As always one minute after I sent the email I found the problem!
It was that I should reassign the initial dataset:
ret = ret.union(sourceDs);
Bye,
Flavio
On Tue, Mar 17, 2015 at 5:58 PM, Flavio Pompermaier <pomperma...@okkam.it>
wrote:
> Hi Fabian,
> I was trying to use the strategy you suggested with flink 0.8.1 but it
> seems that the union of the datasets cannot be created programmatically
> because the union operator gives a name to the generated dataset that is
> the name of the calling function so that only the first dataset is read.
> My code looks like:
>
> private static DataSet<Tuple6<...> getSourceDs(ExecutionEnvironment env, final
> String outputGraph, List<String> tableNames) {
> DataSet<Tuple6<...>> ret = null;
> for (String tableName : tableNames) {
> DataSet<Tuple6<...>> sourceDs = env.createInput(new
> MyTableInputFormat(tableName))
> ....
>
> if(ret==null)
> ret = sourceDs;
> else
> ret.union(sourceDs);
> }
> return ret;
> }
>
> Is this a bug or am I'm doing something wrong?
> Thanks in advance,
> Flavio
>
> On Mon, Dec 22, 2014 at 2:42 PM, <fhue...@gmail.com> wrote:
>
>> Union is just combining data from multiple sources into a single
>> dataset.
>> That’s it. No memory, no disk involved.
>>
>> In you case you have
>>
>> input1.union(input2).groupBy(1).reduce(…)
>>
>> This will translate into:
>>
>> input1 -> repartition ->
>> read-both-inputs -> sort ->
>> reduce
>> input2 -> repartition ->
>>
>> So, in your case not even additional network transfer is involved,
>> because both data sets would need to be partitioned for the reduce anyway.
>>
>> Note, union in Flink has SQL union-all semantics, i.e., there is
>> not removal of duplicates.
>>
>> Cheers, Fabian
>>
>> *From:* Flavio Pompermaier <pomperma...@okkam.it>
>> *Sent:* Monday, 22. December, 2014 14:32
>> *To:* u...@flink.incubator.apache.org
>>
>> Ok thanks Fabian. I'd like just to know the internals of the union of
>> multiple datasets (partitioning, distribution among server, memory/disk,
>> etc..). Do you have any ref to this?
>>
>> Thanks in advance,
>> Flavio
>>
>> On Mon, Dec 22, 2014 at 12:46 PM, Fabian Hueske <fhue...@apache.org>
>> wrote:
>>
>>> Follow the first approach.
>>> Joins are expensive, union comes for free.
>>>
>>> Best, Fabian
>>>
>>> 2014-12-22 11:47 GMT+01:00 Flavio Pompermaier <pomperma...@okkam.it>:
>>>
>>>> Hi guys,
>>>>
>>>> In my use case I have multiple Datasets with the same structure (e.g.
>>>> Tuple3) and I want to produce an output Dataset containing all Tuple3
>>>> grouped by the first field (0).
>>>> I can obtain the same results performing a union of all datasets and
>>>> then a group by (simplest implementation) or join all of them pairwise
>>>> (((A->B)->C)->D)..) or I don't know if there is any other solution. When
>>>> should I use the first or the second approach? Could you help me in
>>>> figuring out the internals of the two approaches? I always have some fear
>>>> when using multiple joins when I don't know exactly their size..
>>>>
>>>> Best,
>>>> Flavio
>>>>
>>>
>>>
>>
