> I wonder if it can be solved by storing state in the external store with a > tuple: (state, previous_state, checkpoint_id). Then when reading from the > store, if checkpoint_id is in the future, read the previous_state, > otherwise take the current_state. >
I think it did "filtering" part work. Pointing to previous checkpoint states that is complete. It do need to hold two states per snapshot(or base+delta). It would be nice if Flink can support incremental checkpointing and reason about who ref who during clean up so that each records are not growing larger and larger. But it seems off topic from your question. > On Mon, Jul 25, 2016 at 3:20 PM, Josh <jof...@gmail.com> wrote: > >> Hi Chen, >> >> Can you explain what you mean a bit more? I'm not sure I understand the >> problem. >> >> Does anyone know if the tooling discussed here has been merged into Flink >> already? Or if there's an example of what this custom sink would look like? >> I guess the sink would buffer updates in-memory between checkpoints. Then >> it would implement the Checkpointed interface and write to the external >> store in snapshotState(...)? >> >> Thanks, >> Josh >> >> On Sun, Jul 24, 2016 at 6:00 PM, Chen Qin <qinnc...@gmail.com> wrote: >> >>> >>> >>> On Jul 22, 2016, at 2:54 AM, Josh <jof...@gmail.com> wrote: >>> >>> Hi all, >>> >>> >(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 feel the problem is about how to commit all snapshots as a >>> transaction. Partial writes pose cleanup challenges when job restore. >>> A easy hack would be treat Rocksdb as cache and keep states updates >>> there. Aka aemanifest. do cleanup check before actual restore. >>> >>> >>> >>> >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. >>> >>> I am planning to implement something like this: >>> >>> Say I have a topology which looks like this: [source => operator => >>> sink], I would like it to work like this: >>> 1. Upon receiving an element, the operator retrieves some state from an >>> external key-value store (would like to put an in-memory cache on top of >>> this with a TTL) >>> 2. The operator emits a new state (and updates its in-memory cache with >>> the new state) >>> 3. The sink batches up all the new states and upon checkpoint flushes >>> them to the external store >>> >>> Could anyone point me at the work that's already been done on this? Has >>> it already been merged into Flink? >>> >>> Thanks, >>> Josh >>> >>> On Thu, Apr 7, 2016 at 12:53 PM, Aljoscha Krettek <aljos...@apache.org> >>> wrote: >>> >>>> Hi, >>>> 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. >>>> >>>> Cheers, >>>> Aljoscha >>>> >>>> On Thu, 7 Apr 2016 at 13:12 Shannon Carey <sca...@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 <se...@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.roe...@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 <sca...@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 >>>>>>> >>>>>> >>>>>> >>>>> >>> >> >
