There are 2 modes of doing iterations.
1) Client controlled iterations (aka Spark)  where the client runs a DAG with 1 or more iterations
and upon their completion, the client determines the termination condition. If the condition
is not met then the client submits more DAGs until it is so. With Tez session and shared object
model to share data between tasks, this model can be efficiently supported. The final write
of each set of iterations has to be in some distributed store, e.g. HDFS. We have seen this
model work with comparable performance to Spark (when doing tests on an experimental prototype
of Spark with Tez). 11 edges can be used to make per vertex iterations really fast.
2) Job controlled iterations (aka Flink)  where the job itself determines the termination
state and adds more iterations as needed. This is currently not support in Tez (addition of
vertices or early DAG exit) but there are jiras open for those items.
Bikas
Original Message
From: Johannes Zillmann [mailto:jzillmann@googlemail.com]
Sent: Wednesday, March 25, 2015 10:24 AM
To: dev@tez.apache.org
Subject: Re: Which computation model does Tez supports
Hey Hitesh, thanks for you thoughts!
In one chooses the multivertex approach, i guess there is no simple thing one could do to
achieve niterations where n is flexible based on the output of the n1 iteration.
So you can't do
 do max 20 iterationos
 stop in case certain conditions are met
!?
Johannes
> On 25 Mar 2015, at 18:05, Hitesh Shah <hitesh@apache.org> wrote:
>
> Hi Johannes,
>
> You would likely not avoid it if you went with the approach of multiple DAGs. For most
iterative programs, you do need to checkpoint at some point. The checkpoint would likely need
to be reliable to reduce the amount of recomputation needed if the check pointed data is
lost. An option would be to use something like the HDFS inmemory storage tier ( which lazily
persists to disk ) to reduce the perf overhead. Also, in terms of loop unrolling, a single
DAG could be preconstructed to run multiple iterations using multiple vertices and then use
the final vertex of the DAG as a checkpointing mechanism after N iterations/vertices.
>
> Also, depending on the amount of data being written out, the overhead of writing to HDFS
may not be too high. Furthermore, with Tez sessions, there is no real overhead of launching
a new DAG ( if some containers are retained ) as compared to trying to do the same with multiple
MR jobs.
>
>  Hitesh
>
>
> On Mar 25, 2015, at 2:02 AM, Johannes Zillmann <jzillmann@googlemail.com> wrote:
>
>> Hey Gopal,
>>
>>> On 25 Mar 2015, at 05:26, Gopal Vijayaraghavan <gopalv@apache.org> wrote:
>>>
>>> Hi,
>>>
>>> Iterative algorithms are expressed as DAGs in a loop.
>>>
>>> The acyclic nature of DAGs, whether in Tez or Spark (since you
>>> mention the
>>> paper) make that the natural way to implement that  repeated
>>> application of the same operation over the same data, with a
>>> decision condition determining whether to stay in the loop or not.
>>
>> Can you point to a piece of code which implements this approach ?
>> If you each look operation is a single DAG, how would that avoid hdfs barrier ?
>>
>> Johannes
>>
>>>
>>> You might want to look at last yearÂ¹s Hadoop Summit presentations
>>> for a direct example of Iterative algorithms with Tez.
>>>
>>> http://www.slideshare.net/Hadoop_Summit/pigontezlowlatencyetlw
>>> ithbig
>>> data/25
>>>
>>>
>>> Logistic regression needs you to use a library which implements that
>>> specific algorithm [1].
>>>
>>> On that note, something which needs incremental iteration can
>>> probably be even more efficient in Tez than these approaches if you
>>> unroll the iteration as 11 edges all of the final tasks ending up generating
outputs.
>>>
>>> Cheers,
>>> Gopal
>>> [1]  https://github.com/myui/hivemall#regression
>>>
>>>
>>> On 3/24/15, 8:43 PM, "Chang Chen" <baibaichen@gmail.com> wrote:
>>>
>>>> Hi
>>>>
>>>> from the PhD Disseration
>>>> <http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS201412.pdf>
>>>> of Matei Zaharia, there are four computation models in the large
>>>> scale clusters:
>>>>
>>>>
>>>> 1. *Iterative algorithm*, such as graph processing and machine
>>>> leaning algorithm 2. *Relational query* 3. *MapReduce*, a general
>>>> parallel computation model 4. *Stream processing*,
>>>>
>>>> Obviously, Tez supports #2 and #3, but for #1 and #4, I don't see
>>>> any examples.
>>>>
>>>> As for streaming, I guess if we implement appropriate input, there
>>>> is no reason that tez can't support in theory.
>>>>
>>>> But for Machine Leaning, how do we use vertex and edge to express
>>>> *Logistic Regression*?
>>>>
>>>> Thanks
>>>> Chang
>>>
>>>
>>
>
