flink-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Josh <jof...@gmail.com>
Subject Re: Combining streams with static data and using REST API as a sink
Date Mon, 23 May 2016 17:36:27 GMT
Hi Max,

Thanks, that's very helpful re the REST API sink. For now I don't need
exactly once guarantees for the sink, so I'll just write a simple HTTP sink
implementation. But may need to move to the idempotent version in future!

For 1), that sounds like a simple/easy solution, but how would I handle
occasional updates in that case, since I guess the open() function is only
called once? Do I need to periodically restart the job, or periodically
trigger tasks to restart and refresh their data? Ideally I would want this
job to be running constantly.

Josh

On Mon, May 23, 2016 at 5:56 PM, Maximilian Michels <mxm@apache.org> wrote:

> Hi Josh,
>
> 1) Use a RichFunction which has an `open()` method to load data (e.g. from
> a database) at runtime before the processing starts.
>
> 2) No that's fine. If you want your Rest API Sink to interplay with
> checkpointing (for fault-tolerance), this is a bit tricky though depending
> on the guarantees you want to have. Typically, you would have "at least
> once" or "exactly once" semantics on the state. In Flink, this is easy to
> achieve, it's a bit harder for outside systems.
>
> "At Least Once"
>
> For example, if you increment a counter in a database, this count will be
> off if you recover your job in the case of a failure. You can checkpoint
> the current value of the counter and restore this value on a failure (using
> the Checkpointed interface). However, your counter might decrease
> temporarily when you resume from a checkpoint (until the counter has caught
> up again).
>
> "Exactly Once"
>
> If you want "exactly once" semantics on outside systems (e.g. Rest API),
> you'll need idempotent updates. An idempotent variant of this would be a
> count with a checkpoint id (cid) in your database.
>
> | cid | count |
> |-----+-------|
> |   0 |     3 |
> |   1 |    11 |
> |   2 |    20 |
> |   3 |   120 |
> |   4 |   137 |
> |   5 |   158 |
>
> You would then always read the newest cid value for presentation. You
> would only write to the database once you know you have completed the
> checkpoint (CheckpointListener). You can still fail while doing that, so
> you need to keep the confirmation around in the checkpoint such that you
> can confirm again after restore. It is important that confirmation can be
> done multiple times without affecting the result (idempotent). On recovery
> from a checkpoint, you want to delete all rows higher with a cid higher
> than the one you resume from. For example, if you fail after checkpoint 3
> has been created, you'll confirm 3 (because you might have failed before
> you could confirm) and then delete 4 and 5 before starting the computation
> again.
>
> You see, that strong consistency guarantees can be a bit tricky. If you
> don't need strong guarantees and undercounting is ok for you, implement a
> simple checkpointing for "at least once" using the Checkpointed interface
> or the KeyValue state if your counter is scoped by key.
>
> Cheers,
> Max
>
>
> On Mon, May 23, 2016 at 3:22 PM, Josh <jofo90@gmail.com> wrote:
> > Hi all,
> >
> > I am new to Flink and have a couple of questions which I've had trouble
> > finding answers to online. Any advice would be much appreciated!
> >
> > What's a typical way of handling the scenario where you want to join
> > streaming data with a (relatively) static data source? For example, if I
> > have a stream 'orders' where each order has an 'item_id', and I want to
> join
> > this stream with my database of 'items'. The database of items is mostly
> > static (with perhaps a few new items added every day). The database can
> be
> > retrieved either directly from a standard SQL database (postgres) or via
> a
> > REST call. I guess one way to handle this would be to distribute the
> > database of items with the Flink tasks, and to redeploy the entire job if
> > the items database changes. But I think there's probably a better way to
> do
> > it?
> > I'd like my Flink job to output state to a REST API. (i.e. using the REST
> > API as a sink). Updates would be incremental, e.g. the job would output
> > tumbling window counts which need to be added to some property on a REST
> > resource, so I'd probably implement this as a PATCH. I haven't found much
> > evidence that anyone else has used a REST API as a Flink sink - is there
> a
> > reason why this might be a bad idea?
> >
> > Thanks for any advice on these,
> >
> > Josh
>
>

Mime
View raw message