Agreed on all points. Let's keep it simple and implement defaults and
later on if the need arises we can standardize APIs for components where
interchangeable implementations could be fitted in.
On 11-03-02 5:46 AM, Manik Surtani wrote:
On 1 Mar 2011, at 18:07, Vladimir Blagojevic wrote:
> I want to roughly outline what I believe needs to be done to implement
> basic distributed
> execution framework for 5.0 Final.
>
> If you recall, Distributed/MapReduce task is a logical work unit
> consisting of multiple distributed
> executables executed individually across Infinispan cluster. Each
> individual task execution
> on Infinispan cluster is governed by failover, load balancing and
> execution policies.
>
> Failover policy
>
> Failover policy regulates how and if distributed task executables are
> migrated to
> backup execution nodes in case of failure.
>
> Executable can fail due to:
>
> - exception raised in task implementation during execution
> - node crash/leave
> - migration failure to/from target execution node
>
> Infinispan will invoke failover mechanism in all above cases except when
> exception
> is raised by task executable itself. Exception will be returned to
> invoker of
> distributed task who can act upon it.
>
> By default, there will be two failover policies: failover off, and
> failover on.
> If failover is on load balancing policy in place will decide where to
> migrate
> task executable for execution. In case failure is off task invoker will
> be notified.
Hmm - tasks may fail for various reasons, including a badly written task which may always
fail. Maybe all you need is a simple retry count?
> Load balancing policy
>
> Load balancing policy decides how distributed task executables are
> dispersed for
> execution around Infinispan cluster. By default data collocating load
> balancing
> policy is used as soon as distributed task is invoked on a set of keys
> in cache.
> Other, simpler, load balancing policies can be implemented as well if a
> need arises.
I don't understand - I thought tasks are executed on all data owners (if any keys are
provided) or on all nodes if no keys are provided. What other "load balancing"
policies do you foresee? :-)
If it doesn't make sense maybe it would be better to remove load balancing from the
API for now, add it later if we see a need?
> Execution policy
>
> Execution policy decides how task executable is executed once it has
> been migrated
> to an execution node. By default priority queue is used for queuing of
> execution
> task executables. Users can, if needed, fine-tune task priority on per
> task basis.
> If priority is not changed for all tasks then all their executables are
> effectively
> queued fifo on execution nodes.
>
> Time permitting, job stealing policy should be implemented taking into
> account ideas
> from fork/join framework and applying it in a distributed fashion
> amongst Infinispan
> nodes.
I would say leave prioritising out for now. Lets keep this simple. This is all stuff
that can be added later if needed.
> Implementation sketches
>
>
> In order to implement distributed/mapreduce task execution I believe we
> should reuse
> existing Infinispan infrastructure (marshalling, remote command
> invocation, interceptor
> chain, thread pools) as much as possible.
>
> As user submits distributed task we would locate Infinispan nodes where
> the input keys
> are located and send executables (DistributedCallable/Mapper/Reducer) to
> those nodes
> using exisiting remote command invocation mechanism. Decision about
> migration of
> executables is effectively done by load balancing policy, the default
> one being
> collocating policy.
>
> When executables wrapped into commands arrive to Infinispan nodes they
> are handed
> off to a special handling object (execution policy) rather than
> invocation handler.
Why? If you are creating a special Command for this, the Command's perform() method
could handle execution. Saves you having to re-engineer the InvocationHandler (which is
very complex as it is).
> Execution policy interacts with execution container and in turn queues
> and monitors
> executables as they are executed in container's thread pool.
> DistributedCallable(s) are
> invoked and results returned to invoking node. Mappers are invoked as
> well and their
> results handed off to Reducers as described in mapreduce algorithm.
> Eventually a
> result of each Reducer is also returned to task invoker and in turn
> Collator is invoked.
>
> In case of task failure due to exception raised in task itself,
> exception is returned to
> task submitter.
Yes, the Command's perform() method would just have to throw an exception. The
InvocationHandler will wrap it in an appropriate ExceptionResponse to me sent back to the
caller.
> In other cases, failover policy along with load
> balancing policy decides
> how to migrate executable to other Infinispan nodes.
>
>
> If you think that I omitted something and/or have suggestion let me know.
>
> Regards,
> Vladimir
>
>
>
>
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--
Manik Surtani
manik(a)jboss.org
twitter.com/maniksurtani
Lead, Infinispan
http://www.infinispan.org
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