alamb commented on code in PR #4736:
URL: https://github.com/apache/arrow-rs/pull/4736#discussion_r1305888339
##########
arrow-row/src/lib.rs:
##########
@@ -1445,6 +1446,70 @@ unsafe fn decode_column(
Ok(array)
}
+macro_rules! downcast_dict {
+ ($array:ident, $key:ident) => {{
+ $array
+ .as_any()
+ .downcast_ref::<DictionaryArray<$key>>()
+ .unwrap()
+ }};
+}
+
+const LOW_CARDINALITY_THRESHOLD: usize = 10;
+
+#[derive(Debug)]
+pub struct CardinalityAwareRowConverter {
+ inner: RowConverter,
+ done: bool,
+}
+
+impl CardinalityAwareRowConverter {
+ pub fn new(fields: Vec<SortField>) -> Result<Self, ArrowError> {
+ Ok(Self {
+ inner: RowConverter::new(fields)?,
+ done: false,
+ })
+ }
+
+ pub fn size(&self) -> usize {
+ self.inner.size()
+ }
+
+ pub fn convert_rows(&self, rows: &Rows) -> Result<Vec<ArrayRef>,
ArrowError> {
+ self.inner.convert_rows(rows)
+ }
+
+ pub fn convert_columns(
+ &mut self,
+ columns: &[ArrayRef]) -> Result<Rows, ArrowError> {
+ if !self.done {
+ for (i, col) in columns.iter().enumerate() {
+ if let DataType::Dictionary(k, _) = col.data_type() {
+ // let cardinality =
col.as_any().downcast_ref::<DictionaryArray<Int32Type>>().unwrap().values().len();
+ let cardinality = match k.as_ref() {
Review Comment:
I originally thought that we should base the decision of "is this a high
cardinality column" on the "in use" cardinality of the dictionary (aka how may
distinct key values there were -- as suggested on
https://github.com/apache/arrow-datafusion/issues/7200#issuecomment-1690147397)
However, I now realize that maybe the number of potential key values (aka
the length of the values array) is actually a more robust predictor of being
"high cardinality" (as the other values in the dictionary could be used in
subsequent batches, perhaps)
Do you have any opinion @tustvold ?
##########
arrow-row/src/lib.rs:
##########
@@ -1445,6 +1446,70 @@ unsafe fn decode_column(
Ok(array)
}
+macro_rules! downcast_dict {
+ ($array:ident, $key:ident) => {{
+ $array
+ .as_any()
+ .downcast_ref::<DictionaryArray<$key>>()
+ .unwrap()
+ }};
+}
+
+const LOW_CARDINALITY_THRESHOLD: usize = 10;
+
+#[derive(Debug)]
+pub struct CardinalityAwareRowConverter {
+ inner: RowConverter,
+ done: bool,
+}
+
+impl CardinalityAwareRowConverter {
+ pub fn new(fields: Vec<SortField>) -> Result<Self, ArrowError> {
+ Ok(Self {
+ inner: RowConverter::new(fields)?,
+ done: false,
+ })
+ }
+
+ pub fn size(&self) -> usize {
+ self.inner.size()
+ }
+
+ pub fn convert_rows(&self, rows: &Rows) -> Result<Vec<ArrayRef>,
ArrowError> {
+ self.inner.convert_rows(rows)
+ }
+
+ pub fn convert_columns(
+ &mut self,
+ columns: &[ArrayRef]) -> Result<Rows, ArrowError> {
+ if !self.done {
+ for (i, col) in columns.iter().enumerate() {
+ if let DataType::Dictionary(k, _) = col.data_type() {
+ // let cardinality =
col.as_any().downcast_ref::<DictionaryArray<Int32Type>>().unwrap().values().len();
+ let cardinality = match k.as_ref() {
+ DataType::Int8 => downcast_dict!(col,
Int32Type).values().len(),
+ DataType::Int16 => downcast_dict!(col,
Int32Type).values().len(),
+ DataType::Int32 => downcast_dict!(col,
Int32Type).values().len(),
+ DataType::Int64 => downcast_dict!(col,
Int64Type).values().len(),
+ DataType::UInt16 => downcast_dict!(col,
UInt16Type).values().len(),
+ DataType::UInt32 => downcast_dict!(col,
UInt32Type).values().len(),
+ DataType::UInt64 => downcast_dict!(col,
UInt64Type).values().len(),
+ _ => unreachable!(),
+ };
+
+ if cardinality >= LOW_CARDINALITY_THRESHOLD {
Review Comment:
this will effectively switch the encoding mid-steam, I think -- which will
mean that the output can't be compared with previously created rows, which is
not correct.
I think the decision has to be made based on the first batch and then that
decision used for encoding all rows
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