Hi Fabian,
I implemented your approach from above. However, the runtime decides to run
two subtasks on the same node, resulting in an out of memory error.
I thought partitioning really does partition the data to nodes, but now it
seems like its partitioning to tasks, and tasks can be one the same
machine, resulting in no partitioning at all.
Is that correct?
This happens, even if I run with parallelism 2. Is there some way to
control this? Or to run node-local initialization?
Cheers
Stefan
On 21 September 2015 at 21:15, Stefan Bunk <stefan.b...@googlemail.com>
wrote:
> Of course!
>
> On 21 September 2015 at 19:10, Fabian Hueske <fhue...@gmail.com> wrote:
>
>> The custom partitioner does not know its task id but the mapper that
>> assigns the partition ids knows its subtaskid.
>>
>> So if the mapper with subtask id 2 assigns partition ids 2 and 7, only 7
>> will be send over the network.
>> On Sep 21, 2015 6:56 PM, "Stefan Bunk" <stefan.b...@googlemail.com>
>> wrote:
>>
>>> Hi Fabian,
>>>
>>> that sounds good, thank you.
>>>
>>> One final question: As I said earlier, this also distributes data in
>>> some unnecessary cases, say ID 4 sends data to ID 3.
>>> Is there no way to find out the ID of the current node? I guess that
>>> number is already available on the node and just needs to be exposed
>>> somehow, right?
>>>
>>> Cheers
>>> Stefan
>>>
>>>
>>>
>>> On 17 September 2015 at 18:39, Fabian Hueske <fhue...@gmail.com> wrote:
>>>
>>>> Hi Stefan,
>>>>
>>>> I think I have a solution for your problem :-)
>>>>
>>>> 1) Distribute both parts of the small data to each machine (you have
>>>> done that)
>>>> 2) Your mapper should have a parallelism of 10, the tasks with ID 0 to
>>>> 4 (get ID via RichFunction.getRuntimeContext().getIndexOfThisSubtask())
>>>> read the first half, tasks 5 to 9 read the second half.
>>>> 3) Give the large input into a FlatMapper which sends out two records
>>>> for each incoming record and assigns the first outgoing record a task ID in
>>>> range 0 to 4 and the second outgoing record an ID in range 5 to 9.
>>>> 4) Have a custom partitioner (DataSet.partitionCustom()) after the
>>>> duplicating mapper, which partitions the records based on the assigned task
>>>> Id before they go into the mapper with the other smaller data set. A record
>>>> with assigned task ID 0 will be sent to the mapper task with subtask index
>>>> 0.
>>>>
>>>> This setup is not very nice, but should work.
>>>>
>>>> Let me know, if you need more detail.
>>>>
>>>> Cheers, Fabian
>>>>
>>>> 2015-09-16 21:44 GMT+02:00 Stefan Bunk <stefan.b...@googlemail.com>:
>>>>
>>>>> Hi Fabian,
>>>>>
>>>>> the local file problem would however not exist, if I just copy both
>>>>> halves to all nodes, right?
>>>>>
>>>>> Lets say I have a file `1st` and a file `2nd`, which I copy to all
>>>>> nodes.
>>>>> Now with your approach from above, I do:
>>>>>
>>>>> // helper broadcast datasets to know on which half to operate
>>>>> val data1stHalf = env.fromCollection("1st")
>>>>> val data2ndHalf = env.fromCollection("2nd")
>>>>>
>>>>> val mapped1 = data.flatMap(yourMap).withBroadcastSet(data1stHalf,
>>>>> "fileName").setParallelism(5)
>>>>> val mapped2 = data.flatMap(yourMap).withBroadcastSet(data2ndHalf,
>>>>> "fileName").setParallelism(5)
>>>>> DataSet result = mapped1.union(mapped2)
>>>>>
>>>>> Then, in my custom operator implementation of flatMap I check the
>>>>> helper broadcast data to know which file to load:
>>>>> override def open(params: Configuration): Unit = {
>>>>> val fileName =
>>>>> getRuntimeContext.getBroadcastVariable[String]("fileName")(0)
>>>>> // read the file from the local filesystem which I copied there earlier
>>>>> this.data = loadFromFileIntoDatastructure("/home/data/" + fileName)
>>>>> }
>>>>> override def flatMap(document: Input, out: Collector[Output]): Unit = {
>>>>> // do sth. with this.data and the input
>>>>> out.collect(this.data.process(input))
>>>>> }
>>>>>
>>>>> I think this should work, or do you see another problem here?
>>>>>
>>>>> Which brings us to the other question:
>>>>> The both halves are so large, that one half of the data fits in the
>>>>> user-remaining memory on a node, but not both halves. So my program would
>>>>> probably memory-crash, if the scheduling trusts one node so much, that it
>>>>> wants to execute two flatMaps there ;-).
>>>>>
>>>>> You are saying, that it is not guaranteed, that all 10 nodes are used,
>>>>> but how likely is it, that one node is given two flatMaps and another one
>>>>> is basically idling? I have no idea of the internals, but I guess there is
>>>>> some heuristic inside which decides how to distribute.In the normal setup
>>>>> that all 10 nodes are up, connection is good, all nodes have the same
>>>>> resources available, input data is evenly distributed in HDFS, then the
>>>>> default case should be to distribute to all 10 nodes, right?
>>>>>
>>>>> I am not running in production, so for me it would be ok, if this
>>>>> works out usually.
>>>>>
>>>>> Cheers
>>>>> Stefan
>>>>>
>>>>>
>>>>> On 15 September 2015 at 23:40, Fabian Hueske <fhue...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Stefan,
>>>>>>
>>>>>> the problem is that you cannot directly influence the scheduling of
>>>>>> tasks to nodes to ensure that you can read the data that you put in the
>>>>>> local filesystems of your nodes. HDFS gives a shared file system which
>>>>>> means that each node can read data from anywhere in the cluster.
>>>>>> I assumed the data is small enough to broadcast because you want to
>>>>>> keep it in memory.
>>>>>>
>>>>>> Regarding your question. It is not guaranteed that two different
>>>>>> tasks, each with parallelism 5, will be distributed to all 10 nodes (even
>>>>>> if you have only 10 processing slots).
>>>>>> What would work is to have one map task with parallelism 10 and a
>>>>>> Flink setup with 10 task managers on 10 machines with only one processing
>>>>>> slot per TM. However, you won't be able to replicate the data to both
>>>>>> sets
>>>>>> of maps because you cannot know which task instance will be executed on
>>>>>> which machine (you cannot distinguish the tasks of both task sets).
>>>>>>
>>>>>> As I said, reading from local file system in a cluster and forcing
>>>>>> task scheduling to specific nodes is quite tricky.
>>>>>> Cheers, Fabian
>>>>>>
>>>>>> 2015-09-15 23:15 GMT+02:00 Stefan Bunk <stefan.b...@googlemail.com>:
>>>>>>
>>>>>>> Hi Fabian,
>>>>>>>
>>>>>>> I think we might have a misunderstanding here. I have already copied
>>>>>>> the first file to five nodes, and the second file to five other nodes,
>>>>>>> outside of Flink. In the open() method of the operator, I just read that
>>>>>>> file via normal Java means. I do not see, why this is tricky or how HDFS
>>>>>>> should help here.
>>>>>>> Then, I have a normal Flink DataSet, which I want to run through the
>>>>>>> operator (using the previously read data in the flatMap
>>>>>>> implementation). As
>>>>>>> I run the program several times, I do not want to broadcast the data
>>>>>>> every
>>>>>>> time, but rather just copy it on the nodes, and be fine with it.
>>>>>>>
>>>>>>> Can you answer my question from above? If the setParallelism-method
>>>>>>> works and selects five nodes for the first flatMap and five _other_
>>>>>>> nodes
>>>>>>> for the second flatMap, then that would be fine for me if there is no
>>>>>>> other
>>>>>>> easy solution.
>>>>>>>
>>>>>>> Thanks for your help!
>>>>>>> Best
>>>>>>> Stefan
>>>>>>>
>>>>>>>
>>>>>>> On 14 September 2015 at 22:28, Fabian Hueske <fhue...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi Stefan,
>>>>>>>>
>>>>>>>> forcing the scheduling of tasks to certain nodes and reading files
>>>>>>>> from the local file system in a multi-node setup is actually quite
>>>>>>>> tricky
>>>>>>>> and requires a bit understanding of the internals.
>>>>>>>> It is possible and I can help you with that, but would recommend to
>>>>>>>> use a shared filesystem such as HDFS if that is possible.
>>>>>>>>
>>>>>>>> Best, Fabian
>>>>>>>>
>>>>>>>> 2015-09-14 19:16 GMT+02:00 Stefan Bunk <stefan.b...@googlemail.com>
>>>>>>>> :
>>>>>>>>
>>>>>>>>> Hi,
>>>>>>>>>
>>>>>>>>> actually, I am distributing my data before the program starts,
>>>>>>>>> without using broadcast sets.
>>>>>>>>>
>>>>>>>>> However, the approach should still work, under one condition:
>>>>>>>>>
>>>>>>>>>> DataSet mapped1 =
>>>>>>>>>> data.flatMap(yourMap).withBroadcastSet(smallData1,"data").setParallelism(5);
>>>>>>>>>> DataSet mapped2 =
>>>>>>>>>> data.flatMap(yourMap).withBroadcastSet(smallData2,"data").setParallelism(5);
>>>>>>>>>>
>>>>>>>>> Is it guaranteed, that this selects a disjoint set of nodes, i.e.
>>>>>>>>> five nodes for mapped1 and five other nodes for mapped2?
>>>>>>>>>
>>>>>>>>> Is there any way of selecting the five nodes concretely?
>>>>>>>>> Currently, I have stored the first half of the data on nodes 1-5 and
>>>>>>>>> the
>>>>>>>>> second half on nodes 6-10. With this approach, I guess, nodes are
>>>>>>>>> selected
>>>>>>>>> randomly so I would have to copy both halves to all of the nodes.
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Stefan
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>
