xushiyan commented on code in PR #5392:
URL: https://github.com/apache/hudi/pull/5392#discussion_r878870330


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rfc/rfc-50/rfc-50.md:
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+
+# RFC-50: Improve Timeline Server
+
+## Proposers
+- @yuzhaojing
+
+## Approvers
+ - @xushiyan
+ - @danny0405
+
+## Abstract
+
+Support client to obtain timeline from timeline server.
+
+## Background
+
+At its core, Hudi maintains a timeline of all actions performed on the table 
at different instants of time. Before each operation is performed on the Hoodie 
table, the information of the HUDI table needs to be obtained through the 
timeline. At present, there are two ways to obtain the timeline of HUDI :
+- Create a MetaClient and get the complete timeline through MetaClient 
#getActiveTimeline, which will directly scan the HDFS directory of metadata
+- Get the timeline through FileSystemView#getTimeline. This timeline is the 
cache timeline obtained by requesting the Embedded timeline service. There is 
no need to repeatedly scan the HDFS directory of metadata, but this timeline 
only contains completed instants
+
+### Problem description
+
+- HUDI designs the Timeline service for processing and caching when accessing 
metadata , but currently does not converge all access to metadata to the 
Timeline service, such as the acquisition of a complete timeline.
+- When the number of tasks written increases, a large number of repeated 
access to metadata will lead to high HDFS NameNode requests, causing greater 
pressure and not easy to expand.
+
+### Spark and Flink write flow comparison diagram
+
+Since Hudi is designed based on the Spark micro-batch model, in the Spark 
write process, all operations on the timeline are completed on the driver side, 
and then distributed to the executor side to start the write operation.
+
+But for Flink , Write tasks are resident services due to their pure streaming 
model. There is also no highly reliable communication mechanism between the 
user-side JM and the TM in Flink, so the TM needs to obtain the latest instant 
by polling the timeline for writing.
+
+![](ComparisonDiagram.png)
+
+### Current
+
+![](CurrentDesign.png)
+
+The current design implementation has two main problems with the convergence 
timeline
+- Since the timeline of the task is pulled from the Embedded timeline service, 
the refresh mechanism of the Embedded timeline service itself will doesn't work
+- MetaClient and HoodieTable are decoupled. Obtain the timeline in MetaClient 
and then request the Embedded timeline service to obtain file-related 
information through the FileSystemViewManager in HoodieTable combined with the 
timeline. There are circular dependencies and problems in the case of using 
MetaClient alone without creating HoodieTable
+
+## Implementation
+
+### Design target
+
+The goal of this solution is to converge the acquisition of timelines and 
obtain them through the Embedded timeline service uniformly. The timeline is 
pulled through HDFS only when the Embedded timeline service is not started.
+
+### Converge the request to loop instant in Flink to JM
+
+- Store the latest instant on the Embedded Timeline Server. Every time JM 
modifies the instant state, it actively performs a sync to Embedded Timeline 
Server
+- Return the latest instant directly when the task pulls the latest instant
+
+### Converge the request to pull instant in meta  client initialization to JM
+
+- Abstract the timeline-related acquisition methods into the new interface 
TableTimelineView, and create the corresponding TimelineViewManager in 
MetaClient, and obtain the timeline through TimelineViewManager.

Review Comment:
   ok sounds fair



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