>
>> 9 maj 2014 kl. 09:08 skrev Martin Krasser <[email protected]>:
>>
>>
>> On 09.05.14 08:41, Roland Kuhn wrote:
>>
>> Hi Martin,
>>
>> 9 maj 2014 kl. 08:05 skrev Martin Krasser <[email protected]>:
>>
>> Hi Roland,
>>
>> thanks for starting a discussion on this. Here are some initial thoughts
>> on your proposal:
>>
>> "... very same throughput optimization by applying the state changes
>> before persisting them ..."
>>
>> I think we agree that whatever changes are going to be made in the
>> future, we must keep the throughput optimizations (by batching
>> writes/updates). As you said, with an EP, this can only be achieved by
>> applying events to current state *before* persisting them. Furthermore, to
>> enable batching, an EP must therefore be able to process new commands while
>> (previous) events are about to be persisted. This however has a very
>> important consequence for commands that read current state. If we allow
>> events to be applied to current state *before* persisting them, we allow
>> clients to read state from that EP that may not be re-readable after a
>> crash. For example:
>>
>> - EP receives update command, derives event and applies it immediately to
>> current state
>> - EP (asynchronously) persists event
>> - EP receives a read command (while event persistence is in progress)
>> - EP (successfully) returns read response to requestor
>> - EP JVM crashes before event was successfully persisted
>> - EP state cannot be reconstructed i.e. previous read cannot be repeated.
>>
>>
>> This is only true if the recovery is incomplete: the update command
>> will not have been acknowledged at this point, so if someone cared about it
>> they will send it again during recovery and the EP will eventually end up
>> in a state where the read will return the same value again. If this type of
>> consistency is not good enough, then you can always defer reads within the
>> write model until after persistence is completed, meaning that the read is
>> only performed once a corresponding read “event” has gone through the
>> journal. We could allow events that are only looped through to make this
>> work, just like non-Persistent commands are looped today (and for the same
>> reason).
>>
>>
>> Delaying reads is only an option when reads are made via messages to a
>> (E)P. If my processor manages state via an STM ref where only the processor
>> updates the STM ref but reads go directly to the STM ref, then you cannot
>> delay reads.
>>
>>
>> In this scenario you would delay updating the STM ref until after the
>> persistence loop, which is exactly the same as for a current
>> command-sourced Processor: the read gets delayed until after the writes are
>> processed, in the same way the STM ref update gets delayed by the write
>> having to go through the journal. Effects, consistency and latency are the
>> same in both implementations.
>>
>>
>> That's true. So, to achieve
>>
>> - repeatable reads
>> - low read latency and
>> - high write throughput
>>
>> reads can go to the STM refs directly and EP must update the STM ref only
>> after having persisted the events. If one *additionally* wants to achieve
>>
>> - read-your-own-write consistency (assuming a client issues an update
>> command, immediately followed by a read command)
>>
>> one would need a way to loop read commands through the journal as well
>> before serving them (which probably requires an addition to the API then).
>> Alternatively, a client only issues a read after having received a
>> write-ack (at the cost of an additional roundtrip).
>>
>>
>> This is an interesting remark: normally read-your-writes is only
>> guaranteed for reads submitted after having received the ACK for the write,
>> so what we are providing here is actually a qualitative improvement on that
>> status quo that is only possible in Reactive systems (normally the ACK is
>> signaled by a synchronous non-exceptional method return).
>>
>> Anyway, I think you convinced me, as usual :) Great proposal, Dr Kuhn!
>>
>>
>> And as usual you helped in refining the proposal: the addition of looping
>> non-persistent events through the journal is an important one, thanks for
>> providing the use-case!
>>
>> So, to summarize, we can incorporate all current functionality provided
>> by Processor and Channel into EventsourcedProcessor by adding the following
>> two features:
>>
>>
>> - the ability to opt out of stashing everything while waiting for
>> persist()ing
>> - the ability to loop non-persistent events through the journal
>>
>>
>> Everyone, please consider what this would mean for your code base and
>> comment, now is the right time to speak up! The same goes for opinions on
>> whether PersistentChannel pulls its weight or not (as argued earlier in
>> this thread).
>>
>> Regards,
>>
>> Roland
>>
>>
>> Cheers,
>> Martin
>>
>> --
>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>> >>>>>>>>>> Check the FAQ:
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>>
>>
>>
>> *Dr. Roland Kuhn*
>> *Akka Tech Lead*
>> Typesafe <http://typesafe.com/> – Reactive apps on the JVM.
>> twitter: @rolandkuhn
>> <http://twitter.com/#!/rolandkuhn>
>>
>>
> --
> >>>>>>>>>> Read the docs: http://akka.io/docs/
> >>>>>>>>>> Check the FAQ:
> http://doc.akka.io/docs/akka/current/additional/faq.html
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>
>
>
> *Dr. Roland Kuhn*
> *Akka Tech Lead*
> Typesafe <http://typesafe.com/> – Reactive apps on the JVM.
> twitter: @rolandkuhn
> <http://twitter.com/#!/rolandkuhn>
>
>
--
>>>>>>>>>> Read the docs: http://akka.io/docs/
>>>>>>>>>> Check the FAQ:
>>>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>>>>>> Search the archives: https://groups.google.com/group/akka-user
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