Hey Josh,
> I suspect now that this would only happen if the partitioning formula is
>identical
This is true, Kafka allows pluggable partitioning strategies, so you could
use a custom partitioner to make the partitioning map to whatever your
upstream shards are doing.
> I can declare the shard-id as the partitionKey with each Message, but
>will this guarantee that no two shards will ever be MERGED by the
>partitioner?
If the partition count for the topic matches the shard count from the
upstream DB, then yes, it guarantees that each shard will exist only in
its own partition, and never be merged.
> I think we'd still end up with out-of-order events in any partitions
>that merge multiple upstream shards, unless we take steps to sort them.
>Is this accurate?
Correct. If you merge shards, then the messages for a single shard will be
in-order within the partition, but the ordering between shards within a
single partition is undefined. This is essentially the same as the
multi-producer problem. That said, this doesn't seem to me to be a big
deal, provided that the consumer's processing logic only works with
messages from within a single shard at any one time. In practice, a lot of
use cases generalize to this (e.g. counting grouped by a key in the
shard). I think this is what Ben is saying in his email ("Partitioning is
a lot like sharding -- it's important that all data for the same user ends
up in the same shard/partition, but it's totally fine if data for a bunch
of unrelated users is in there too.").
> Now, just to stir the pot: the upstream OLTP database is heavily
>bucketed to achieve fine-grained distribution: my source has *1024*
>logical shards! I think this might need to be stepped down with an
>initial merge process...
It really depends on the size of your cluster. We have some grids with
topics in the 512-768 partition range, and it works OK. There are a few
places where large partition counts will impact your latency (leadership
election, MirrorMaker consumer group fail over, etc), though. Smaller
partition counts are usually preferable, since they're more operable.
In general, I agree with what Ben's saying, but a lot of it comes down to
exactly what use cases you have, how things are sharded, what kinds of
joins you're trying to do, etc.
Cheers,
Chris
On 12/9/14 12:54 PM, "Ben Kirwin" <[email protected]> wrote:
>I don't think it has to be quite so tricky. Some quick notes:
>
>'Preserving order' is a really nice way to think about these problems.
>At the beginning of your job, you have an ordered series of files for
>each shard, and each file has an ordered series of records. (Here,
>I'll assume that everything is sharded by hashing the user id.)
>There's a few interesting relationships here:
>
>- If two events for a user are always in the same shard, then all
>these events already have a 'total order'. This order is semantically
>important, so you want to preserve it all the way through your stream
>processing pipeline. The easiest way to preserve the ordering of two
>messages in Kafka is to write them to the same topic / partition.
>- Ordering isn't always meaningful -- for example, two events for
>unrelated users that happened in the same shard are *also* ordered,
>but since all these users have in common is that they hashed to the
>same shard, you probably don't care. In that case, you're free to
>*not* preserve the ordering and write them to different topics /
>partitions.
>- For events from totally different shards, there's no 'existing'
>ordering, so there's nothing to preserve. Again, you can partition
>these however you like.
>
>Partitioning is a lot like sharding -- it's important that all data
>for the same user ends up in the same shard/partition, but it's
>totally fine if data for a bunch of unrelated users is in there too.
>
>So assuming all that is right, one solution is to put the data for all
>event types in a *single* topic, partitioned by user id. Each
>partition will have data for a bunch of users, but all the data for a
>particular user is in just one partition. You can have as many or as
>few partitions as you want. If you do that, analyses like "what level
>was this user when they made that purchase?" are trivial; instead of
>worrying about joins, you just have your job filter out the events you
>don't care about. You'll still have to regroup / etc. for jobs that
>aggregate across a bunch of different users, but that's normal: most
>users aren't from the same shard, so there was no strict ordering
>between them in the first place.
>
>There's a bunch of possible variations on this, but that's the basic
>idea I was getting at last week.
>
>On Mon, Dec 8, 2014 at 3:50 PM, josh gruenberg <[email protected]> wrote:
>> Hi guys,
>>
>> Thanks for the quick replies, and for your attention to the correctness
>> beyond my specific questions!
>>
>> After a weekend of reflection, I can now see just how much I DIDN'T
>> understand before about time and order in stream-processing :-).
>>
>> You're right to scrutinize the ordering guarantees given multiple
>>producers
>> fanning out the upstream partitions. This has me reconsidering my entire
>> topology. My idea as of Friday morning was to generate a topic in kafka
>> containing input-file URIs, partitioned by upstream shard-id, and to
>>then
>> have a Task explode the files into type-collated topics, partitioned by
>> user-id. I was mistakenly imagining that, because the upstream shards
>>are
>> already partitioned by user-id, this would somehow result in the
>>upstream
>> partitioning/ordering being preserved as long as downstream partitions
>>were
>> also based on user-id. I suspect now that this would only happen if the
>> partitioning formula is identical (murmur3, anyone? ;-), and the number
>>of
>> partitions is also consistent end-to-end! Does that sound right to you?
>> Anyway, I believe my initial design was flawed, as you suspected.
>>
>> Ben's suggestion that we instead partition by upstream shard-id is
>> intriguing... But I'm confused about this: I can declare the shard-id as
>> the partitionKey with each Message, but will this guarantee that no two
>> shards will ever be MERGED by the partitioner? At the very least, I'd
>> expect that we'd need to ensure that the cardinality of the partitions
>> matched the upstream shard-count end-to-end... If not (ie, if there are
>> fewer partitions), I think we'd still end up with out-of-order events in
>> any partitions that merge multiple upstream shards, unless we take
>>steps to
>> sort them. Is this accurate?
>>
>>
>> Now, just to stir the pot: the upstream OLTP database is heavily
>>bucketed
>> to achieve fine-grained distribution: my source has *1024* logical
>>shards!
>> I think this might need to be stepped down with an initial merge
>>process...
>>
>>
>> Then, as per my original question, the need to merge-sort just reappears
>> whenever we want to consume multiple streams, and I don't see a way to
>> avoid injecting (and propagating!) timestamp-heartbeats to maintain
>>global
>> order through a merge without blocking for "drained" streams. Moreover,
>>if
>> we want to alter the partitioning downstream, we'll need to do a more
>> complicated 2-step shuffle: first merging upstream partitions together,
>> then buffering multiple messages until we have data from all upstream
>> partitions AND streams to determine what's next..? (This thinking is
>> half-baked, please correct me if I'm confused.)
>>
>> Similarly, sharing stateful Stores outside of their generating Task (for
>> example, to join a user's current "level" with multiple
>>event-types/Tasks)
>> seems troubling, as I'm not sure how to coordinate timestamps with the
>> Store's changelog for this.
>>
>>
>> Overwhelming. Am I overthinking this? Do you think I can get this
>>working
>> without implementing full heartbeat-plumbing and custom
>> buffering/merge-sorting for every Task? I'm not seeing a robust
>> alternative...
>>
>>
>> Thanks for your insights; I appreciate any suggestions you can offer!
>>
>> Cheers,
>> -josh
>>
>>
>> On Fri, Dec 5, 2014 at 5:12 PM, Chris Riccomini <
>> [email protected]> wrote:
>>
>>> Hey Ben,
>>>
>>> This is a good point. I was thinking more about this as well. Another
>>> example is when a message is sent but not yet replicated to the rest of
>>> the ISR in Kafka. In such a case, the Samza job would think it's at
>>>head
>>> (since the last message it received == the high watermark), but there
>>>are
>>> still messages in-flight that might be < the timestamp for the stream
>>> you're joining against.
>>>
>>> > In your case, it seems like partitioning by shard id should work
>>>
>>> Yea, this is an interesting idea. This should work provided that the
>>>join
>>> is happening within the same shard (in the example given, that was the
>>> case), and there is only one producer for the shard, which is properly
>>> interleaving the messages from the two strictly ordered files (assuming
>>> the files are one per-table).
>>>
>>> Cheers,
>>> Chris
>>>
>>> On 12/5/14 4:54 PM, "Ben Kirwin" <[email protected]> wrote:
>>>
>>> >> In order to visit each input in the correct order, the
>>> >>event-type-specific
>>> >> streams need to be interleaved/time-aligned for each summarizer Task
>>> >>based
>>> >> on embedded timestamps. However, Samza offers no inter-stream
>>>ordering
>>> >> semantics, so this ordering would seem to be the job of a custom
>>> >> MessageChooser. But I don't see how a MessageChooser can do this
>>>without
>>> >> one additional bit of context: when we haven't been offered an item
>>>from
>>> >> one or more input streams, we need to know whether the missing
>>>stream(s)
>>> >> are "at head," to decide whether we must await an upcoming message
>>>for
>>> >> timestamp comparison.
>>> >
>>> >Unless I misunderstand your design, I don't think knowing whether the
>>> >stream is 'at head' actually helps you here.
>>> >
>>> >Let's say the upstream task is loading data into two topics:
>>> >'level-changed' and 'purchase-completed'. Suppose a Kafka node goes
>>> >down while it's producing a new batch of messages, and the
>>> >'purchase-completed' events in that batch are written, but the
>>> >'level-changed' events are not. The downstream Samza task will see /
>>> >process those 'purchase-completed' events, but there's no way for it
>>> >to know that it should expect some preceding 'level-changed' events --
>>> >since they never made it into Kafka, Samza think's it's 'caught up'.
>>> >(There are some other race-type problems you can get, but I think
>>> >that's the most obvious.)
>>> >
>>> >Normally, I suggest that whenever you care about the relative ordering
>>> >of some data, you try and put that data in the same partition of the
>>> >same topic. When messages are in the same partition, the ordering's
>>> >obvious -- but as you've noticed, it gets a lot trickier to
>>> >re-establish order when Kafka doesn't enforce it. In your case, it
>>> >seems like partitioning by shard id should work; consumers can just
>>> >filter out the messages they don't care about.
>>> >
>>> >In your case, another option is to add periodic 'marker' messages with
>>> >the current timestamp to each topic/partition. When your samza job
>>> >gets a marker as input, it can be sure that the upstream job will
>>> >never send an event with a smaller timestamp on that partition. When
>>> >your task sees a 'purchase-completed' event, it just needs to buffer
>>> >until it sees either a 'level-changed' event or a marker with a >=
>>> >timestamp -- and then it can be confident it knows the player's level
>>> >at that moment in time. (Still, I suggest the first option if you can
>>> >swing it -- it's a whole lot harder to mess up.)
>>> >
>>> >--
>>> >Ben Kirwin
>>> >http://ben.kirw.in/
>>>
>>>
>
>
>
>--
>Ben Kirwin
>http://ben.kirw.in/
