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From Fabian Hueske <fhue...@apache.org>
Subject Re: long runtime
Date Thu, 25 Sep 2014 08:51:53 GMT
Hi,

the plan shows all operator DOPs as 1.
Did you create the plan locally or on the cluster with the correct DOP? The
CLI client offers the -p parameter also for "info -e".

BTW, you could try to set the DOP to the number of cores in your cluster.
(But that doesn't explain why the job is so slow).

2014-09-25 10:01 GMT+02:00 Florian Hönicke <rockstarflo@gmail.com>:

>  yes. I ran the massJoin on the cluster as well on 500MB.
> I attached the execution plan.
>
> Greetings,
> Florian
>
>
> Am 25.09.2014 um 00:41 schrieb Fabian Hueske:
>
>  OK, the log shows that the tasks are evenly distributed to all nodes.
> I assume you run the program on the cluster as well on 500MB, right?
>
>  Can you please also post the execution plan for the cluster execution?
> You get it with (See also:
> http://flink.incubator.apache.org/docs/0.6-incubating/cli.html):
> ./flink info -e jarfile.jar <parameters>
>
>  Thanks, Fabian
>
> 2014-09-25 0:21 GMT+02:00 Florian Hönicke <rockstarflo@gmail.com>:
>
>>  Thanks for your quick answer.
>> In the following, I roughly sketch the mass-join algorithm.
>> http://www.cs.berkeley.edu/~jnwang/papers/icde14_massjoin.pdf
>> It's a R-S-Join which i modified to a self-join.
>> Given a set of token sets. The massJoin finds all similar sets (regarding
>> to the Jaccard Similarity(intersection/union))
>> First, it calculates a global token grouping, i.e., each to token is
>> grouped in one of 30 groups. Each group has almost the same token count.
>> Than, it generates two types of signatures for each input set.
>> If two sets are similar, they must share a common signature.
>> In the next step, we find all candidate pairs (pairs which share a common
>> signature).
>> Some candidate pairs are filtered using the global token grouping.
>> The remaining candidate pairs are verified to filter out all dissimilar
>> pairs.
>>
>> @Fabian
>> I specified the DOP via the command-line client as follows:
>> /home/hoenicke/flink-0.6-incubating/bin/flink run -p 11
>> /home/hoenicke/flink-0.6-incubating/jar/mass6.jar 0.9 \
>> file:///home/hoenicke/flink-0.6-incubating/input/inputNummeriert.txt
>> file:///home/hoenicke/flink-0.6-incubating/output -v
>>
>> The log file is attached.
>>
>> Best, Florian
>>
>> Am 24.09.2014 um 22:45 schrieb Fabian Hueske:
>>
>>  Hi,
>>
>>  how did you specify the degree of parallelism DOP for your program?
>> Via the command-line client or system-configuration or otherwise?
>>
>>  The JobManager log file (./log/*jobManager*.log) contains you the DOP
>> of each task.
>>
>>  Best, Fabian
>>
>> 2014-09-24 18:41 GMT+02:00 Stephan Ewen <sewen@apache.org>:
>>
>>> Hi!
>>>
>>>  Ad-hoc, that is not easy to say. It depends on your algorithm, how
>>> much data replication it does...
>>>
>>>  We'd need a bit of time to look into the code. It would help if you
>>> could roughly sketch the algorithm for us and give us a breakdown of how
>>> much time is spent in which operator (like a screenshot of the runtime web
>>> monitor).
>>>
>>>  Greetings,
>>> Stephan
>>>
>>>
>>> On Wed, Sep 24, 2014 at 6:18 PM, Florian Hönicke <rockstarflo@gmail.com>
>>> wrote:
>>>
>>>> Hello :)
>>>>
>>>> my Flink program is extreme slow.
>>>> I implemented a set similarity join in Flink (Mass-Join).
>>>> Furthermore, I implemented a local version in Java.
>>>> I compared both Implementations.
>>>> The Local version needs one minute to compute a 500MB Dataset.
>>>> My Flink program needs 5 minutes (cluster: 11 nodes, 20 000 MB RAM).
>>>> I use the Flink version 0.6.
>>>> What could be the cause?
>>>>
>>>> I would welcome your response,
>>>> Florian Hönicke
>>>>
>>>
>>>
>>
>>
>
>

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