Re: [DISCUSS] KIP-253: Support in-order message delivery with partition expansion

[Jan Filipiak]

Hi Jun,

thanks for your great reply, even though I only managed to throw a few 
sentences in, because of time last time. I think you are starting to 
understand me pretty well. There is still some minor things that need to 
be flattened out. I already had these points previously but I try to 
rephrase them into answers to your questions for clarity.

On 23.03.2018 01:43, Jun Rao wrote:
Hi, Jan,

Thanks for the reply.

Ok. So your idea is to have a special consumer reshuffle the data to a new
set of partitions. Each consumer group will not increase its tasks
immediately. We just need to make sure that each consumer instance is
assigned the set of new partitions covering the same set of keys. At some
point later, each consumer group can decide independently when to increase
the number of tasks. At that point, the consumer will need to rebuild the
local state for the new tasks. Is that right?
This has multiple aspects. The middle part

 We just need to make sure that each consumer instance is
assigned the set of new partitions covering the same set of keys

is probably the most important one here. This is essentially what the 
second part of my idea is about.
We need a protocol that allows consumers to switch from old topic (old 
partiton count) to the new topic (new partion count). (If it really is a 
new topic or not depends on the implementation -I like to model it as 
such as its easier in my head). The protocol I envision is something 
like this:
Per partition there is information from the old offset to the new offset.

topicOldMapping-0 : 50 => [0:25,2:25]
topicOldMapping-1 : 60 => [1:15,3:45]

A consumer can get a hold on this information and stop at 50 and 60 and 
continue at 25,25,15,45

The same transition works when the data is not copied. a mapping would 
look like

topicOldMapping-0 : 50 => [0:0,2:0]
topicOldMapping-1 : 60 => [1:0,3:0]

No data would have been copied and 50 and 60 are the endoffsets and we 
would start at the very beginning of the new topic 0,0,0,0


I agree that decoupling the number of tasks in a consumer group from the
number of partitions in the input topic is a good idea. This allows each
consumer group to change its degree of parallelism independently. There are
different ways to achieve such decoupling. You suggested one approach,
which works when splitting partitions. Not sure if this works in general,
for example when merging partitions.
Merging is the same.

topicOldMapping-0 : 25 => [0:50]
topicOldMapping-1 : 25 => [1:50]
topicOldMapping-2 : 15 => [0:50]
topicOldMapping-3 : 45 => [1:50]

generating this mapping is trivial again if we do not copy data. (end 
offsets point to start offsets)
If we do copy data the consumer will create the mapping.


Once a consumer group decides to change its degree of parallelism, it needs
to rebuild the local state for the new tasks. We can either rebuild the
states from the input topic or from the local state (through change capture
topic). I think we agreed that rebuilding from the local state is more
efficient. It also seems that it's better to let the KStreams library do
the state rebuilding, instead of each application. Would you agree?
If you want todo by RPC, you are changing the running application 
without having a new good one. This
is against the kappa achitecture I would not recommend that.

If you replay the changelog and only poll records that are for your 
partition. You have the problem of knowing
which offset from the input topic your current state relates to.

If you rebuild you could leave the old running and just wait for the new 
to be _good_ then change who's output you
show to the customers.


About the special consumer. I guess that's only needed if one wants to
recopy the existing data? However, if we can truly decouple the tasks in
the consumer group from the partitions in the input topic, it seems there
is no need for copying existing data? It's also not clear to me who will
start/stop this special consumer.
Who starts and stops it is also not very clear to me. I do not have 
strong opinions.
The thing is that I am looking for an explanation how you can have a 
logcompacted topic working without copying.
I agree that for running consumers its no problem as they are already 
past the history. But the whole purpose of Log compaction
is to be able to bootstrap new consumers. They are completely lost with 
a topic expanded without repartitioning.
The topic will be broken forever. and this is not acceptable.

This is why I am so intrigued to model the problem as described because 
it has no overhead for the no copy path while it allows
to also perform a copy.

State handling wise one has also all the options. Exactly the 3 you 
mentioned I guess. its just that this statestore RPC is a bad idea
and it was only invented to allow for optimisations that are better not 
done.Not to say they are premature ;)

I hope it makes it clear

best jan

Thanks,

Jun

On Wed, Mar 21, 2018 at 6:57 AM, Jan Filipiak <jan.filip...@trivago.com>
wrote:

Hi Jun,

I was really seeing progress in our conversation but your latest reply is
just devastating.
I though we were getting close being on the same page now it feels like we
are in different libraries.

I just quickly slam my answers in here. If they are to brief I am sorry
give me a ping and try to go into details more.
Just want to show that your pro/cons listing is broken.

Best Jan

and want to get rid of this horrible compromise


On 19.03.2018 05:48, Jun Rao wrote:

Hi, Jan,

Thanks for the discussion. Great points.

Let me try to summarize the approach that you are proposing. On the broker
side, we reshuffle the existing data in a topic from current partitions to
the new partitions. Once the reshuffle fully catches up, switch the
consumers to start consuming from the new partitions. If a consumer needs
to rebuild its local state (due to partition changes), let the consumer
rebuild its state by reading all existing data from the new partitions.
Once all consumers have switches over, cut over the producer to the new
partitions.

The pros for this approach are that :
1. There is just one way to rebuild the local state, which is simpler.

true thanks

The cons for this approach are:
1. Need to copy existing data.

Very unfair and not correct. It does not require you to copy over existing
data. It _allows_ you to copy all existing data.

2. The cutover of the producer is a bit complicated since it needs to
coordinate with all consumer groups.

Also not true. I explicitly tried to make clear that there is only one
special consumer (in the case of actually copying data) coordination is
required.

3. The rebuilding of the state in the consumer is from the input topic,
which can be more expensive than rebuilding from the existing state.

true, but rebuilding state is only required if you want to increase
processing power, so we assume this is at hand.

4. The broker potentially has to know the partitioning function. If this
needs to be customized at the topic level, it can be a bit messy.

I would argue against having the operation being performed by the broker.
This was not discussed yet but if you see my original email i suggested
otherwise from the beginning.

Here is an alternative approach by applying your idea not in the broker,
but in the consumer. When new partitions are added, we don't move existing
data. In KStreams, we first reshuffle the new input data to a new topic T1
with the old number of partitions and feed T1's data to the rest of the
pipeline. In the meantime, KStreams reshuffles all existing data of the
change capture topic to another topic C1 with the new number of
partitions.
We can then build the state of the new tasks from C1. Once the new states
have been fully built, we can cut over the consumption to the input topic
and delete T1. This approach works with compacted topic too. If an
application reads from the beginning of a compacted topic, the consumer
will reshuffle the portion of the input when the number of partitions
doesn't match the number of tasks.

We all wipe this idea from our heads instantly. Mixing Ideas from an
argument is not a resolution strategy
just leads to horrible horrible software.


The pros of this approach are:
1. No need to copy existing data.
2. Each consumer group can cut over to the new partitions independently.
3. The state is rebuilt from the change capture topic, which is cheaper
than rebuilding from the input topic.
4. Only the KStreams job needs to know the partitioning function.

The cons of this approach are:
1. Potentially the same input topic needs to be reshuffled more than once
in different consumer groups during the transition phase.

What do you think?

Thanks,

Jun



On Thu, Mar 15, 2018 at 1:04 AM, Jan Filipiak <jan.filip...@trivago.com>
wrote:

Hi Jun,
thank you for following me on these thoughts. It was important to me to
feel that kind of understanding for my arguments.

What I was hoping for (I mentioned this earlier) is that we can model the
case where we do not want to copy the data the exact same way as the case
when we do copy the data. Maybe you can peek into the mails before to see
more details for this.

This means we have the same mechanism to transfer consumer groups to
switch topic. The offset mapping that would be generated would even be
simpler End Offset of the Old topic => offset 0 off all the partitions of
the new topic. Then we could model the transition of a non-copy expansion
the exact same way as a copy-expansion.

I know this only works when topic growth by a factor. But the benefits of
only growing by a factor are to strong anyways. See Clemens's hint and
remember that state reshuffling is entirely not needed if one doesn't
want
to grow processing power.

I think these benefits should be clear, and that there is basically no
downside to what is currently at hand but just makes everything easy.

One thing you need to know is. that if you do not offer rebuilding a log
compacted topic like i suggest that even if you have consumer state
reshuffling. The topic is broken and can not be used to bootstrap new
consumers. They don't know if they need to apply a key from and old
partition or not. This is a horrible downside I haven't seen a solution
for
in the email conversation.

I argue to:

Only grow topic by a factor always.
Have the "no copy consumer" transition as the trivial case of the "copy
consumer transition".
If processors needs to be scaled, let them rebuild from the new topic and
leave the old running in the mean time.
Do not implement key shuffling in streams.

I hope I can convince you especially with the fact how I want to handle
consumer transition. I think
you didn't quite understood me there before. I think the term "new topic"
intimidated you a little.
How we solve this on disc doesn't really matter, If the data goes into
the
same Dir or a different Dir or anything. I do think that it needs to
involve at least rolling a new segment for the existing partitions.
But most of the transitions should work without restarting consumers.
(newer consumers with support for this). But with new topic i just meant
the topic that now has a different partition count. Plenty of ways to
handle that (versions, aliases)

Hope I can further get my idea across.

Best Jan






On 14.03.2018 02:45, Jun Rao wrote:

Hi, Jan,
Thanks for sharing your view.

I agree with you that recopying the data potentially makes the state
management easier since the consumer can just rebuild its state from
scratch (i.e., no need for state reshuffling).

On the flip slide, I saw a few disadvantages of the approach that you
suggested. (1) Building the state from the input topic from scratch is
in
general less efficient than state reshuffling. Let's say one computes a
count per key from an input topic. The former requires reading all
existing
records in the input topic whereas the latter only requires reading data
proportional to the number of unique keys. (2) The switching of the
topic
needs modification to the application. If there are many applications
on a
topic, coordinating such an effort may not be easy. Also, it's not clear
how to enforce exactly-once semantic during the switch. (3) If a topic
doesn't need any state management, recopying the data seems wasteful. In
that case, in place partition expansion seems more desirable.

I understand your concern about adding complexity in KStreams. But,
perhaps
we could iterate on that a bit more to see if it can be simplified.

Jun


On Mon, Mar 12, 2018 at 11:21 PM, Jan Filipiak <
jan.filip...@trivago.com>
wrote:

Hi Jun,

I will focus on point 61 as I think its _the_ fundamental part that I
cant
get across at the moment.

Kafka is the platform to have state materialized multiple times from
one
input. I emphasize this: It is the building block in architectures that
allow you to
have your state maintained multiple times. You put a message in once,
and
you have it pop out as often as you like. I believe you understand
this.

Now! The path of thinking goes the following: I am using apache kafka
and
I _want_ my state multiple times. What am I going todo?

A) Am I going to take my state that I build up, plunge some sort of RPC
layer ontop of it, use that RPC layer to throw my records across
instances?
B) Am I just going to read the damn message twice?

Approach A is fundamentally flawed and a violation of all that is good
and
holy in kafka deployments. I can not understand how this Idea can come
in
the first place.
(I do understand: IQ in streams, they polluted the kafka streams
codebase
really bad already. It is not funny! I think they are equally flawed as
A)

I say, we do what Kafka is good at. We repartition the topic once. We
switch the consumers.
(Those that need more partitions are going to rebuild their state in
multiple partitions by reading the new topic, those that don't just
assign
the new partitions properly)
We switch producers. Done!

The best thing! It is trivial, hipster stream processor will have an
easy
time with that aswell. Its so super simple. And simple IS good!
It is what kafka was build todo. It is how we do it today. All I am
saying
is that a little broker help doing the producer swap is super useful.

For everyone interested in why kafka is so powerful with approach B,
please watch https://youtu.be/bEbeZPVo98c?t=1633
I already looked up a good point in time, I think after 5 minutes the
"state" topic is handled and you should be able to understand me
and inch better.

Please do not do A to the project, it deserves better!

Best Jan



On 13.03.2018 02:40, Jun Rao wrote:

Hi, Jan,

Thanks for the reply. A few more comments below.

50. Ok, we can think a bit harder for supporting compacted topics.

51. This is a fundamental design question. In the more common case,
the
reason why someone wants to increase the number of partitions is that
the
consumer application is slow and one wants to run more consumer
instances
to increase the degree of parallelism. So, fixing the number of
running
consumer instances when expanding the partitions won't help this case.
If
we do need to increase the number of consumer instances, we need to
somehow
reshuffle the state of the consumer across instances. What we have
been
discussing in this KIP is whether we can do this more effectively
through
the KStream library (e.g. through a 2-phase partition expansion). This
will
add some complexity, but it's probably better than everyone doing this
in
the application space. The recopying approach that you mentioned
doesn't
seem to address the consumer state management issue when the consumer
switches from an old to a new topic.

52. As for your example, it depends on whether the join key is the
same
between (A,B) and (B,C). If the join key is the same, we can do a
2-phase
partition expansion of A, B, and C together. If the join keys are
different, one would need to repartition the data on a different key
for
the second join, then the partition expansion can be done
independently
between (A,B) and (B,C).

53. If you always fix the number of consumer instances, we you
described
works. However, as I mentioned in #51, I am not sure how your proposal
deals with consumer states when the number of consumer instances
grows.
Also, it just seems that it's better to avoid re-copying the existing
data.

60. "just want to throw in my question from the longer email in the
other
Thread here. How will the bloom filter help a new consumer to decide
to
apply the key or not?" Not sure that I fully understood your question.
The
consumer just reads whatever key is in the log. The bloom filter just
helps
clean up the old keys.

61. "Why can we afford having a topic where its apparently not
possible
to
start a new application on? I think this is an overall flaw of the
discussed idea here. Not playing attention to the overall
architecture."
Could you explain a bit more when one can't start a new application?

Jun



On Sat, Mar 10, 2018 at 1:40 AM, Jan Filipiak <
jan.filip...@trivago.com
wrote:

Hi Jun, thanks for your mail.

Thank you for your questions!
I think they are really good and tackle the core of the problem I
see.

I will answer inline, mostly but still want to set the tone here.

The core strength of kafka is what Martin once called the
kappa-Architecture. How does this work?
You have everything as a log as in kafka. When you need to change
something.
You create the new version of your application and leave it running
in
parallel.
Once the new version is good you switch your users to use the new
application.

The online reshuffling effectively breaks this architecture and I
think
the switch in thinking here is more harmful
than any details about the partitioning function to allow such a
change.
I
feel with my suggestion we are the closest to
the original and battle proven architecture and I can only warn to
move
away from it.

I might have forgotten something, sometimes its hard for me to
getting
all
the thoughts captured in a mail, but I hope the comments inline will
further make my concern clear, and put some emphasis on why I prefer
my
solution ;)

One thing we should all be aware of when discussing this, and I think
Dong
should have mentioned it (maybe he did).
We are not discussing all of this out of thin air but there is an
effort
in the Samza project.

https://cwiki.apache.org/confluence/display/SAMZA/SEP-5%3A+
Enable+partition+expansion+of+input+streams
https://issues.apache.org/jira/browse/SAMZA-1293

To be clear. I think SEP-5 (state of last week, dont know if it
adapted
to
this discussion) is on a way better path than KIP-253, and I can't
really
explain why.

Best Jan,

nice weekend everyone


On 09.03.2018 03:36, Jun Rao wrote:

Hi, Jan,

Thanks for the feedback. Just some comments on the earlier points
that
you
mentioned.

50. You brought up the question of whether existing data needs to be
copied
during partition expansion. My understand of your view is that avoid
copying existing data will be more efficient, but it doesn't work
well
with
compacted topics since some keys in the original partitions will
never
be
cleaned. It would be useful to understand your use case of compacted
topics
a bit more. In the common use case, the data volume in a compacted
topic
may not be large. So, I am not sure if there is a strong need to
expand
partitions in a compacted topic, at least initially.

I do agree. State is usually smaller. Update rates might be also

competitively high.
Doing Log-compaction (even beeing very efficient and configurable) is
also
a more expensive operation than
just discarding old segments. Further if you want to use more
consumers
processing the events
you also have to grow the number of partitions. Especially for
use-cases
we do (KIP-213) a tiny state full
table might be very expensive to process if it joins against a huge
table.

I can just say we have been in the spot of needing to grow log
compacted
topics. Mainly for processing power we can bring to the table.

Further i am not at all concerned about the extra spaced used by
"garbage
keys". I am more concerned about the correctness of innocent
consumers.
The
logic becomes complicated. Say for streams one would need to load the
record into state but not forward it the topology ( to have it
available
for shuffeling). I rather have it simple and a topic clean regardless
if
it
still has its old partition count. Especially with multiple
partitions
growth's I think it becomes insanely hard to to this shuffle correct.
Maybe
Streams and Samza can do it. Especially if you do "hipster stream
processing" <https://www.confluent.io/blog
/introducing-kafka-streams-
stream-processing-made-simple/>. This makes kafka way to
complicated.
With my approach I think its way simpler because the topic has no
"history"
in terms of partitioning but is always clean.


51. "Growing the topic by an integer factor does not require any
state

redistribution at all." Could you clarify this a bit more? Let's say
you
have a consumer app that computes the windowed count per key. If you
double
the number of partitions from 1 to 2 and grow the consumer instances
from
1
to 2, we would need to redistribute some of the counts to the new
consumer
instance. Regarding to linear hashing, it's true that it won't solve
the
problem with compacted topics. The main benefit is that it
redistributes
the keys in one partition to no more than two partitions, which
could
help
redistribute the state.

You don't need to spin up a new consumer in this case. every
consumer

would just read every partition with the (partition % num_task)
task.
it
will still have the previous data right there and can go on.

This sounds contradictory to what I said before, but please bear with
me.

52. Good point on coordinating the expansion of 2 topics that need to
be

joined together. This is where the 2-phase partition expansion could
potentially help. In the first phase, we could add new partitions to
the 2
topics one at a time but without publishing to the new patitions.
Then,
we
can add new consumer instances to pick up the new partitions. In
this
transition phase, no reshuffling is needed since no data is coming
from
the
new partitions. Finally, we can enable the publishing to the new
partitions.

I think its even worse than you think. I would like to introduce the

Term
transitive copartitioning. Imagine
2 streams application. One joins (A,B) the other (B,C) then there is
a
transitive copartition requirement for
(A,C) to be copartitioned aswell. This can spread significantly and
require many consumers to adapt at the same time.

It is also not entirely clear to me how you not need reshuffling in
this
case. If A has a record that never gets updated after the expansion
and
the
coresponding B record moves to a new partition. How shall they meet
w/o
shuffle?

53. "Migrating consumer is a step that might be made completly

unnecessary
if - for example streams - takes the gcd as partitioning scheme
instead
of
enforcing 1 to 1." Not sure that I fully understand this. I think
you
mean
that a consumer application can run more instances than the number
of
partitions. In that case, the consumer can just repartitioning the
input
data according to the number of instances. This is possible, but
just
has
the overhead of reshuffling the data.

No what I meant is ( that is also your question i think Mathias)
that
if

you grow a topic by a factor.
Even if your processor is statefull you can can just assign all the
multiples of the previous partition to
this consumer and the state to keep processing correctly will be
present
w/o any shuffling.

Say you have an assignment
Statefull consumer => partition
0 => 0
1 => 1
2 => 2

and you grow you topic by 4 you get,

0 => 0,3,6,9
1 => 1,4,7,10
2 => 2,5,8,11

Say your hashcode is 8. 8%3 => 2  before so consumer for partition 2
has
it.
Now you you have 12 partitions so 8%12 => 8, so it goes into
partition
8
which is assigned to the same consumer
who had 2 before and therefore knows the key.

Userland reshuffeling is there as an options. And it does exactly
what
I
suggest. And I think its the perfect strategie. All I am suggestion
is
broker side support to switch the producers to the newly partitioned
topic.
Then the old (to few partition topic) can go away.  Remember the list
of
steps in the beginning of this thread. If one has broker support for
all
where its required and streams support for those that aren’t
necessarily.
Then one has solved the problem.
I repeat it because I think its important. I am really happy that you
brought that up! because its 100% what I want just with the
differences
to
have an option to discard the to small topic later (after all
consumers
adapted). And to have order correct there. I need broker support
managing
the copy process + the produces and fence them against each other. I
also
repeat. the copy process can run for weeks in the worst case. Copying
the
data is not the longest task migrating consumers might very well be.
Once all consumers switched and copying is really up to date (think
ISR
like up to date) only then we stop the producer, wait for the copy to
finish and use the new topic for producing.

After this the topic is perfect in shape. and no one needs to worry
about
complicated stuff. (old keys hanging around might arrive in some
other
topic later.....). can only imagine how many tricky bugs gonna arrive
after
someone had grown and shrunken is topic 10 times.






54. "The other thing I wanted to mention is that I believe the
current

suggestion (without copying data over) can be implemented in pure
userland
with a custom partitioner and a small feedbackloop from
ProduceResponse
=>
Partitionier in coorporation with a change management system." I am
not
sure a customized partitioner itself solves the problem. We probably
need
some broker side support to enforce when the new partitions can be
used.
We
also need some support on the consumer/kstream side to preserve the
per
key
ordering and potentially migrate the processing state. This is not
trivial
and I am not sure if it's ideal to fully push to the application
space.

Broker support is defenitly the preferred way here. I have nothing

against
broker support.
I tried to say that for what I would preffer - copying the data over,
at
least for log compacted topics -
I would require more broker support than the KIP currently offers.



Jun

On Tue, Mar 6, 2018 at 10:33 PM, Jan Filipiak <
jan.filip...@trivago.com
wrote:

Hi Dong,

are you actually reading my emails, or are you just using the
thread I

started for general announcements regarding the KIP?

I tried to argue really hard against linear hashing. Growing the
topic
by
an integer factor does not require any state redistribution at
all. I
fail
to see completely where linear hashing helps on log compacted
topics.

If you are not willing to explain to me what I might be
overlooking:
that
is fine.
But I ask you to not reply to my emails then. Please understand my
frustration with this.

Best Jan



On 06.03.2018 19:38, Dong Lin wrote:

Hi everyone,

Thanks for all the comments! It appears that everyone prefers
linear

hashing because it reduces the amount of state that needs to be
moved
between consumers (for stream processing). The KIP has been
updated
to
use
linear hashing.

Regarding the migration endeavor: it seems that migrating producer
library
to use linear hashing should be pretty straightforward without
much operational endeavor. If we don't upgrade client library to
use
this
KIP, we can not support in-order delivery after partition is
changed
anyway. Suppose we upgrade client library to use this KIP, if
partition
number is not changed, the key -> partition mapping will be
exactly
the
same as it is now because it is still determined using
murmur_hash(key)
%
original_partition_num. In other words, this change is backward
compatible.

Regarding the load distribution: if we use linear hashing, the
load
may
be
unevenly distributed because those partitions which are not split
may
receive twice as much traffic as other partitions that are split.
This
issue can be mitigated by creating topic with partitions that are
several
times the number of consumers. And there will be no imbalance if
the
partition number is always doubled. So this imbalance seems
acceptable.

Regarding storing the partition strategy as per-topic config: It
seems
not
necessary since we can still use murmur_hash as the default hash
function
and additionally apply the linear hashing algorithm if the
partition
number
has increased. Not sure if there is any use-case for producer to
use a
different hash function. Jason, can you check if there is some
use-case
that I missed for using the per-topic partition strategy?

Regarding how to reduce latency (due to state store/load) in
stream
processing consumer when partition number changes: I need to read
the
Kafka
Stream code to understand how Kafka Stream currently migrate state
between
consumers when the application is added/removed for a given job. I
will
reply after I finish reading the documentation and code.


Thanks,
Dong


On Mon, Mar 5, 2018 at 10:43 AM, Jason Gustafson <
ja...@confluent.io>
wrote:

Great discussion. I think I'm wondering whether we can continue to
leave

Kafka agnostic to the partitioning strategy. The challenge is

communicating
the partitioning logic from producers to consumers so that the
dependencies
between each epoch can be determined. For the sake of discussion,
imagine
you did something like the following:

1. The name (and perhaps version) of a partitioning strategy is
stored
in
topic configuration when a topic is created.
2. The producer looks up the partitioning strategy before writing
to
a
topic and includes it in the produce request (for fencing). If it
doesn't
have an implementation for the configured strategy, it fails.
3. The consumer also looks up the partitioning strategy and uses
it
to
determine dependencies when reading a new epoch. It could either
fail
or
make the most conservative dependency assumptions if it doesn't
know
how
to
implement the partitioning strategy. For the consumer, the new
interface
might look something like this:

// Return the partition dependencies following an epoch bump
Map<Integer, List<Integer>> dependencies(int
numPartitionsBeforeEpochBump,
int numPartitionsAfterEpochBump)

The unordered case then is just a particular implementation which
never
has
any epoch dependencies. To implement this, we would need some way
for
the
consumer to find out how many partitions there were in each
epoch,
but
maybe that's not too unreasonable.

Thanks,
Jason


On Mon, Mar 5, 2018 at 4:51 AM, Jan Filipiak <
jan.filip...@trivago.com
wrote:

Hi Dong

thank you very much for your questions.

regarding the time spend copying data across:
It is correct that copying data from a topic with one partition
mapping

to

a topic with a different partition mapping takes way longer than

we
can

stop producers. Tens of minutes is a very optimistic estimate
here.

Many
people can not afford copy full steam and therefore will have
some
rate
limiting in place, this can bump the timespan into the day's.
The
good

part

is that the vast majority of the data can be copied while the

producers

are

still going. One can then, piggyback the consumers ontop of this

timeframe,

by the method mentioned (provide them an mapping from their old

offsets

to

new offsets in their repartitioned topics. In that way we
separate

migration of consumers from migration of producers (decoupling

these
is
what kafka is strongest at). The time to actually swap over the
producers
should be kept minimal by ensuring that when a swap attempt is
started

the

consumer copying over should be very close to the log end and is

expected

to finish within the next fetch. The operation should have a
time-out
and
should be "reattemtable".

Importance of logcompaction:
If a producer produces key A, to partiton 0, its forever gonna
be
there,
unless it gets deleted. The record might sit in there for
years. A
new
producer started with the new partitions will fail to delete the
record

in

the correct partition. Th record will be there forever and one
can

not

reliable bootstrap new consumers. I cannot see how linear hashing

can

solve

this.

Regarding your skipping of userland copying:

100%, copying the data across in userland is, as far as i can
see,
only
a
usecase for log compacted topics. Even for logcompaction +
retentions
it
should only be opt-in. Why did I bring it up? I think log
compaction
is
a
very important feature to really embrace kafka as a "data
plattform".
The
point I also want to make is that copying data this way is
completely
inline with the kafka architecture. it only consists of reading
and

writing

to topics.

I hope it clarifies more why I think we should aim for more than

the
current KIP. I fear that once the KIP is done not much more
effort
will

be

taken.

On 04.03.2018 02:28, Dong Lin wrote:
Hey Jan,

In the current proposal, the consumer will be blocked on waiting
for

other
consumers of the group to consume up to a given offset. In most

cases,
all
consumers should be close to the LEO of the partitions when the
partition
expansion happens. Thus the time waiting should not be long e.g.
on
the

order of seconds. On the other hand, it may take a long time to
wait

for
the entire partition to be copied -- the amount of time is
proportional

to

the amount of existing data in the partition, which can take

tens of

minutes. So the amount of time that we stop consumers may not be
on

the
same order of magnitude.

If we can implement this suggestion without copying data over
in
purse
userland, it will be much more valuable. Do you have ideas on
how
this

can

be done?

Not sure why the current KIP not help people who depend on log

compaction.
Could you elaborate more on this point?

Thanks,

Dong
On Wed, Feb 28, 2018 at 10:55 PM, Jan
Filipiak<Jan.Filipiak@trivago.
com
wrote:

Hi Dong,

I tried to focus on what the steps are one can currently
perform
to

expand

or shrink a keyed topic while maintaining a top notch
semantics.
I can understand that there might be confusion about "stopping
the
consumer". It is exactly the same as proposed in the KIP.
there
needs

to

be

a time the producers agree on the new partitioning. The extra
semantics I
want to put in there is that we have a possibility to wait
until

all
the

existing data
is copied over into the new partitioning scheme. When I say
stopping

I
think more of having a memory barrier that ensures the
ordering. I
am
still
aming for latencies  on the scale of leader failovers.

Consumers have to explicitly adapt the new partitioning scheme
in
the
above scenario. The reason is that in these cases where you
are

dependent

on a particular partitioning scheme, you also have other
topics

that
have

co-partition enforcements or the kind -frequently. Therefore all
your
other

input topics might need to grow accordingly.
What I was suggesting was to streamline all these operations
as
best
as
possible to have "real" partition grow and shrinkage going on.

Migrating

the producers to a new partitioning scheme can be much more

streamlined
with proper broker support for this. Migrating consumer is a
step

that
might be made completly unnecessary if - for example streams -
takes

the

gcd as partitioning scheme instead of enforcing 1 to 1.
Connect

consumers
and other consumers should be fine anyways.
I hope this makes more clear where I was aiming at. The rest
needs

to
be

figured out. The only danger i see is that when we are

introducing

this
feature as supposed in the KIP, it wont help any people
depending
on

log
compaction.

The other thing I wanted to mention is that I believe the
current

suggestion (without copying data over) can be implemented in
pure

userland
with a custom partitioner and a small feedbackloop from
ProduceResponse
=>
Partitionier in coorporation with a change management system.

Best Jan








On 28.02.2018 07:13, Dong Lin wrote:

Hey Jan,

I am not sure if it is acceptable for producer to be stopped
for a

while,

particularly for online application which requires low
latency. I
am
also
not sure how consumers can switch to a new topic. Does user

application

needs to explicitly specify a different topic for

producer/consumer
to

subscribe to? It will be helpful for discussion if you can
provide

more
detail on the interface change for this solution.

Thanks,
Dong

On Mon, Feb 26, 2018 at 12:48 AM, Jan
Filipiak<Jan.Filipiak@trivago.

com

wrote:

Hi,
just want to throw my though in. In general the functionality
is

very
usefull, we should though not try to find the architecture to

hard
while
implementing.

The manual steps would be to

create a new topic
the mirrormake from the new old topic to the new topic
wait for mirror making to catch up.
then put the consumers onto the new topic
            (having mirrormaker spit out a mapping from old
offsets to
new
offsets:
                if topic is increased by factor X there is
gonna
be a
clean
mapping from 1 offset in the old topic to X offsets in the
new
topic,
                if there is no factor then there is no
chance to
generate a
mapping that can be reasonable used for continuing)
            make consumers stop at appropriate points and
continue
consumption
with offsets from the mapping.
have the producers stop for a minimal time.
wait for mirrormaker to finish
let producer produce with the new metadata.


Instead of implementing the approach suggest in the KIP
which
will
leave
log compacted topic completely crumbled and unusable.
I would much rather try to build infrastructure to support
the
mentioned
above operations more smoothly.
Especially having producers stop and use another topic is
difficult

and

it would be nice if one can trigger "invalid metadata"

exceptions

for
them
and
if one could give topics aliases so that their produces with
the
old
topic
will arrive in the new topic.

The downsides are obvious I guess ( having the same data
twice
for

the

transition period, but kafka tends to scale well with

datasize).

So
its a

nicer fit into the architecture.

I further want to argument that the functionality by the KIP
can
completely be implementing in "userland" with a custom
partitioner

that

handles the transition as needed. I would appreciate if

someone

could
point
out what a custom partitioner couldn't handle in this case?
With the above approach, shrinking a topic becomes the same
steps.
Without
loosing keys in the discontinued partitions.

Would love to hear what everyone thinks.

Best Jan


















On 11.02.2018 00:35, Dong Lin wrote:

Hi all,

I have created KIP-253: Support in-order message delivery
with

partition

expansion. See
https://cwiki.apache.org/confluence/display/KAFKA/KIP-253%
3A+Support+in-order+message+de
livery+with+partition+expansio
n
.

This KIP provides a way to allow messages of the same key
from
the
same
producer to be consumed in the same order they are produced
even
if

we

expand partition of the topic.

Thanks,
Dong