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From Ovidiu-Cristian MARCU <ovidiu-cristian.ma...@inria.fr>
Subject Re: KeyGroupRangeAssignment ?
Date Tue, 21 Feb 2017 11:04:44 GMT
Hi,

As in my example, each key is a window so I want to evenly distributed processing to all slots.
If I have 100 keys and 100 slots, for each key I have the same rate of events, I don’t want
skewed distribution.

Best,
Ovidiu

> On 21 Feb 2017, at 11:38, Aljoscha Krettek <aljoscha@apache.org> wrote:
> 
> Hi Ovidiu,
> what's the reason for wanting to make the parallelism equal to the number
> of keys? I think in general it's very hard to ensure that hashes even go to
> different key groups. It can always happen that all your keys (if you have
> so few of them) are assigned to the same parallel operator instance.
> 
> Cheers,
> Aljoscha
> 
> On Tue, 21 Feb 2017 at 10:53 Till Rohrmann <trohrmann@apache.org> wrote:
> 
>> Hi Ovidiu,
>> 
>> at the moment it is not possible to plugin a user defined hash function/key
>> group assignment function. If you like, then you can file a JIRA issue to
>> add this functionality.
>> 
>> The key group assignment in your example looks quite skewed. One question
>> concerning how you calculated it: Shouldn't the number of element in each
>> group sum up to 1024? this only works for the first case. What do the
>> numbers mean then?
>> 
>> Cheers,
>> Till
>> 
>> On Mon, Feb 20, 2017 at 3:45 PM, Ovidiu-Cristian MARCU <
>> ovidiu-cristian.marcu@inria.fr> wrote:
>> 
>>> Hi,
>>> 
>>> Thank you for clarifications (I am working with KeyedStream so a custom
>>> partitioner does not help).
>>> 
>>> So I should set maxParallelism>=parallelism and change my keys (from
>>> input.keyBy(0)) such that key group assignment works as expected),
>>> but I can’t modify these keys in order to make it work.
>>> 
>>> The other option is to change Flink’s internals in order to evenly
>>> distribute keys (changing computeKeyGroupForKeyHash: is this enough?).
>>> What I was looking for was an api to change the way key group assignment
>>> is done, but without changing Flink’s runtime.
>>> 
>>> I think that the maxParallelism setting is not enough (it introduces this
>>> inefficient way of distributing data for processing when using
>> KeyedStream).
>>> Is it possible to expose somehow the key group assignment?
>>> 
>>> This is how keys are distributed (1024 keys, key=1..1024; and groups from
>>> 2 to 16 - equiv. parallelism that is number of slots):
>>> 
>>> {0=517, 1=507} 2
>>> {0=881, 1=809, 2=358} 3
>>> {0=1139, 1=1048, 2=617, 3=268} 4
>>> {0=1319, 1=1268, 2=829, 3=473, 4=207} 5
>>> {0=1512, 1=1425, 2=1008, 3=644, 4=352, 5=179} 6
>>> {0=1656, 1=1586, 2=1160, 3=781, 4=512, 5=310, 6=139} 7
>>> {0=1781, 1=1718, 2=1280, 3=908, 4=645, 5=417, 6=278, 7=141} 8
>>> {0=1901, 1=1828, 2=1395, 3=1031, 4=738, 5=529, 6=399, 7=240, 8=131} 9
>>> {0=1996, 1=1934, 2=1493, 3=1134, 4=846, 5=614, 6=513, 7=354, 8=233, 9=99}
>>> 10
>>> {0=2094, 1=2017, 2=1577, 3=1226, 4=935, 5=713, 6=610, 7=434, 8=359,
>> 9=174,
>>> 10=101} 11
>>> {0=2192, 1=2091, 2=1669, 3=1316, 4=1008, 5=797, 6=705, 7=517, 8=446,
>>> 9=255, 10=173, 11=95} 12
>>> {0=2257, 1=2175, 2=1741, 3=1396, 4=1079, 5=882, 6=785, 7=596, 8=524,
>>> 9=340, 10=254, 11=186, 12=73} 13
>>> {0=2330, 1=2258, 2=1816, 3=1458, 4=1160, 5=951, 6=858, 7=667, 8=602,
>>> 9=417, 10=329, 11=265, 12=135, 13=66} 14
>>> {0=2397, 1=2323, 2=1897, 3=1542, 4=1233, 5=1008, 6=934, 7=723, 8=671,
>>> 9=479, 10=385, 11=344, 12=210, 13=118, 14=72} 15
>>> {0=2454, 1=2395, 2=1949, 3=1603, 4=1296, 5=1055, 6=998, 7=803, 8=739,
>>> 9=539, 10=453, 11=410, 12=280, 13=178, 14=147, 15=61} 16
>>> 
>>> Best,
>>> Ovidiu
>>> 
>>>> On 20 Feb 2017, at 12:04, Till Rohrmann <trohrmann@apache.org> wrote:
>>>> 
>>>> Hi Ovidiu,
>>>> 
>>>> the way Flink works is to assign key group ranges to operators. For
>> each
>>> element you calculate a hash value and based on that you assign it to a
>> key
>>> group. Thus, in your example, you have either a key group with more than
>> 1
>>> key or multiple key groups with 1 or more keys assigned to an operator.
>>>> 
>>>> So what you could try to do is to reduce the number of key groups to
>>> your parallelism via env.setMaxParallelism() and then try to figure a key
>>> out whose hashes are uniformly distributed over the key groups. The key
>>> group assignment is calculated via murmurHash(key.hashCode()) %
>>> maxParallelism.
>>>> 
>>>> Alternatively if you don’t need a keyed stream, you could try to use a
>>> custom partitioner via DataStream.partitionCustom.
>>>> 
>>>> Cheers,
>>>> Till
>>>> 
>>>> 
>>>> On Mon, Feb 20, 2017 at 11:46 AM, Ovidiu-Cristian MARCU <
>>> ovidiu-cristian.marcu@inria.fr <mailto:ovidiu-cristian.marcu@inria.fr>>
>>> wrote:
>>>> Hi,
>>>> 
>>>> Can you please comment on how can I ensure stream input records are
>>> distributed evenly onto task slots?
>>>> See attached screen Records received issue.
>>>> 
>>>> I have a simple application which is applying some window function over
>>> a stream partitioned as follows:
>>>> (parallelism is equal to the number of keys; records with the same key
>>> are streamed evenly)
>>>> 
>>>> // get the execution environment
>>>> final StreamExecutionEnvironment env = StreamExecutionEnvironment.
>>> getExecutionEnvironment();
>>>> // get input data by connecting to the socket
>>>> DataStream<String> text = env.socketTextStream("localhost", port,
>> "\n");
>>>> DataStream<Tuple8<String, String, String, Integer, String, Double,
>> Long,
>>> Long>> input = text.flatMap(...);
>>>> DataStream<Double> counts1 = null;
>>>> counts1 = input.keyBy(0).countWindow(windowSize, slideSize)
>>>>              .apply(new WindowFunction<Tuple8<String, String, String,
>>> Integer, String, Double, Long, Long>, Double, Tuple, GlobalWindow>() {
>>>>              ...
>>>>              });
>>>> counts1.writeAsText(params.get("output1"));
>>>> env.execute("Socket Window WordCount”);
>>>> 
>>>> Best,
>>>> Ovidiu
>>>> 
>>>> 
>>> 
>>> 
>> 


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