Hi Siew,
The hint about the lower source parallelism compared to the operator
parallelism might be the right one.
Can you check if all source tasks are scheduled to the same machine?
In that case your application might be bottlenecked by the out-going
network connection of that single machine.
Best, Fabian
2018-06-18 17:29 GMT+02:00 Siew Wai Yow <wai_...@hotmail.com>:
> Thanks @Fabian for your confirmation about the better performance when
> scaling happened at same TM machine. But it is so funny that it give
> impression "the more I scale the less I get" when the performance drop with
> more TM in play.
>
> @Ovidiu question is interesting to know too. @Till do you mind to share
> your thoughts?
>
> Thank you guys!
>
> ------------------------------
> *From:* Ovidiu-Cristian MARCU <ovidiu-cristian.ma...@inria.fr>
> *Sent:* Monday, June 18, 2018 6:28 PM
> *To:* Fabian Hueske
> *Cc:* Siew Wai Yow; Jörn Franke; user@flink.apache.org
> *Subject:* Re: Flink application does not scale as expected, please help!
>
> Hi all,
>
> Allow me to add some comments/questions on this issue that is very
> interesting.
> According to documentation [1] the pipeline example assumes the source is
> running with the same parallelism as successive map operator and the
> workflow optimizes to collocate source and map tasks if possible.
>
> For an application configuring the source with different parallelism,
> assuming N task managers each with m slots, if I configure
> the source operator with parallelism m, then all of the source's tasks
> could be scheduled on the first task manager?
> I think the same story holds for sinks tasks.
> So, in general is there any control over scheduling of source and sink
> tasks?
> Would it be possible to enforce scheduling of source tasks to be balanced
> across task managers? Not sure if this is the default.
> If the source creates a non-keyed stream, can we enforce the source to
> push records to local map tasks?
>
> For Siew’s example, after source#map a keyBy complicates further things
> since each key can be possibly processed on another task manager.
> At least the keyBy operator should run with the same parallelism as source
> and map and be pipelined on same slot (maybe shared slot configuration
> could enforce that).
>
> *DataStream<Record> AggregatedRecordWithAuditStream = sourceStringStream*
> *.map(new JsonToRecordTranslator(markerFactory.getMarker(),
> inputlink)).name("JsonRecTranslator").setParallelism(pJ2R) *
> *.keyBy(new KeySelector<Record, String>() {*
> *private static final long serialVersionUID = 1L;*
> *@Override*
> *public String getKey(Record r) throws Exception {*
> *return r.getUNIQUE_KEY(); *
> *}*
> *}) *
> *.process(new ProcessAggregation(aggrDuration, markerFactory.getMarker(),
> markerFactory.getMarker())).setParallelism(pAggr)*
> *.name("AggregationDuration: " + aggrDuration +"ms");*
>
>
> Thanks,
> Ovidiu
>
> [1] https://ci.apache.org/projects/flink/flink-docs-
> release-1.5/internals/job_scheduling.html
>
> <https://ci.apache.org/projects/flink/flink-docs-release-1.5/internals/job_scheduling.html>
> Apache Flink 1.5 Documentation: Jobs and Scheduling
> <https://ci.apache.org/projects/flink/flink-docs-release-1.5/internals/job_scheduling.html>
> ci.apache.org
> Execution resources in Flink are defined through Task Slots. Each
> TaskManager will have one or more task slots, each of which can run one
> pipeline of parallel tasks. A pipeline consists of multiple successive
> tasks, such as the n-th parallel instance of a MapFunction together with
> the n-th parallel ...
>
> On 18 Jun 2018, at 10:05, Fabian Hueske <fhue...@gmail.com> wrote:
>
> Not sure if TM local assignment is explicitly designed in 1.5.0, but it
> might be an artifact of how slots are registered in the resource manager.
> Till (in CC) should know how that works.
>
> Anyway, tasks that run in the same TM exchange data via in-memory channels
> which is of course much faster than going over the network.
> So yes, a performance drop when tasks are scheduled to different TMs is
> not unexpected IMO.
> You can check that by starting multiple TMs with a single slot each and
> running you job on that setup.
>
> Best, Fabian
>
>
>
> 2018-06-18 9:57 GMT+02:00 Siew Wai Yow <wai_...@hotmail.com>:
>
> Hi Fabian,
>
> We are using Flink 1.5.0. Any different in scheduler in Flink 1.5.0?
>
> *"Hence, applications might scale better until tasks are scheduled to
> different machines."*
>
> This seems the case. We have 32 vCPU 16 slots in one TM machine. So the
> scaling work perfectly 1-2-4-8-16 because all happens in same TM. When
> scale to 32 the performance drop, not even in par with case of parallelism
> 16. Is this something expected? Thank you.
>
> Regards,
> Yow
>
> ------------------------------
> *From:* Fabian Hueske <fhue...@gmail.com>
> *Sent:* Monday, June 18, 2018 3:47 PM
> *To:* Siew Wai Yow
> *Cc:* Jörn Franke; user@flink.apache.org
>
> *Subject:* Re: Flink application does not scale as expected, please help!
>
> Hi,
>
> Which Flink version are you using?
> Did you try to analyze the bottleneck of the application, i.e., is it CPU,
> disk IO, or network bound?
>
> Regarding the task scheduling. AFAIK, before version 1.5.0, Flink tried to
> schedule tasks on the same machine to reduce the amount of network transfer.
> Hence, applications might scale better until tasks are scheduled to
> different machines.
>
> Fabian
>
> 2018-06-16 12:20 GMT+02:00 Siew Wai Yow <wai_...@hotmail.com>:
>
> Hi Jorn, Please find the source @https://github.com/swyow/flink_sample_git
> Thank you!
>
> ------------------------------
> *From:* Jörn Franke <jornfra...@gmail.com>
> *Sent:* Saturday, June 16, 2018 6:03 PM
>
> *To:* Siew Wai Yow
> *Cc:* user@flink.apache.org
> *Subject:* Re: Flink application does not scale as expected, please help!
>
> Can you share the app source on gitlab, github or bitbucket etc?
>
> On 16. Jun 2018, at 11:46, Siew Wai Yow <wai_...@hotmail.com> wrote:
>
> Hi, There is an interesting finding, the reason of low parallelism work
> much better is because all task being run in same TM, once we scale more,
> the task is distributed to different TM and the performance worse than the
> low parallelism case. Is this something expected? The more I scale the less
> I get?
>
> ------------------------------
> *From:* Siew Wai Yow <wai_...@hotmail.com>
> *Sent:* Saturday, June 16, 2018 5:09 PM
> *To:* Jörn Franke
> *Cc:* user@flink.apache.org
> *Subject:* Re: Flink application does not scale as expected, please help!
>
> Hi Jorn, the input data is 1kb per record, in production it will have 10
> billions of record per day and it will be increased so scalability is quite
> important to us to handle more data. Unfortunately this is not work as
> expected even with only 10 millions of testing data. The test application
> is just a simple jackson map + an empty process. CPU and memory is not an
> issue as we have 32 vCPU + 100 GB RAM per TM. Network should be fine as
> well as total TX+RX peak is around 800Mbps while we have 1000Mbps. Do you
> mind to share your thought? Or mind to test the attach application in your
> lab?
>
>
> To run the program, sample parameters,
>
> "aggrinterval=6000000 loop=7500000 statsd=1 psrc=4 pJ2R=32 pAggr=72 URL=
> do36.mycompany.com:8127"
>
> - aggrinterval: time in ms for timer to trigger
> - loop: how many row of data to feed
> - statsd: to send result to statsd
> - psrc: source parallelism
> - pJ2R: parallelism of map operator(JsonRecTranslator)
> - pAggr: parallelism of process+timer operator(AggregationDuration)
>
>
>
> Thank you!
> Yow
>
> ------------------------------
> *From:* Jörn Franke <jornfra...@gmail.com>
> *Sent:* Saturday, June 16, 2018 4:46 PM
> *To:* Siew Wai Yow
> *Cc:* user@flink.apache.org
> *Subject:* Re: Flink application does not scale as expected, please help!
>
> How large is the input data? If the input data is very small then it does
> not make sense to scale it even more. The larger the data is the more
> parallelism you will have. You can modify this behavior of course by
> changing the partition on the Dataset.
>
> On 16. Jun 2018, at 10:41, Siew Wai Yow <wai_...@hotmail.com> wrote:
>
> Hi,
>
>
> We found that our Flink application with simple logic, which using process
> function is not scale-able when scale from 8 parallelism onward even though
> with sufficient resources. Below it the result which is capped at ~250k
> TPS. No matter how we tune the parallelism of the operators it just not
> scale, same to increase source parallelism.
>
>
> Please refer to "scaleNotWork.png",
> 1. fixed source parallelism 4, other operators parallelism 8
> 2. fixed source parallelism 4, other operators parallelism 16
> 3. fixed source parallelism 4, other operators parallelism 32
> 4. fixed source parallelism 6, other operators parallelism 8
> 5. fixed source parallelism 6, other operators parallelism 16
> 6. fixed source parallelism 6, other operators parallelism 32
> 7. fixed source parallelism 6, other operators parallelism 64 performance
> worse than parallelism 32.
>
>
> Sample source code attached(flink_app_parser_git.zip). It is a simple
> program, parsing json record into object, and pass it to a empty logic
> Flink's process function. Rocksdb is in used, and the source is generated
> by the program itself. This could be reproduce easily.
>
>
> We choose Flink because of it scalability, but this is not the case now,
> appreciated if anyone could help as this is impacting our projects! thank
> you.
>
>
> To run the program, sample parameters,
>
> "aggrinterval=6000000 loop=7500000 statsd=1 psrc=4 pJ2R=32 pAggr=72 URL=
> do36.mycompany.com:8127"
>
> - aggrinterval: time in ms for timer to trigger
> - loop: how many row of data to feed
> - statsd: to send result to statsd
> - psrc: source parallelism
> - pJ2R: parallelism of map operator(JsonRecTranslator)
> - pAggr: parallelism of process+timer operator(AggregationDuration)
>
> We are running in VMWare, 5 Task Managers and each has 32 slots.
>
>
> Architecture: x86_64
> CPU op-mode(s): 32-bit, 64-bit
> Byte Order: Little Endian
> CPU(s): 32
> On-line CPU(s) list: 0-31
> Thread(s) per core: 1
> Core(s) per socket: 1
> Socket(s): 32
> NUMA node(s): 1
> Vendor ID: GenuineIntel
> CPU family: 6
> Model: 63
> Model name: Intel(R) Xeon(R) CPU E5-2640 v3 @ 2.60GHz
> Stepping: 2
> CPU MHz: 2593.993
> BogoMIPS: 5187.98
> Hypervisor vendor: VMware
> Virtualization type: full
> L1d cache: 32K
> L1i cache: 32K
> L2 cache: 256K
> L3 cache: 20480K
> NUMA node0 CPU(s): 0-31
> Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat
> pse36 clflush dts mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm
> constant_tsc arch_perfmon pebs bts nopl xtopology tsc_reliable nonstop_tsc
> aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe
> popcnt aes xsave avx f16c rdrand hypervisor lahf_lm epb fsgsbase smep
> dtherm ida arat pln pts
>
> total used free shared buff/cache available
>
>
> Mem: 98 24 72 0 1 72
> Swap: 3 0 3
>
>
> Please refer TM.png and JM.png for further details.
> The test without any checkpoint enable.
>
>
> Thanks.
>
> Regards,
> Yow
>
> <flink_app_parser_git.zip>
>
> <JM.png>
>
> <sample.png>
>
> <scaleNotWork.png>
>
> <TM.png>
>
>
>
>
>
