weiqingy commented on code in PR #2424:
URL: https://github.com/apache/auron/pull/2424#discussion_r3653804516
##########
native-engine/datafusion-ext-plans/src/common/row_null_checker.rs:
##########
@@ -52,15 +60,32 @@ impl RowNullChecker {
/// - `false` bits indicate rows that contain at least one null value
/// - `true` bits indicate rows where all fields are non-null
pub fn has_nulls(&self, rows: &Rows) -> NullBuffer {
- // Create NullBuffer from the collected bits
- NullBuffer::from_iter((0..rows.num_rows()).map(|row_index| {
- let row_data = rows.row(row_index);
- // Check if this row has any null values
- let has_null = self.has_null(row_data.as_ref());
- // NullBuffer uses true for valid (non-null) and false for null
- // So we need to invert the result since has_null returns true for
"has nulls"
- !has_null
- }))
+ let parser = self.row_converter.parser();
+ let parsed_rows = rows
+ .iter()
+ .map(|row| parser.parse(row.data()))
+ .collect::<Vec<_>>();
+ let key_columns = self
+ .row_converter
+ .convert_rows(parsed_rows)
+ .expect("failed to decode row data in RowNullChecker");
Review Comment:
`parse()` re-wraps the bytes with *this* converter's `fields`, which is how
they get past `assert!(Arc::ptr_eq(...), "rows were not produced by this
RowConverter")` in `convert_rows` (`arrow-row/src/lib.rs:701-704`). That assert
is what licenses the `unsafe { convert_raw(...) }` just below it (`:710-713`).
One consequence worth knowing before picking a fix for the panic already
raised here: `?` would not fully close it. `RowParser` forces `validate_utf8:
true` (`:844`), and that path runs `GenericStringArray::from(decoded)`
(`arrow-row/src/variable.rs:325`), whose `From` impl is
`try_from_binary(v).unwrap()` (`arrow-array/src/array/string_array.rs:74`). So
bad bytes panic inside Arrow before `convert_rows` can return `Err`.
Would reaching the producer's converter through the stream be an option?
`common/key_rows_output.rs:41-44` exposes only `schema()` and `keys()` today.
With the converter available, `convert_rows(rows.iter())` would work directly,
no `parse()` round-trip, and the assert would be doing real work.
##########
native-engine/datafusion-ext-plans/src/common/row_null_checker.rs:
##########
@@ -52,15 +60,32 @@ impl RowNullChecker {
/// - `false` bits indicate rows that contain at least one null value
/// - `true` bits indicate rows where all fields are non-null
pub fn has_nulls(&self, rows: &Rows) -> NullBuffer {
- // Create NullBuffer from the collected bits
- NullBuffer::from_iter((0..rows.num_rows()).map(|row_index| {
- let row_data = rows.row(row_index);
- // Check if this row has any null values
- let has_null = self.has_null(row_data.as_ref());
- // NullBuffer uses true for valid (non-null) and false for null
- // So we need to invert the result since has_null returns true for
"has nulls"
- !has_null
- }))
+ let parser = self.row_converter.parser();
+ let parsed_rows = rows
+ .iter()
+ .map(|row| parser.parse(row.data()))
+ .collect::<Vec<_>>();
+ let key_columns = self
+ .row_converter
+ .convert_rows(parsed_rows)
+ .expect("failed to decode row data in RowNullChecker");
+
+ NullBuffer::from_iter(
+ (0..rows.num_rows())
+ .map(|row_index| key_columns.iter().all(|column|
!column.is_null(row_index))),
Review Comment:
`Array::is_null` only reads the null buffer
(`arrow-array/src/array/mod.rs:250-252`), and `NullArray::nulls()` returns
`None` (`.../null_array.rs:112-114`). So for a `DataType::Null` key, which is
what Arrow decodes that field to (`arrow-row/src/lib.rs:1548`), `is_null` is
always `false`.
On master such a key is always null (`row_null_checker.rs:240`,
`DataTypeInfo::Null => true`), so after this change the null-skip arms at
`sort_merge_join_exec.rs:382-383` stop firing and rows that never matched
before would take part in the join. Spark's `NullType` does map to Arrow `Null`
(`ArrowUtils.scala:45`), though I could not get Spark to actually plan an SMJ
with one in `on`, so I cannot say it is reachable today.
Would `logical_nulls()` be the safer read here? Roughly this, in case it
helps:
```rust
let logical_nulls: Vec<_> = key_columns.iter().map(|c|
c.logical_nulls()).collect();
NullBuffer::from_iter((0..rows.num_rows()).map(|i| {
logical_nulls.iter().all(|n| n.as_ref().is_none_or(|n| n.is_valid(i)))
}))
```
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