[SUPPORT] Poor Upsert Performance on COW table due to indexing

Hello, I am having performance issues when attempting to upsert data into a Hudi COW table. With the below specs it is taking longer than 4 hours to finish upserting (if it ever does finish). In the screenshots below, you can see that it is taking a long time doing the index scan. I have tried disabling hoodie.bloom.index.prune.by.ranges because our record key is random. I've also tried upserting using the "Simple" index type and did not see any performance improvements. Is there anything else I can do to improve the performance?

**Specs:**
Table Size: 13.6TB (compressed in S3)
Number of partitions: 1135 (hoodie.datasource.hive_sync.partition_fields=year,month)
Upsert dataset size: 68 million records, 6GB compressed
Index type: Default (Bloom)
Number of nodes: 30
Node type: r6g.8xlarge
Average record size: ~40 bytes (calculated by File Size/Num Records: 10MB/250000 records)

**Environment Description**

* Hudi version : 0.9.0
* Spark version : 2.4.8
* EMR version: 5.34.0
* Hive version : 2.38.0
* Hadoop version : Amazon 2.10.1
* Storage (HDFS/S3/GCS..) : S3

The report provides no repository files, tests, or code entry points to inspect. Start by reviewing the reported Hudi, Spark, Hadoop, S3, table-size, partition, index, and workload details, then identify the relevant indexing and upsert entry points; done would require a confirmed performance improvement or a documented configuration-based explanation.

Written by the indexing model from the issue text.