rangareddy commented on issue #3077:
URL: https://github.com/apache/hudi/issues/3077#issuecomment-5103004459

   This issue was reviewed as part of the JIRA-migrated backlog triage.
   
   Findings: This is a tuning thread rather than an identified defect, and the 
workload description points at the answer that was never actually tested. With 
**99%+ inserts and under 1% updates**, `upsert` is the wrong operation -- every 
write pays for index lookup and small-file packing to service a fraction of a 
percent of records. `bulk_insert` for the initial load and `insert` for 
subsequent batches, with **clustering** scheduled separately to consolidate 
file sizes, is the configuration for this shape. MOR was tried and did not 
help, which is expected: MOR optimises *updates*, and there are almost none 
here.
   
   On the specific stage that dominated: its parallelism is the number of 
partitions the batch touches, by construction (`UpsertPartitioner.java:287`), 
so with 5 partitions it will always be a 5-task stage regardless of 
`hoodie.upsert.shuffle.parallelism`. That explains why raising parallelism had 
little effect.
   
   The last round of suggestions (2022-09-13: disable small-file handling, 
enable clustering, raise max file size to ~250MB) was never followed up, and 
there has been no substantive discussion since. Closing as stale -- the report 
is against an 0.8-era write path that has been substantially rewritten, so 
re-measuring it would not be meaningful.
   
   The same two stages are still being discussed on recent versions in #2620, 
which is being kept open as the tracking issue for a performance pass. If 
large-batch upsert latency is still a problem for you on 1.0.x / 1.1.x / 1.2.0, 
please add your numbers there or open a fresh issue with the Hudi version, 
write operation, and Spark UI stage timings.


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