In Gluten, there is still a gap in supporting all Spark expressions natively (e.g., some JSON functions or Java UDFs). In this case, Gluten will choose the JVM code path to run the expressions, which can introduce performance regressions.
Partial projections, which allow Gluten to minimal data copy between JVM and C++, were added to avoid these performance regressions.
For example, with the expression hash(udf(col0)), col1, col2, col3, col4, partial projection allows us to convert only col0 to row or column to Arrow as input, and convert udf(col0) as an alias partialProject1_. Then, ProjectExecTransformer will handle hash(partialProject1_), col1, col2, col3, col4, partialProject1_. This feature saves the cost of converting the columnar format to row format and vice-versa.
The partial projection feature can also benefit from expressions that are not natively supported. For example, substr(from_json(col_a)). Since from_json is not fully supported, Gluten may use the JVM code path. Instead of projecting the whole expression, partial projection will attempt to project from_json() and perform a native projection of substr().
In the case of blacklisted expressions defined in spark.gluten.expression.blacklist, this feature is also beneficial.
This feature is in the preliminary stages of development and will be improved in future updates.