Yes, I will do that. Regarding the metrics dump through REST, it does provide for the TM specific but refuses to do it for all jobs and vertices/operators etc .Moreover I am not sure I have access to the vertices ( vertex_id ) readily from the UI.
curl http://[jm]/taskmanagers/[tm_id] curl http://[jm]/taskmanagers/[tm_id]/metrics On Wed, Mar 24, 2021 at 4:24 AM Arvid Heise <[email protected]> wrote: > Hi Vishal, > > REST API is the most direct way to get through all metrics as Matthias > pointed out. Additionally, you could also add a JMX reporter and log to the > machines to check. > > But in general, I think you are on the right track. You need to reduce the > metrics that are sent to DD by configuring the scope / excluding variables. > > Furthermore, I think it would be a good idea to make the timeout > configurable. Could you open a ticket for that? > > Best, > > Arvid > > On Wed, Mar 24, 2021 at 9:02 AM Matthias Pohl <[email protected]> > wrote: > >> Hi Vishal, >> what about the TM metrics' REST endpoint [1]. Is this something you could >> use to get all the metrics for a specific TaskManager? Or are you looking >> for something else? >> >> Best, >> Matthias >> >> [1] >> https://ci.apache.org/projects/flink/flink-docs-release-1.12/ops/rest_api.html#taskmanagers-metrics >> >> On Tue, Mar 23, 2021 at 10:59 PM Vishal Santoshi < >> [email protected]> wrote: >> >>> That said, is there a way to get a dump of all metrics exposed by TM. I >>> was searching for it and I bet we could get it for ServieMonitor on k8s ( >>> scrape ) but am missing a way to het a TM and dump all metrics that are >>> pushed. >>> >>> Thanks and regards. >>> >>> On Tue, Mar 23, 2021 at 5:56 PM Vishal Santoshi < >>> [email protected]> wrote: >>> >>>> I guess there is a bigger issue here. We dropped the property to 500. >>>> We also realized that this failure happened on a TM that had one specific >>>> job running on it. What was good ( but surprising ) that the exception was >>>> the more protocol specific 413 ( as in the chunk is greater then some size >>>> limit DD has on a request. >>>> >>>> Failed to send request to Datadog (response was Response{protocol=h2, >>>> code=413, message=, url= >>>> https://app.datadoghq.com/api/v1/series?api_key=**********} >>>> <https://app.datadoghq.com/api/v1/series?api_key=0ffa36e48f5042465635b5843fa3f2a6%7D> >>>> ) >>>> >>>> which implies that the Socket timeout was masking this issue. The 2000 >>>> was just a huge payload that DD was unable to parse in time ( or was slow >>>> to upload etc ). Now we could go lower but that makes less sense. We could >>>> play with >>>> https://ci.apache.org/projects/flink/flink-docs-stable/ops/metrics.html#system-scope >>>> to reduce the size of the tags ( or keys ). >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Tue, Mar 23, 2021 at 11:33 AM Vishal Santoshi < >>>> [email protected]> wrote: >>>> >>>>> If we look at this >>>>> <https://github.com/apache/flink/blob/97bfd049951f8d52a2e0aed14265074c4255ead0/flink-metrics/flink-metrics-datadog/src/main/java/org/apache/flink/metrics/datadog/DatadogHttpReporter.java#L159> >>>>> code , the metrics are divided into chunks up-to a max size. and >>>>> enqueued >>>>> <https://github.com/apache/flink/blob/97bfd049951f8d52a2e0aed14265074c4255ead0/flink-metrics/flink-metrics-datadog/src/main/java/org/apache/flink/metrics/datadog/DatadogHttpClient.java#L110>. >>>>> The Request >>>>> <https://github.com/apache/flink/blob/97bfd049951f8d52a2e0aed14265074c4255ead0/flink-metrics/flink-metrics-datadog/src/main/java/org/apache/flink/metrics/datadog/DatadogHttpClient.java#L75> >>>>> has a 3 second read/connect/write timeout which IMHO should have been >>>>> configurable ( or is it ) . While the number metrics ( all metrics ) >>>>> exposed by flink cluster is pretty high ( and the names of the metrics >>>>> along with tags ) , it may make sense to limit the number of metrics in a >>>>> single chunk ( to ultimately limit the size of a single chunk ). There is >>>>> this configuration which allows for reducing the metrics in a single chunk >>>>> >>>>> metrics.reporter.dghttp.maxMetricsPerRequest: 2000 >>>>> >>>>> We could decrease this to 1500 ( 1500 is pretty, not based on any >>>>> empirical reasoning ) and see if that stabilizes the dispatch. It is >>>>> inevitable that the number of requests will grow and we may hit the >>>>> throttle but then we know the exception rather than the timeouts that are >>>>> generally less intuitive. >>>>> >>>>> Any thoughts? >>>>> >>>>> >>>>> >>>>> On Mon, Mar 22, 2021 at 10:37 AM Arvid Heise <[email protected]> wrote: >>>>> >>>>>> Hi Vishal, >>>>>> >>>>>> I have no experience in the Flink+DataDog setup but worked a bit with >>>>>> DataDog before. >>>>>> I'd agree that the timeout does not seem like a rate limit. It would >>>>>> also be odd that the other TMs with a similar rate still pass. So I'd >>>>>> suspect n/w issues. >>>>>> Can you log into the TM's machine and try out manually how the system >>>>>> behaves? >>>>>> >>>>>> On Sat, Mar 20, 2021 at 1:44 PM Vishal Santoshi < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> Hello folks, >>>>>>> This is quite strange. We see a TM stop reporting >>>>>>> metrics to DataDog .The logs from that specific TM for every >>>>>>> DataDog dispatch time out with* java.net.SocketTimeoutException: >>>>>>> timeout *and that seems to repeat over every dispatch to DataDog. >>>>>>> It seems it is on a 10 seconds cadence per container. The TM remains >>>>>>> humming, so does not seem to be under memory/CPU distress. And the >>>>>>> exception is *not* transient. It just stops dead and from there on >>>>>>> timeout. >>>>>>> >>>>>>> Looking at SLA provided by DataDog any throttling exception should >>>>>>> pretty much not be a SocketTimeOut, till of course the reporting the >>>>>>> specific issue is off. This thus appears very much a n/w issue which >>>>>>> appears weird as other TMs with the same n/w just hum along, sending >>>>>>> their >>>>>>> metrics successfully. The other issue could be just the amount of >>>>>>> metrics >>>>>>> and the current volume for the TM is prohibitive. That said the >>>>>>> exception >>>>>>> is still not helpful. >>>>>>> >>>>>>> Any ideas from folks who have used DataDog reporter with Flink. I >>>>>>> guess even best practices may be a sufficient beginning. >>>>>>> >>>>>>> Regards. >>>>>>> >>>>>>>
