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From Aljoscha Krettek <aljos...@apache.org>
Subject Re: State in external db (dynamodb)
Date Thu, 07 Apr 2016 11:53:09 GMT
regarding windows and incremental aggregation. This is already happening in
Flink as of now. When you give a ReduceFunction on a window, which "sum"
internally does, the result for a window is incrementally updated whenever
a new element comes in. This incremental aggregation only happens when you
specify a ReduceFunction or a FoldFunction, not for the general case of a
WindowFunction, where all elements in the window are required.

You are right about incremental snapshots. We mainly want to introduce them
to reduce latency incurred by snapshotting. Right now, processing stalls
when a checkpoint happens.


On Thu, 7 Apr 2016 at 13:12 Shannon Carey <scarey@expedia.com> wrote:

> Thanks very kindly for your response, Stephan!
> We will definitely use a custom sink for persistence of idempotent
> mutations whenever possible. Exposing state as read-only to external
> systems is a complication we will try to avoid. Also, we will definitely
> only write to the DB upon checkpoint, and the write will be synchronous and
> transactional (no possibility of partial success/failure).
> However, we do want Flink state to be durable, we want it to be in memory
> when possible, and we want to avoid running out of memory due to the size
> of the state. For example, if you have a wide window that hasn't gotten an
> event for a long time, we want to evict that window state from memory.
> We're now thinking of using Redis (via AWS Elasticache) which also
> conveniently has TTL, instead of DynamoDB.
> I just wanted to check whether eviction of (inactive/quiet) state from
> memory is something that I should consider implementing, or whether Flink
> already had some built-in way of doing it.
> Along the same lines, I am also wondering whether Flink already has means
> of compacting the state of a window by applying an aggregation function to
> the elements so-far (eg. every time window is triggered)? For example, if
> you are only executing a sum on the contents of the window, the window
> state doesn't need to store all the individual items in the window, it only
> needs to store the sum. Aggregations other than "sum" might have that
> characteristic too. I don't know if Flink is already that intelligent or
> whether I should figure out how to aggregate window contents myself when
> possible with something like a window fold? Another poster (Aljoscha) was
> talking about adding incremental snapshots, but it sounds like that would
> only improve the write throughput not the memory usage.
> Thanks again!
> Shannon Carey
> From: Stephan Ewen <sewen@apache.org>
> Date: Wednesday, April 6, 2016 at 10:37 PM
> To: <user@flink.apache.org>
> Subject: Re: State in external db (dynamodb)
> Hi Shannon!
> Welcome to the Flink community!
> You are right, sinks need in general to be idempotent if you want
> "exactly-once" semantics, because there can be a replay of elements that
> were already written.
> However, what you describe later, overwriting of a key with a new value
> (or the same value again) is pretty much sufficient. That means that when a
> duplicate write happens during replay, the value for the key is simply
> overwritten with the same value again.
> As long as all computation is purely in Flink and you only write to the
> key/value store (rather than read from k/v, modify in Flink, write to k/v),
> you get the consistency that for example counts/aggregates never have
> duplicates.
> If Flink needs to look up state from the database (because it is no longer
> in Flink), it is a bit more tricky. I assume that is where you are going
> with "Subsequently, when an event is processed, we must be able to
> quickly load up any evicted state".  In that case, there are two things
> you can do:
> (1)  Only write to the DB upon a checkpoint, at which point it is known
> that no replay of that data will occur any more. Values from partially
> successful writes will be overwritten with correct value. I assume that is
> what you thought of when referring to the State Backend, because in some
> sense, that is what that state backend would do.
> I think it is simpler to realize that in a custom sink, than developing a
> new state backend.  Another Flink committer (Chesnay) has developed some
> nice tooling for that, to be merged into Flink soon.
> (2) You could attach version numbers to every write, and increment the
> versions upon each checkpoint. That allows you to always refer to a
> consistent previous value, if some writes were made, but a failure occurred
> before the checkpoint completed.
> I hope these answers apply to your case. Let us know if some things are
> still unclear, or if I misunderstood your question!
> Greetings,
> Stephan
> On Wed, Apr 6, 2016 at 8:14 AM, Sanne de Roever <sanne.de.roever@gmail.com
> > wrote:
>> FYI Cassandra has a TTL on data:
>> https://docs.datastax.com/en/cql/3.1/cql/cql_using/use_expire_t.html
>> On Wed, Apr 6, 2016 at 7:55 AM, Shannon Carey <scarey@expedia.com> wrote:
>>> Hi, new Flink user here!
>>> I found a discussion on user@flink.apache.org about using DynamoDB as a
>>> sink. However, as noted, sinks have an at-least-once guarantee so your
>>> operations must idempotent.
>>> However, another way to go about this (and correct me if I'm wrong) is
>>> to write the state to the external store via a custom State Backend. Since
>>> the state participates in checkpointing, you don't have to worry about
>>> idempotency: every time state is checkpointed, overwrite the value of that
>>> key.
>>> We are starting a project with Flink, and we are interested in evicting
>>> the state from memory once a TTL is reached during which no events have
>>> come in for that state. Subsequently, when an event is processed, we must
>>> be able to quickly load up any evicted state. Does this sound reasonable?
>>> We are considering using DynamoDB for our state backend because it seems
>>> like all we will need is a key-value store. The only weakness of this is
>>> that if state gets older than, say, 2 years we would like to get rid of it
>>> which might not be easy in DynamoDB. I don't suppose Flink has any
>>> behind-the-scenes features that deal with getting rid of old state (either
>>> evicting from memory or TTL/aging out entirely)?
>>> Thanks for your time!
>>> Shannon Carey

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