Hi Zhu, Thanks for your feedback.
Following your suggestion, I have updated the public interface section of the FLIP with the following additions: 1. UI: The job topology will display a hybrid of the current JobGraph along with downstream components yet to be converted to a StreamGraph. On the topology graph display page, there will be a "Show Pending Operators" button in the upper right corner for users to switch back to a job topology that only includes JobVertices. 2. Rest API: Add a new field "stream-graph-plan" will be added to the job details REST API, which represents the runtime Stream graph. The field "job-vertex-id" is valid only when the StreamNode has been converted to a JobVertex, and it will hold the ID of the corresponding JobVertex for that StreamNode. For further information, please feel free to review the public interface section of FLIP-469 Best, Junrui Zhu Zhu <reed...@gmail.com> 于2024年7月15日周一 10:29写道： > +1 for the FLIP > > It is useful to adaptively optimize logical execution plans(stream > operators and > stream edges) for batch jobs. > > One question: > The FLIP already proposed to update the REST API & Web UI to show operators > that are not yet converted to job vertices. However, I think it would be > better if Flink can display these operators as part of the graph, allowing > users to have an overview of the processing logic graph at early stages of > the job execution. > This change would also involve the public interface, so instead of > postponing > it to a later FLIP, I prefer to have a design for it in this FLIP. WDYT? > > Thanks, > Zhu > > Junrui Lee <jrlee....@gmail.com> 于2024年7月11日周四 11:27写道： > > > Hi devs, > > > > Xia Sun, Lei Yang, and I would like to initiate a discussion about > > FLIP-469: Supports Adaptive Optimization of StreamGraph. > > > > This FLIP is the second in the series on adaptive optimization of > > StreamGraph and follows up on FLIP-468 [1]. As we proposed in FLIP-468 to > > enable the scheduler to recognize and access the StreamGraph, in this > FLIP, > > we will propose a mechanism for adaptive optimization of StreamGraph. It > > allows the scheduler to dynamically adjust the logical execution plan at > > runtime. This mechanism is the base of adaptive optimization strategies, > > such as adaptive broadcast join and skewed join optimization. > > > > Unlike the traditional execution of jobs based on a static StreamGraph, > > this mechanism will progressively determine StreamGraph during runtime. > The > > determined StreamGraph will be transformed into a specific JobGraph, > while > > the indeterminate part will allow Flink to flexibly adjust according to > > real-time job status and actual input conditions. > > > > Note that this FLIP focuses on the introduction of the mechanism and does > > not introduce any actual optimization strategies; these will be discussed > > in subsequent FLIPs. > > > > For more details, please refer to FLIP-469 [2]. We look forward to your > > feedback. > > > > Best, > > > > Xia Sun, Lei Yang and Junrui Lee > > > > [1] > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-468%3A+Introducing+StreamGraph-Based+Job+Submission > > [2] > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-469%3A+Supports+Adaptive+Optimization+of+StreamGraph > > >
