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From Stephan Ewen <se...@apache.org>
Subject Re: intermediate result reuse
Date Tue, 15 Sep 2015 06:25:18 GMT
ToSet should be good to use.

By default, the Iterators stream data (across memory, network, and disk),
which allows you to use very large groups (larger than memory).

With ToSet, your group naturally has to fit into memory. But in most cases
it will ;-)

On Mon, Sep 14, 2015 at 11:06 PM, Fabian Hueske <fhueske@gmail.com> wrote:

> Ah, sorry :-)
> toSet, toList, etc. are regular methods of Scala's Iterator API [1] and
> not part of Flink's API although the concrete iterator is provided by
> Flink. I am not a Scala expert, but I think it will eagerly fetch the
> contents of the function's iterator into a set (or list). This call is part
> of the user function and executed just like any other call.
>
> [1]
> http://www.scala-lang.org/api/2.10.4/index.html#scala.collection.Iterator
>
>
> 2015-09-14 22:26 GMT+02:00 Michele Bertoni <
> michele1.bertoni@mail.polimi.it>:
>
>> sorry i was not talking about that collect, I know what a collector is
>> I was talking about the outer join case where inside a cogroup you should
>> do a ToSet on left or right side and collect it to be traversable multiple
>> times
>>
>> with a toSet it is transforming (something like) a lazy iterator to a
>> list in memory: is it actually collecting something thus stopping execution
>> or is it something different?
>>
>>
>>
>>
>>
>> Il giorno 14/set/2015, alle ore 22:18, Fabian Hueske <fhueske@gmail.com>
>> ha scritto:
>>
>> Hi Michele,
>>
>> collect on DataSet and collect on a Collector within a Function are two
>> different things and have the same name by coincidence (actually, this is
>> the first time I noticed that).
>>
>> DataSet.collect() fetches a DataSet which can be distributed across
>> several TaskManagers (in a cluster) to you local client program.
>> Collector.collect() adds a value to the result of a function call. The
>> collector is used in function that can return an arbitrary number of
>> results (0 to n).
>>
>> Best,
>> Fabian
>>
>> 2015-09-14 20:58 GMT+02:00 Michele Bertoni <
>> michele1.bertoni@mail.polimi.it>:
>>
>>> Hi Stephan,
>>> I have one more question: what happens when I do collect inside a
>>> cogroup (i.e. doing an outer join) or in a groupreduce?
>>>
>>>
>>>
>>> Il giorno 13/set/2015, alle ore 02:13, Stephan Ewen <sewen@apache.org>
>>> ha scritto:
>>>
>>> Hi!
>>>
>>> In most places where you use collect(), you should be able to use a
>>> broadcast variable to the same extend. This keeps the plan as one DAG,
>>> executed in one unit, so no re-computation will happen.
>>>
>>> Intermediate result caching is actually a work that has been in progress
>>> for a while, but has stalled for a bit due to prioritization of some other
>>> issues. It will be resumed in the near future, definitely. Too many parts
>>> are already in place to not complete this feature...
>>>
>>> Greetings,
>>> Stephan
>>>
>>>
>>> On Sat, Sep 12, 2015 at 6:44 PM, Michele Bertoni <
>>> michele1.bertoni@mail.polimi.it> wrote:
>>>
>>>> ok, I think I got the point: I don’t have two execute but a collect in
>>>> some branch
>>>> I will look for a way to remove it
>>>>
>>>>
>>>> What I am doing is to keep all the elements of A that as value equal to
>>>> something in B, where B (at this point) is very small
>>>> Is it better to collect or a cogroup?
>>>>
>>>>
>>>> btw is something you expect to solve i further versions?
>>>>
>>>>
>>>> thanks
>>>> michele
>>>>
>>>>
>>>>
>>>>
>>>> Il giorno 12/set/2015, alle ore 16:27, Stephan Ewen <sewen@apache.org>
>>>> ha scritto:
>>>>
>>>> Fabian has explained it well. All functions are executed lazily as one
>>>> DAG, when "env.execute()" is called.
>>>>
>>>> Beware that there are three exceptions:
>>>>   - count()
>>>>   - collect()
>>>>   - print()
>>>>
>>>> These functions trigger an immediate program execution (they are
>>>> "eager" functions). They will execute all that is needed for produce their
>>>> result. Summing up:
>>>>
>>>> ---------------------------
>>>>
>>>> One execution in this case (result "a" is reused by "b" and "c")
>>>>
>>>> a = env.createInput() -> map() -> reduce() -> filter()
>>>>
>>>> b = a.flatmap()
>>>> c = a.groupBy() -> reduce()
>>>>
>>>> b.writeAsText()
>>>> c.writeAsCsv()
>>>>
>>>> env.execute();
>>>>
>>>> ---------------------------
>>>>
>>>> Two executions in this case ("a" is computed twice, once for "b" and
>>>> once for "c")
>>>>
>>>> a = env.createInput() -> map() -> reduce() -> filter()
>>>>
>>>> b = a -> flatmap() -> count()
>>>> c = a -> groupBy() -> reduce().collect()
>>>>
>>>> ---------------------------
>>>>
>>>> Greetings,
>>>> Stephan
>>>>
>>>>
>>>> On Sat, Sep 12, 2015 at 11:31 AM, Fabian Hueske <fhueske@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Michele,
>>>>>
>>>>> Flink programs can have multiple sinks.
>>>>> In your program, the intermediate result a will be streamed to both
>>>>> filters (b and c) at the same time and both sinks will be written at
the
>>>>> same time.
>>>>> So in this case, there is no need to materialize the intermediate
>>>>> result a.
>>>>>
>>>>> If you call execute() after you defined b, the program will compute a
>>>>> and stream the result only to b.
>>>>> If you call execute() again after you defined c, the program will
>>>>> compute a again and stream the result to c.
>>>>>
>>>>> Summary:
>>>>> Flink programs can usually stream intermediate results without
>>>>> materializing them. There are a few cases where it needs to materialize
>>>>> intermediate results in order to avoid deadlocks, but these are fully
>>>>> transparently handled.
>>>>> It is not possible (yet!) to share results across program executions,
>>>>> i.e., whenever you call execute().
>>>>>
>>>>> I suppose, you call execute() between defining b and c. If you execute
>>>>> that call, a will be computed once and both b and c are computed at the
>>>>> same time.
>>>>>
>>>>> Best, Fabian
>>>>>
>>>>> 2015-09-12 11:02 GMT+02:00 Michele Bertoni <
>>>>> michele1.bertoni@mail.polimi.it>:
>>>>>
>>>>>> Hi everybody, I have a question about internal optimization
>>>>>> is flink able to reuse intermediate result that are used twice in
the
>>>>>> graph?
>>>>>>
>>>>>> i.e.
>>>>>> a = readsource -> filter -> reduce -> something else even
more
>>>>>> complicated
>>>>>>
>>>>>> b = a filter(something)
>>>>>> store b
>>>>>>
>>>>>> c = a filter(something else)
>>>>>> store c
>>>>>>
>>>>>> what happens to a? is it computed twice?
>>>>>>
>>>>>> in my read function I have a some logging commands and I see the
>>>>>> printed twice, but it sounds strange to me
>>>>>>
>>>>>>
>>>>>>
>>>>>> thanks
>>>>>> cheers
>>>>>> michele
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>

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