Ok thanks. It's basically telephone area codes, they barely ever change. On Mon, 30 Sep 2019 at 06:03, Gaël Renoux <gael.ren...@datadome.co> wrote:
> Hi John, > > I've had a similar requirement, and I've resorted to simply use a static > cache (I'm coding in Scala, so that's a lazy value on a singleton object - > in Java that would be a static value on some utility class, with a > synchronized lazy-loading getter). The value is reloaded after some > duration, which adds a small latency at regular intervals. Keep in mind > that one instance of that value will be loaded on each task manager > (provided that at least one task running on that task manager calls the > getter). > > If you're OK with restarting the job when your data changes, it would be > better to load it on start (no need to synchronize stuff). Just load it > inside your job initialization code (it will be executed within the job > manager) and pass that data as a parameter to your operator's constructor. > The data format must be serializable. > > Gaël > > > On Sat, Sep 28, 2019 at 2:18 AM Sameer Wadkar <sam...@axiomine.com> wrote: > >> The main consideration in these type of scenarios is not the type of >> source function you use. The key point is how does the event operator get >> the slow moving master data and cache it. And then recover it if it fails >> and restarts again. >> >> It does not matter that the csv file does not change often. It is >> possible that the event operator may fail and restart. The csv data needs >> to made available to it again. >> >> In that scenario the initial suggestion I made to pass the csv data in >> the constructor is not adequate by itself. You need to store it in the >> operator state which allows it to recover it when it restarts on failure. >> >> As long as the above takes place you have resiliency and you can use any >> suitable method or source. I have not used Table source as much but >> connected streams and operator state has worked out for me in similar >> scenarios. >> >> Sameer >> >> Sent from my iPhone >> >> On Sep 27, 2019, at 4:38 PM, John Smith <java.dev....@gmail.com> wrote: >> >> It's a fairly small static file that may update once in a blue moon lol >> But I'm hopping to use existing functions. Why can't I just use CSV to >> table source? >> >> Why should I have to now either write my own CSV parser or look for 3rd >> party, then what put in a Java Map and lookup that map? I'm finding Flink >> to be a bit of death by 1000 paper cuts lol >> >> if i put the CSV in a table I can then use it to join across it with the >> event no? >> >> On Fri, 27 Sep 2019 at 16:25, Sameer W <sam...@axiomine.com> wrote: >> >>> Connected Streams is one option. But may be an overkill in your scenario >>> if your CSV does not refresh. If your CSV is small enough (number of >>> records wise), you could parse it and load it into an object (serializable) >>> and pass it to the constructor of the operator where you will be streaming >>> the data. >>> >>> If the CSV can be made available via a shared network folder (or S3 in >>> case of AWS) you could also read it in the open function (if you use Rich >>> versions of the operator). >>> >>> The real problem I guess is how frequently does the CSV update. If you >>> want the updates to propagate in near real time (or on schedule) the option >>> 1 ( parse in driver and send it via constructor does not work). Also in >>> the second option you need to be responsible for refreshing the file read >>> from the shared folder. >>> >>> In that case use Connected Streams where the stream reading in the file >>> (the other stream reads the events) periodically re-reads the file and >>> sends it down the stream. The refresh interval is your tolerance of stale >>> data in the CSV. >>> >>> On Fri, Sep 27, 2019 at 3:49 PM John Smith <java.dev....@gmail.com> >>> wrote: >>> >>>> I don't think I need state for this... >>>> >>>> I need to load a CSV. I'm guessing as a table and then filter my events >>>> parse the number, transform the event into geolocation data and sink that >>>> downstream data source. >>>> >>>> So I'm guessing i need a CSV source and my Kafka source and somehow >>>> join those transform the event... >>>> >>>> On Fri, 27 Sep 2019 at 14:43, Oytun Tez <oy...@motaword.com> wrote: >>>> >>>>> Hi, >>>>> >>>>> You should look broadcast state pattern in Flink docs. >>>>> >>>>> --- >>>>> Oytun Tez >>>>> >>>>> *M O T A W O R D* >>>>> The World's Fastest Human Translation Platform. >>>>> oy...@motaword.com — www.motaword.com >>>>> >>>>> >>>>> On Fri, Sep 27, 2019 at 2:42 PM John Smith <java.dev....@gmail.com> >>>>> wrote: >>>>> >>>>>> Using 1.8 >>>>>> >>>>>> I have a list of phone area codes, cities and their geo location in >>>>>> CSV file. And my events from Kafka contain phone numbers. >>>>>> >>>>>> I want to parse the phone number get it's area code and then >>>>>> associate the phone number to a city, geo location and as well count how >>>>>> many numbers are in that city/geo location. >>>>>> >>>>> > > -- > Gaël Renoux > Senior R&D Engineer, DataDome > M +33 6 76 89 16 52 <+33+6+76+89+16+52> > E gael.ren...@datadome.co <gael.ren...@datadome.co> > W www.datadome.co > <http://www.datadome.co?utm_source=WiseStamp&utm_medium=email&utm_term=&utm_content=&utm_campaign=signature> > > > <https://www.facebook.com/datadome/?utm_source=WiseStamp&utm_medium=email&utm_term=&utm_content=&utm_campaign=signature> > <https://fr.linkedin.com/company/datadome?utm_source=WiseStamp&utm_medium=email&utm_term=&utm_content=&utm_campaign=signature> > <https://twitter.com/data_dome?utm_source=WiseStamp&utm_medium=email&utm_term=&utm_content=&utm_campaign=signature> > [image: Read DataDome reviews on G2] > <https://www.g2.com/products/datadome/reviews?utm_source=review-widget&utm_source=WiseStamp&utm_medium=email&utm_term=&utm_content=&utm_campaign=signature> >
