Hi, Andrey Thanks for your response.
I have checked this Jira ticket and I think it can work in standalone mode which TaskManager has been started before scheduling tasks. But we are currently running flink on yarn in per-job cluster mode. I noticed that this issue has already been raised. I will keep watching this ticket. Thanks again. Best Weihua Hu > 2020年1月15日 17:53，Andrey Zagrebin <azagre...@apache.org> 写道： > > HI HuWeihua, > > I think your issue should resolve with 1.9.2 and 1.10 (not released but in > progress). > You can check the related Jira ticket [1]. > > Best, > Andrey > > [1] https://jira.apache.org/jira/browse/FLINK-12122 > <https://jira.apache.org/jira/browse/FLINK-12122> > On Wed, Jan 15, 2020 at 10:08 AM HuWeihua <huweihua....@gmail.com > <mailto:huweihua....@gmail.com>> wrote: > Hi, All > We encountered some problems during the upgrade from Flink 1.5 to Flink 1.9. > Flink's scheduling strategy has changed. Flink 1.9 prefers centralized > scheduling, while Flink 1.5 prefers decentralized scheduling. This change has > caused resources imbalance and blocked our upgrade plan. We have thousands of > jobs that need to be upgraded. > > For example, > There is a job with 10 sources and 100 sinks. Each source need 1 core and > each sink need 0.1 core. > Try to run this job on Yarn, configure the numberOfTaskSlots is 10, > yarn.containers.vcores is 2. > > When using Flink-1.5: > Each TaskManager will run 1 source and 9 sinks, they need 1.9 cores totally. > So the job with this configuration works very well. The schedule results is > shown in Figure 1. > When using Flink-1.9: > The 10 sources will be scheduled to one TaskManager and the 100 sinks will > scheduled to other 10 TaskManagers. The schedule results is shown in Figure > 2. > In this scenario, the TaskManager which run sources need 10 cores, other > TaskManagers need 1 cores. But TaskManager must be configured the same, So we > need 11 TaskManager with 10 cores. > This situation waste (10-2)*11 = 88 cores more than Flink 1.5. > > In addition to the waste of resources, we also encountered other problems > caused by centralized scheduling strategy. > Network bandwidth. Tasks of the same type are scheduled to the one > TaskManager, causing too much network traffic on the machine. > Some jobs need to sink to the local agent. After centralized scheduling, the > insufficient processing capacity of the single machine causes a backlog of > consumption. > > In summary, we think a decentralized scheduling strategy is necessary. > > > Figure 1. Flink 1.5 schedule results > <粘贴的图形-3.tiff> > > Figure 2. Flink 1.9 schedule results > <粘贴的图形-4.tiff> > > > > Best > Weihua Hu >
