(tried to reformat)
Hi,
I have a problem which seems to be unsolvable in Flink at the moment
(1.0-Snapshot, current master branch)
and I would kindly ask for some input, ideas on alternative approaches
or just a confirmatory "yup, that doesn't work".
### Here's the situation:
I have a dataset and its elements are totally ascending sorted by some
key (Int). Each element has a "next-pointer" to its successor, which is
just another field with the key of the following element:
x0 -> x1 -> x2 -> x3 -> ... -> xn
The keys are not necessarily increasing by 1, so it may be that: x0 has
key 2 and x1 has key 10, x2 has 11, x3 has 25 and so on. I need to
process that set in the following way:
iterate:
find all pairs of elements where "next == key" BUT make sure no element
appears in multiple pairs
example: do pair (x0, x1), (x2, x3), (x4, x5), ... but don't pair (x1,
x2), (x3, x4), ...
then, if some condition is met, combine a pair
run above procedure again with switched pairing-condition:
example: do pair (x1, x2), (x3, x4), (x5, x6), ... do not pair (x0, x1),
(x2, x3), ..
I hope the problem is clear...
### Now my approach: pseudo-scala-code:
val indexed = input.zipWithIndex
val flagged = indexed.map((i, el) => el.setFlag(i % 2 == 0))
val left = flagged.filter(el => el.flag)
val right = flagged.filter(el => !el.flag)
left.fullOuterJoin(right)
.where(el.next)
.equalTo(el.key)
...
I attach my elements with a temporary key, that is increasing by 1, with
zipWithIndex. Then, I map that tempKey to a boolean joinFlag: true if
key is even, false if key is odd. Then I filter all elements with true,
and put them in a dataset that is the left side of the next == key join.
The right side are all elements with flag == false In the second run, I
switch the flag construction to el.setFlag(i % 2 != 0).
That actually works, there is only one problem:
### The problem:
In my approach, I must not loose the total ordering of the data, because
only if that ordering is preserved, the assignment of alternating
join-flags works. Initially it is done by range-partitioning and
partition-sorting. However, that ordering is destroyed, when data is
shuffled for the join. And I can not restore it, because I have to run
the whole thing in an iteration, and range-partitioning is not supported
within iterations.
### Help?
It sounds all very complicated, but the only thing I really have to
solve is that join without any element appearing in multiple pairs (as
described in "the situation"). If anyone has any idea how to solve this,
that person would make my day so hard...
Anyways, thanks for your time!
Best, Fridtjof
Am 01.02.16 um 11:32 schrieb Fridtjof Sander:
Hi,
I have a problem which seems to be unsolvable in Flink at the moment
(1.0-Snapshot, current master branch)
and I would kindly ask for some input, ideas on alternative approaches
or just a confirmatory "yup, that doesn't work".
### Here's the situation:
I have a dataset and its elements are totally ascending sorted by some
key (Int). Each element has a "next-pointer" to its successor, which
is just another field with the key of the following element: x0 -> x1
-> x2 -> x3 -> ... -> xn The keys are not necessarily increasing by 1,
so it may be that: x0 has key 2 and x1 has key 10, x2 has 11, x3 has
25 and so on. I need to process that set in the following way:
iterate: find all pairs of elements where "next == key" BUT make sure
no element appears in multiple pairs example: do pair (x0, x1), (x2,
x3), (x4, x5), ... but don't pair (x1, x2), (x3, x4), ... then, if
some condition is met, combine a pair run above procedure again with
switched pairing-condition: example: do pair (x1, x2), (x3, x4), (x5,
x6), ... do not pair (x0, x1), (x2, x3), .. I hope the problem is
clear... ### Now my approach: pseudo-scala-code:
val indexed = input.zipWithIndex val flagged = indexed.map((i, el) =>
el.setFlag(i % 2 == 0)) val left = flagged.filter(el => el.flag)
val right = flagged.filter(el => !el.flag) left.fullOuterJoin(right)
.where(el.next) .equalTo(el.key) ... I attach my elements with a
temporary key, that is increasing by 1, with zipWithIndex. Then, I map
that tempKey to a boolean joinFlag: true if key is even, false if key
is odd. Then I filter all elements with true, and put them in a
dataset that is the left side of the next == key join. The right side
are all elements with flag == false In the second run, I switch the
flag construction to el.setFlag(i % 2 != 0). That actually works,
there is only one problem: ### The problem: In my approach, I must not
loose the total ordering of the data, because only if that ordering is
preserved, the assignment of alternating join-flags works. Initially
it is done by range-partitioning and partition-sorting. However, that
ordering is destroyed, when data is shuffled for the join. And I can
not restore it, because I have to run the whole thing in an iteration,
and range-partitioning is not supported within iterations. ### Help?
It sounds all very complicated, but the only thing I really have to
solve is that join without any element appearing in multiple pairs (as
described in "the situation"). If anyone has any idea how to solve
this, that person would make my day so hard... Anyways, thanks for
your time! Best, Fridtjof
