r7raul1984 opened a new pull request, #52554:
URL: https://github.com/apache/spark/pull/52554
### What changes were proposed in this pull request?
By pushing down the filter condition in the count_if function, we can reduce
the volume of data that needs to be processed.
```
spark.sql("create table t1(a int, b int, c int) using parquet")
spark.sql("select count_if(a <>1) from t1").explain("cost")
```
Current:
```
== Optimized Logical Plan ==
Aggregate [count(if (NOT _common_expr_0#6) null else _common_expr_0#6) AS
count_if((NOT (a = 1)))#4L], Statistics(sizeInBytes=16.0 B, rowCount=1)
+- Project [NOT (a#0 = 1) AS _common_expr_0#6], Statistics(sizeInBytes=1.0 B)
+- Relation spark_catalog.default.t1[a#0,b#1,c#2] parquet,
Statistics(sizeInBytes=0.0 B)
```
After this pr:
```
== Optimized Logical Plan ==
Aggregate [count(if (NOT _common_expr_2#22) null else _common_expr_2#22) AS
count_if((NOT (a = 1)))#21L], Statistics(sizeInBytes=16.0 B, rowCount=1)
+- Project [NOT (a#3 = 1) AS _common_expr_2#22], Statistics(sizeInBytes=1.0
B)
+- Filter (isnotnull(a#3) AND NOT (a#3 = 1)), Statistics(sizeInBytes=1.0
B)
+- Relation spark_catalog.default.t1[a#3,b#4,c#5] parquet,
Statistics(sizeInBytes=0.0 B)
```
### Why are the changes needed?
Push down the conditional filter expressions in COUNT_IF to improve query
performance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
### Was this patch authored or co-authored using generative AI tooling?
No.
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