join with no element appearing in multiple join-pairs

[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

[Fridtjof Sander]

(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

[Till Rohrmann]

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 <[hidden email]> 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

[Fridtjof Sander]

Hi Till,

thanks for your reply!

The problem with that is, that I sometimes combine two elements:

So from x0 -> x1 -> x2 I join (x0, x1) which might become x0 -> x2 in the end.

The indices from zipWithIndex then are 0 and 2, resulting in equal joins flags. Sequential elements always have to have alternating flags, which gets violated here.

Best
Fridtjof

Am 01.02.16 um 12:26 schrieb Till Rohrmann:

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 <[hidden email]> 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

[Till Rohrmann]

In the described case, can it be that you still have elements with `id % 2 == 1` in your data set or are they filtered out? If they are filtered out, then you can simply shift the indices for each iteration to the right. 

On Mon, Feb 1, 2016 at 12:32 PM, Fridtjof Sander <[hidden email]> wrote:

Hi Till,

thanks for your reply!

The problem with that is, that I sometimes combine two elements:

So from x0 -> x1 -> x2 I join (x0, x1) which might become x0 -> x2 in the end.

The indices from zipWithIndex then are 0 and 2, resulting in equal joins flags. Sequential elements always have to have alternating flags, which gets violated here.

Best
Fridtjof

Am 01.02.16 um 12:26 schrieb Till Rohrmann:

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 <[hidden email]> 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

[Fridtjof Sander]

I still have them.

To be clear: I do need to compare each element with its successor, I just can't have one element to be paired multiple times in the same step. That's why I divide the join into two steps:

data: x0 -> x1 -> x2

The first join will only pair (x0, x1). These may or may not be combined to one element. However, x2 will be in the result-set (of the first join) anyway: Elements will only be removed if they are paired with others and combined with them.

If x0 and x1 are combined to x12, then the second join will pair (x12, x2). If not, it will pair (x1, x2) and x0 will be in the result-set (of the second join).

This two-step join happens inside of an iteration.

If I wouldn't have this two-step join, I would pair (x0, x1) and (x1, x2). In the event that both are combined, x1 is used twice to build a new element, and that must not happen.

Am 1. Februar 2016 13:00:05 MEZ, schrieb Till Rohrmann [hidden email]:

In the described case, can it be that you still have elements with `id % 2 == 1` in your data set or are they filtered out? If they are filtered out, then you can simply shift the indices for each iteration to the right. 

On Mon, Feb 1, 2016 at 12:32 PM, Fridtjof Sander <[hidden email]> wrote:

Hi Till,

thanks for your reply!

The problem with that is, that I sometimes combine two elements:

So from x0 -> x1 -> x2 I join (x0, x1) which might become x0 -> x2 in the end.

The indices from zipWithIndex then are 0 and 2, resulting in equal joins flags. Sequential elements always have to have alternating flags, which gets violated here.

Best
Fridtjof

Am 01.02.16 um 12:26 schrieb Till Rohrmann:

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 <[hidden email]> 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