rangareddy commented on issue #3751:
URL: https://github.com/apache/hudi/issues/3751#issuecomment-5102148156

   This issue was reviewed as part of the JIRA-migrated backlog triage.
   
   Findings: This was a configuration-tuning thread and it largely resolved 
in-thread. Two concrete corrections were made along the way:
   
   - The reason for choosing COW over MOR was Athena support, but Athena has 
supported MOR since 2021-07-16; and if you prefer COW-like query semantics, 
Hive-syncing a MOR table gives you both `foo` and `foo_rt`, with `foo` behaving 
like a read-optimized COW table.
   - The resource shape was the main bottleneck: 19 executors at 
`executor-cores 1` caps effective concurrency at roughly 19-38 tasks, while the 
Spark and Hudi parallelism settings were much higher, so those settings could 
not take effect. Raising cores to 3-5 and aligning `spark.default.parallelism`, 
`spark.sql.shuffle.partitions` and the `hoodie.*.shuffle.parallelism` values is 
what moved the needle. Note also that 
`hoodie.datasource.write.row.writer.enable` applied only to `bulk_insert` at 
the time, so it was not affecting your upsert path.
   
   You reported a reworked, more stable setup on 2022-04-25 after upgrading to 
0.10.0 and moving from `GLOBAL_BLOOM` to `BLOOM`. The follow-up request on 
2022-09-07 for partition count and Spark stage screenshots was never answered, 
and there has been no activity since.
   
   Closing as stale, since no Hudi-side defect was isolated and the report 
predates several rewrites of the write path. For the underlying "many small 
input files" shape, the current recommendation is `bulk_insert` (or `insert`) 
plus scheduled **clustering** rather than paying small-file packing on every 
micro-batch. If write speed is still a problem on a recent version, please open 
a fresh issue with the Hudi version, partition count and Spark UI stage timings.


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