mbutrovich commented on code in PR #4428:
URL: https://github.com/apache/datafusion-comet/pull/4428#discussion_r3326581605


##########
README.md:
##########
@@ -58,17 +60,22 @@ See the [Comet Benchmarking 
Guide](https://datafusion.apache.org/comet/contribut
 
 ## What Comet Accelerates
 
-Comet replaces Spark operators and expressions with native Rust 
implementations that run on Apache DataFusion.
-It uses Apache Arrow for zero-copy data transfer between the JVM and native 
code.
+Comet replaces Spark operators and expressions with implementations that 
consume and produce Apache Arrow
+batches. Most run as native Rust code on top of Apache DataFusion; some run as 
JVM code over Arrow batches.
+Either way the work stays in the Comet pipeline without falling back to 
Spark's row-based engine.
 
 - **Parquet scans**: native Parquet reader integrated with Spark's query 
planner
 - **Apache Iceberg**: accelerated Parquet scans when reading Iceberg tables 
from Spark
   (see the [Iceberg 
guide](https://datafusion.apache.org/comet/user-guide/iceberg.html))
-- **Shuffle**: native columnar shuffle with support for hash and range 
partitioning
+- **Shuffle**: Arrow-IPC columnar shuffle with support for hash and range 
partitioning, in a native Rust
+  implementation paired with a JVM fallback for unsupported partition key types
 - **Expressions**: hundreds of supported Spark expressions across math, 
string, datetime, array,
   map, JSON, hash, and predicate categories
 - **Aggregations**: hash aggregate with support for `FILTER (WHERE ...)` 
clauses
 - **Joins**: hash join, sort-merge join, and broadcast join
+- **Scala/Java UDFs**: experimental support for keeping Scala/Java scalar UDFs 
in the Comet pipeline

Review Comment:
   We can drop "experimental" if #4514 merges first.



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