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From Robert Evans <ev...@yahoo-inc.com>
Subject Re: On the topic of task scheduling
Date Tue, 04 Sep 2012 13:11:51 GMT
The other thing to point out too is that in order to solve this problem
perfectly you litterly have to solve the halting problem.  You have to
predict if the maps are going to finish quickly or slowly.  If they finish
quickly then you want to launch reduces quickly to start fetching data
from the mappers, if they are going to finish very slowly, then you have a
lot of reducers taking up resources not doing anything.  That is why there
is the config parameter that can be set on a per job basis to tell the AM
when to start launch maps.  We have actually been experimenting with
setting this to 100% because it improves utilization of the cluster a lot.
But be careful there are a lot of bug that you might run into if you do
this.  I think we have fixed al of them, but I don't know how many have
been merged into 2.1 and how many are still sitting on 2.2.

--Bobby

On 9/2/12 1:46 PM, "Arun C Murthy" <acm@hortonworks.com> wrote:

>Vasco,
>
> Welcome to Hadoop!
>
> You observations are all correct - in simplest case you launch all
>reduces up front (we used to do that initially) and get a good 'pipeline'
>between maps, shuffle (i.e. moving map-outputs to reduces) and the reduce
>itself.
>
> However, one thing to remember is that keeping reduces up and running
>without sufficient maps being completed is a waste of resources in the
>cluster. As a result, we have a simple heuristic in hadoop-1 i.e. do not
>launch reduces until a certain percentage of the job's maps are complete
>- by default it's set to 5%. However, there still is a flaw with it
>(regardless of what you set it to be i.e. 5% or 50%). If it's too high,
>you lose the 'pipeline' and too low (5%), reduces still spin waiting for
>all maps to complete wasting resources in the cluster.
>
> Given that, we've implemented the heuristic you've described below for
>hadoop-2 which is better at balancing resource-utilization v/s pipelining
>or job latency.
>
> However, as you've pointed out there are several improvements which are
>feasible. But, remember that the complexity involved has on a number of
>factors you've already mentioned:
> # Job size (a job with 100m/10r v/s 100000m/10000r)
> # Skew for reduces
> # Resource availability i.e. other active jobs/shuffles in the system,
>network bandwidth etc.
>
> If you look at an ideal shuffle it will look so (pardon my primitive
>scribble):
> http://people.apache.org/~acmurthy/ideal-shuffle.png
>
> From that graph:
> # X i.e. when to launch reduces depends on resource availability, job
>size & maps' completion rate.
> # Slope of shuffles (red worm) depends on network b/w, skew etc.
>
> None of your points are invalid - I'm just pointing out the
>possibilities and complexities.
>
> Your points about aggregation are also valid, look at
>http://code.google.com/p/sailfish/ for e.g.
>
> One of the advantages of hadoop-2 is that anyone can play with these
>heuristics and implement your own - I'd love to help if you are
>interested in playing with them.
>
> Related jiras:
> https://issues.apache.org/jira/browse/MAPREDUCE-4584
>
>hth,
>Arun
>
>On Sep 2, 2012, at 9:34 AM, Vasco Visser wrote:
>
>> Hi,
>> 
>> I am new to the list, I am working with hadoop in the context of my
>> MSc graduation project (has nothing to do with task scheduling per
>> se). I came across task scheduling because I ran into the fifo
>> starvation bug (MAPREDUCE-4613). Now, I am running 2.1.0 branch where
>> the fifo starvation issue is solved. The behavior of task scheduling I
>> observe in this branch is as follows. It begins with all containers
>> allocated to mappers. Pretty quickly reducers are starting to be
>> scheduled. In a linear way more containers are given to reducers,
>> until about 50% (does anybody know why 50%?) of available containers
>> are reducers (this point is reached when ~ 50% of the mappers are
>> finished). It stays ~50-50 for until all mappers are scheduled. Only
>> then the proportion of containers allocated to reducers is increased
>> to > 50%.
>> 
>> I don't think this is in general quite the optimal (in terms of total
>> job completion time) scheduling behavior. The reason being that the
>> last reducer can only be scheduled when a free container becomes
>> available after all mappers are scheduled. Thus, in order to shorten
>> total job completion time the last reducer must be scheduled as early
>> as possible.
>> 
>> For the following gedankenexperiment, assume # reducer is set to 99%
>> capacity, as suggested somewhere in the hadoop docs, and that each
>> reducer will process roughly the same amount of work. I am going to
>> schedule as in 2.1.0, but instead of allocating reducers slowly up to
>> 50 % of capacity, I am just going to take away containers. Thus, the
>> amount of map work is the same as in 2.1.0, only no reduce work will
>> be done. At the point that the proportion of reducers would increased
>> to more than 50% of the containers (i.e., near the end of the map
>> phase), I schedule all reducers in the containers I took away, making
>> sure that the last reducer is scheduled at the same moment as it would
>> be in 2.1.0.  My claim is that the job completion time of this
>> hypothetical scheduling is about the same as the scheduling in 2.1.0
>> (as the last reducer is scheduled at the same time), even though I
>> took away 50% of the available resources for a large part of the job!
>> The conclusion is that it would be better to allocate all available
>> containers to mappers, and that reducers are starting to be scheduled
>> when the map phase is nearing its end, instead of right at the
>> beginning of the job.
>> 
>> Scheduling reducers early seems to me the way to go only when: 1) the
>> output from mappers is very skewed, i.e., some reducers are expected
>> to need much more time than others, 2) the network connection between
>> nodes is (expected to be) a big bottleneck, i.e., schedule reducers
>> early to smear out data transfer over the lifetime of a job, or 3)
>> there is no contention for resource containers.
>> 
>> with regard to point 1: skewedness can be determined by looking at
>> relative sizes of partitioned mapper output.
>> 
>> with regard to point 2: I think the network is only a bottleneck if it
>> feeds tuples slower than the reducer can merge sort the tuples (am I
>> right?). Also, it might be a nice optimization to transfer the
>> intermediate data to the machine that is going/likely to run a
>> specific reducer before the reducer is actually ran there (e.g.,
>> something like a per machine prefetch manager?). A per machine task
>> scheduling queue would be needed for this, to determine where a
>> reducer is going/likely to be scheduled.
>> 
>> Just my two cents. I'm interested in hearing opinions on this matter.
>> 
>> Regards, Vasco
>
>--
>Arun C. Murthy
>Hortonworks Inc.
>http://hortonworks.com/
>
>


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