We’re setting the timestamp to the date of last update of each document. That’s why I think pushing everything to PubSub before starting the job could work.
The data is read from CouchDB’s changes feed into PubSub and the idea is to transfer everything into GCS since the very beginning. Aside from that we have another job running “live” with live data. Any other idea/suggestion? Thanks!! On Thu, 8 Feb 2018 at 22:39, Robert Bradshaw <[email protected]> wrote: > You can set the timestamp attribute of your pubsub messages which will > hold back the watermark, see > > > https://beam.apache.org/documentation/sdks/javadoc/2.2.0/org/apache/beam/sdk/io/gcp/pubsub/PubsubIO.Read.html#withTimestampAttribute-java.lang.String- > > However, if you're mixing historical and live data, it may make more > sense to read these as two separate sources (e.g. the static data from > a set of files, the live data from pubsub) and then flatten them for > further processing. > > On Thu, Feb 8, 2018 at 1:23 PM, Carlos Alonso <[email protected]> > wrote: > > Yes, the data is finite (although it comes through PubSub, so I guess is > > considered unbounded). > > How could I hold the watermark and prevent it from moving? > > > > Thanks! > > > > On Thu, Feb 8, 2018 at 10:06 PM Robert Bradshaw <[email protected]> > wrote: > >> > >> Where is the watermark for this old data coming from? Rather than > >> messing with allowed lateness, would it be possible to hold the > >> watermark back appropriately during the time you're injecting old data > >> (assuming there's only a finite amount of it)? > >> > >> On Thu, Feb 8, 2018 at 12:56 PM, Carlos Alonso <[email protected]> > >> wrote: > >> > Thanks for your responses!! > >> > > >> > I have a scenario where I have to reprocess very disordered data for 4 > >> > or 5 > >> > years and I don't want to lose any data. I'm thinking of setting a > very > >> > big > >> > allowed lateness (5 years), but before doing that I'd like to > understand > >> > the > >> > consequences that may have. I guess memory wise will be very consuming > >> > as no > >> > window will ever expire, but I guess I could overcome that with brute > >> > force > >> > (many machines with many RAM) but, are there more concerns I should be > >> > aware > >> > of? This should be a one-off thing. > >> > > >> > Thanks! > >> > > >> > On Thu, Feb 8, 2018 at 6:59 PM Raghu Angadi <[email protected]> > wrote: > >> >> > >> >> On Wed, Feb 7, 2018 at 10:33 PM, Pawel Bartoszek > >> >> <[email protected]> wrote: > >> >>> > >> >>> Hi Raghu, > >> >>> Can you provide more details about increasing allowed lateness? Even > >> >>> if I > >> >>> do that I still need to compare event time of record with processing > >> >>> time(system current time) in my ParDo? > >> >> > >> >> > >> >> I see. PaneInfo() associated with each element has 'Timing' enum, so > we > >> >> can tell if the element is late, but it does not tell how late. > >> >> How about this : We can have a periodic timer firing every minute and > >> >> store the scheduled time of the timer in state as the watermark time. > >> >> We > >> >> could compare element time to this stored time for good approximation > >> >> (may > >> >> require parallel stage with global window, dropping any events that > >> >> 'clearly > >> >> within limits' based on current time). There are probably other ways > to > >> >> do > >> >> this with timers within existing stage. > >> >> > >> >>> > >> >>> Pawel > >> >>> > >> >>> On 8 February 2018 at 05:40, Raghu Angadi <[email protected]> > wrote: > >> >>>> > >> >>>> The watermark is not directly available, you essentially infer from > >> >>>> fired triggers (e.g. fixed windows). I would consider some of these > >> >>>> options > >> >>>> : > >> >>>> - Adhoc debugging : if the pipeline is close to realtime, you can > >> >>>> just > >> >>>> guess if a element will be dropped based on its timestamp and > current > >> >>>> time > >> >>>> in the first stage (before first aggregation) > >> >>>> - Increase allowed lateness (say to 3 days) and drop the elements > >> >>>> yourself you notice are later than 1 day. > >> >>>> - Place the elements into another window with larger allowed > >> >>>> lateness > >> >>>> and log very late elements in another parallel aggregation (see > >> >>>> TriggerExample.java in Beam repo). > >> >>>> > >> >>>> On Wed, Feb 7, 2018 at 2:55 PM, Carlos Alonso < > [email protected]> > >> >>>> wrote: > >> >>>>> > >> >>>>> Hi everyone!! > >> >>>>> > >> >>>>> I have a streaming job running with fixed windows of one hour and > >> >>>>> allowed lateness of two days and the number of dropped due to > >> >>>>> lateness > >> >>>>> elements is slowly, but continuously growing and I'd like to > >> >>>>> understand > >> >>>>> which elements are those. > >> >>>>> > >> >>>>> I'd like to get the watermark from inside the job to compare it > >> >>>>> against > >> >>>>> each element and write log messages with the ones that will be > >> >>>>> potentially > >> >>>>> discarded.... Does that approach make any sense? I which case... > How > >> >>>>> can I > >> >>>>> get the watermark from inside the job? Any other ideas? > >> >>>>> > >> >>>>> Thanks in advance!! > >> >>>> > >> >>>> > >> >>> > >> > >
