Hi Fridtjof,
I might miss something, but can’t you assign the ids once before
starting the iteration and then reuse them throughout the iterations?
Of course you would have to add another field to your input data but
then you don’t have to run the |zipWithIndex| for every iteration.
Cheers,
Till
On Mon, Feb 1, 2016 at 11:37 AM, Fridtjof Sander
<fsan...@mailbox.tu-berlin.de <mailto:fsan...@mailbox.tu-berlin.de>>
wrote:
(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
