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From DB Tsai <dbt...@dbtsai.com>
Subject Re: TreeReduce Functionality in Spark
Date Thu, 04 Jun 2015 22:47:23 GMT
For the first round, you will have 16 reducers working since you have
32 partitions. Two of 32 partitions will know which reducer they will
go by sharing the same key using reduceByKey.

After this step is done, you will have 16 partitions, so the next
round will be 8 reducers.

Sincerely,

DB Tsai
-------------------------------------------------------
Blog: https://www.dbtsai.com


On Thu, Jun 4, 2015 at 12:06 PM, Raghav Shankar <raghav0110.cs@gmail.com> wrote:
> Hey DB,
>
> Thanks for the reply!
>
> I still don't think this answers my question. For example, if I have a top()
> action being executed and I have 32 workers(32 partitions), and I choose a
> depth of 4, what does the overlay of intermediate reducers look like? How
> many reducers are there excluding the master and the worker? How many
> partitions get sent to each of these intermediate reducers? Does this number
> vary at each level?
>
> Thanks!
>
>
> On Thursday, June 4, 2015, DB Tsai <dbtsai@dbtsai.com> wrote:
>>
>> By default, the depth of the tree is 2. Each partition will be one node.
>>
>> Sincerely,
>>
>> DB Tsai
>> -------------------------------------------------------
>> Blog: https://www.dbtsai.com
>>
>>
>> On Thu, Jun 4, 2015 at 10:46 AM, Raghav Shankar <raghav0110.cs@gmail.com>
>> wrote:
>> > Hey Reza,
>> >
>> > Thanks for your response!
>> >
>> > Your response clarifies some of my initial thoughts. However, what I
>> > don't
>> > understand is how the depth of the tree is used to identify how many
>> > intermediate reducers there will be, and how many partitions are sent to
>> > the
>> > intermediate reducers. Could you provide some insight into this?
>> >
>> > Thanks,
>> > Raghav
>> >
>> > On Thursday, June 4, 2015, Reza Zadeh <reza@databricks.com> wrote:
>> >>
>> >> In a regular reduce, all partitions have to send their reduced value to
>> >> a
>> >> single machine, and that machine can become a bottleneck.
>> >>
>> >> In a treeReduce, the partitions talk to each other in a logarithmic
>> >> number
>> >> of rounds. Imagine a binary tree that has all the partitions at its
>> >> leaves
>> >> and the root will contain the final reduced value. This way there is no
>> >> single bottleneck machine.
>> >>
>> >> It remains to decide the number of children each node should have and
>> >> how
>> >> deep the tree should be, which is some of the logic in the method you
>> >> pasted.
>> >>
>> >> On Wed, Jun 3, 2015 at 7:10 PM, raggy <raghav0110.cs@gmail.com> wrote:
>> >>>
>> >>> I am trying to understand what the treeReduce function for an RDD
>> >>> does,
>> >>> and
>> >>> how it is different from the normal reduce function. My current
>> >>> understanding is that treeReduce tries to split up the reduce into
>> >>> multiple
>> >>> steps. We do a partial reduce on different nodes, and then a final
>> >>> reduce
>> >>> is
>> >>> done to get the final result. Is this correct? If so, I guess what I
>> >>> am
>> >>> curious about is, how does spark decide how many nodes will be on each
>> >>> level, and how many partitions will be sent to a given node?
>> >>>
>> >>> The bulk of the implementation is within this function:
>> >>>
>> >>>     partiallyReduced.treeAggregate(Option.empty[T])(op, op, depth)
>> >>>       .getOrElse(throw new UnsupportedOperationException("empty
>> >>> collection"))
>> >>>
>> >>> The above function is expanded to
>> >>>
>> >>> val cleanSeqOp = context.clean(seqOp)
>> >>>       val cleanCombOp = context.clean(combOp)
>> >>>       val aggregatePartition =
>> >>>         (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp,
>> >>> cleanCombOp)
>> >>>       var partiallyAggregated = mapPartitions(it =>
>> >>> Iterator(aggregatePartition(it)))
>> >>>       var numPartitions = partiallyAggregated.partitions.length
>> >>>       val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 /
>> >>> depth)).toInt, 2)
>> >>>       // If creating an extra level doesn't help reduce
>> >>>       // the wall-clock time, we stop tree aggregation.
>> >>>       while (numPartitions > scale + numPartitions / scale) {
>> >>>         numPartitions /= scale
>> >>>         val curNumPartitions = numPartitions
>> >>>         partiallyAggregated =
>> >>> partiallyAggregated.mapPartitionsWithIndex
>> >>> {
>> >>>           (i, iter) => iter.map((i % curNumPartitions, _))
>> >>>         }.reduceByKey(new HashPartitioner(curNumPartitions),
>> >>> cleanCombOp).values
>> >>>       }
>> >>>       partiallyAggregated.reduce(cleanCombOp)
>> >>>
>> >>> I am completely lost about what is happening in this function. I would
>> >>> greatly appreciate some sort of explanation.
>> >>>
>> >>>
>> >>>
>> >>>
>> >>> --
>> >>> View this message in context:
>> >>>
>> >>> http://apache-spark-user-list.1001560.n3.nabble.com/TreeReduce-Functionality-in-Spark-tp23147.html
>> >>> Sent from the Apache Spark User List mailing list archive at
>> >>> Nabble.com.
>> >>>
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>> >>>
>> >>
>> >

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