Dandandan commented on code in PR #2156:
URL: https://github.com/apache/arrow-datafusion/pull/2156#discussion_r845206360
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datafusion/physical-expr/src/expressions/in_list.rs:
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@@ -32,13 +33,19 @@ use arrow::{
record_batch::RecordBatch,
};
-use crate::PhysicalExpr;
+use crate::{expressions, PhysicalExpr};
use arrow::array::*;
use arrow::buffer::{Buffer, MutableBuffer};
use datafusion_common::ScalarValue;
use datafusion_common::{DataFusionError, Result};
use datafusion_expr::ColumnarValue;
+/// Size at which to use a Set rather than Vec for `IN` / `NOT IN`
+/// Value chosen to be consistent with Spark
+///
https://github.com/apache/spark/blob/4e95738fdfc334c25f44689ff8c2db5aa7c726f2/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L259-L265
+/// TODO: add switch codeGen in In_List
+static OPTIMIZER_INSET_THRESHOLD: usize = 10;
Review Comment:
Cool analysis!
I am wondering if other data types make it a bit different, such as strings
/ utf8 arrays? I expect the conversion to be a bit slower there, because of the
extra conversion/allocations needed.
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