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Fix output schema generated by CommonSubExprEliminate #3726

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merged 4 commits into from
Oct 11, 2022
Merged

Fix output schema generated by CommonSubExprEliminate #3726

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05b0b47
CommonSubexprEliminate: Fix additional col schema
Oct 5, 2022
f930e25
Use correct types in test id_array_visitor
Oct 5, 2022
f1909cf
Re-enable fall back schema for datatype resolution
Oct 5, 2022
ea26e7a
Add comment on fall-back logic using all schemas
Oct 6, 2022
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183 changes: 149 additions & 34 deletions datafusion/optimizer/src/common_subexpr_eliminate.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@

use crate::{OptimizerConfig, OptimizerRule};
use arrow::datatypes::DataType;
use datafusion_common::{DFField, DFSchema, DataFusionError, Result};
use datafusion_common::{DFField, DFSchema, DFSchemaRef, DataFusionError, Result};
use datafusion_expr::{
col,
expr_rewriter::{ExprRewritable, ExprRewriter, RewriteRecursion},
Expand Down Expand Up @@ -94,7 +94,10 @@ fn optimize(
schema,
alias,
}) => {
let arrays = to_arrays(expr, input, &mut expr_set)?;
let input_schema = Arc::clone(input.schema());
let all_schemas: Vec<DFSchemaRef> =
plan.all_schemas().into_iter().cloned().collect();
let arrays = to_arrays(expr, input_schema, all_schemas, &mut expr_set)?;

let (mut new_expr, new_input) = rewrite_expr(
&[expr],
Expand All @@ -112,22 +115,18 @@ fn optimize(
)?))
}
LogicalPlan::Filter(Filter { predicate, input }) => {
let schema = plan.schema().as_ref().clone();
let data_type = if let Ok(data_type) = predicate.get_type(&schema) {
data_type
} else {
// predicate type could not be resolved in schema, fall back to all schemas
let schemas = plan.all_schemas();
let all_schema =
schemas.into_iter().fold(DFSchema::empty(), |mut lhs, rhs| {
lhs.merge(rhs);
lhs
});
predicate.get_type(&all_schema)?
};
let input_schema = Arc::clone(input.schema());
let all_schemas: Vec<DFSchemaRef> =
plan.all_schemas().into_iter().cloned().collect();

let mut id_array = vec![];
expr_to_identifier(predicate, &mut expr_set, &mut id_array, data_type)?;
expr_to_identifier(
predicate,
&mut expr_set,
&mut id_array,
input_schema,
all_schemas,
)?;

let (mut new_expr, new_input) = rewrite_expr(
&[&[predicate.clone()]],
Expand All @@ -153,7 +152,11 @@ fn optimize(
window_expr,
schema,
}) => {
let arrays = to_arrays(window_expr, input, &mut expr_set)?;
let input_schema = Arc::clone(input.schema());
let all_schemas: Vec<DFSchemaRef> =
plan.all_schemas().into_iter().cloned().collect();
let arrays =
to_arrays(window_expr, input_schema, all_schemas, &mut expr_set)?;

let (mut new_expr, new_input) = rewrite_expr(
&[window_expr],
Expand All @@ -175,8 +178,17 @@ fn optimize(
input,
schema,
}) => {
let group_arrays = to_arrays(group_expr, input, &mut expr_set)?;
let aggr_arrays = to_arrays(aggr_expr, input, &mut expr_set)?;
let input_schema = Arc::clone(input.schema());
let all_schemas: Vec<DFSchemaRef> =
plan.all_schemas().into_iter().cloned().collect();
let group_arrays = to_arrays(
group_expr,
Arc::clone(&input_schema),
all_schemas.clone(),
&mut expr_set,
)?;
let aggr_arrays =
to_arrays(aggr_expr, input_schema, all_schemas, &mut expr_set)?;

let (mut new_expr, new_input) = rewrite_expr(
&[group_expr, aggr_expr],
Expand All @@ -197,7 +209,10 @@ fn optimize(
)?))
}
LogicalPlan::Sort(Sort { expr, input, fetch }) => {
let arrays = to_arrays(expr, input, &mut expr_set)?;
let input_schema = Arc::clone(input.schema());
let all_schemas: Vec<DFSchemaRef> =
plan.all_schemas().into_iter().cloned().collect();
let arrays = to_arrays(expr, input_schema, all_schemas, &mut expr_set)?;

let (mut new_expr, new_input) = rewrite_expr(
&[expr],
Expand Down Expand Up @@ -255,14 +270,20 @@ fn pop_expr(new_expr: &mut Vec<Vec<Expr>>) -> Result<Vec<Expr>> {

fn to_arrays(
expr: &[Expr],
input: &LogicalPlan,
input_schema: DFSchemaRef,
all_schemas: Vec<DFSchemaRef>,
expr_set: &mut ExprSet,
) -> Result<Vec<Vec<(usize, String)>>> {
expr.iter()
.map(|e| {
let data_type = e.get_type(input.schema())?;
let mut id_array = vec![];
expr_to_identifier(e, expr_set, &mut id_array, data_type)?;
expr_to_identifier(
e,
expr_set,
&mut id_array,
Arc::clone(&input_schema),
all_schemas.clone(),
)?;

Ok(id_array)
})
Expand Down Expand Up @@ -370,7 +391,15 @@ struct ExprIdentifierVisitor<'a> {
expr_set: &'a mut ExprSet,
/// series number (usize) and identifier.
id_array: &'a mut Vec<(usize, Identifier)>,
data_type: DataType,
/// input schema for the node that we're optimizing, so we can determine the correct datatype
/// for each subexpression
input_schema: DFSchemaRef,
/// all schemas in the logical plan, as a fall back if we cannot resolve an expression type
/// from the input schema alone
// This fallback should never be necessary as the expression datatype should always be
// resolvable from the input schema of the node that's being optimized.
// todo: This can likely be removed if we are sure it's safe to do so.
all_schemas: Vec<DFSchemaRef>,

// inner states
visit_stack: Vec<VisitRecord>,
Expand Down Expand Up @@ -448,7 +477,25 @@ impl ExpressionVisitor for ExprIdentifierVisitor<'_> {

self.id_array[idx] = (self.series_number, desc.clone());
self.visit_stack.push(VisitRecord::ExprItem(desc.clone()));
let data_type = self.data_type.clone();

let data_type = if let Ok(data_type) = expr.get_type(&self.input_schema) {
data_type
} else {
// Expression type could not be resolved in schema, fall back to all schemas.
//
// This fallback should never be necessary as the expression datatype should always be
// resolvable from the input schema of the node that's being optimized.
// todo: This else-branch can likely be removed if we are sure it's safe to do so.
let merged_schema =
self.all_schemas
.iter()
.fold(DFSchema::empty(), |mut lhs, rhs| {
lhs.merge(rhs);
lhs
});
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expr.get_type(&merged_schema)?
};

self.expr_set
.entry(desc)
.or_insert_with(|| (expr.clone(), 0, data_type))
Expand All @@ -462,12 +509,14 @@ fn expr_to_identifier(
expr: &Expr,
expr_set: &mut ExprSet,
id_array: &mut Vec<(usize, Identifier)>,
data_type: DataType,
input_schema: DFSchemaRef,
all_schemas: Vec<DFSchemaRef>,
) -> Result<()> {
expr.accept(ExprIdentifierVisitor {
expr_set,
id_array,
data_type,
input_schema,
all_schemas,
visit_stack: vec![],
node_count: 0,
series_number: 0,
Expand Down Expand Up @@ -577,7 +626,8 @@ fn replace_common_expr(
mod test {
use super::*;
use crate::test::*;
use datafusion_expr::logical_plan::JoinType;
use arrow::datatypes::{Field, Schema};
use datafusion_expr::logical_plan::{table_scan, JoinType};
use datafusion_expr::{
avg, binary_expr, col, lit, logical_plan::builder::LogicalPlanBuilder, sum,
Operator,
Expand All @@ -597,22 +647,36 @@ mod test {
fn id_array_visitor() -> Result<()> {
let expr = binary_expr(
binary_expr(
sum(binary_expr(col("a"), Operator::Plus, lit("1"))),
sum(binary_expr(col("a"), Operator::Plus, lit(1))),
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I agree this test doesn't make sense as coercion should have happened before this pass

Operator::Minus,
avg(col("c")),
),
Operator::Multiply,
lit(2),
);

let schema = Arc::new(DFSchema::new_with_metadata(
vec![
DFField::new(None, "a", DataType::Int64, false),
DFField::new(None, "c", DataType::Int64, false),
],
Default::default(),
)?);

let mut id_array = vec![];
expr_to_identifier(&expr, &mut HashMap::new(), &mut id_array, DataType::Int64)?;
expr_to_identifier(
&expr,
&mut HashMap::new(),
&mut id_array,
Arc::clone(&schema),
vec![schema],
)?;

let expected = vec![
(9, "SUM(a + Utf8(\"1\")) - AVG(c) * Int32(2)Int32(2)SUM(a + Utf8(\"1\")) - AVG(c)AVG(c)cSUM(a + Utf8(\"1\"))a + Utf8(\"1\")Utf8(\"1\")a"),
(7, "SUM(a + Utf8(\"1\")) - AVG(c)AVG(c)cSUM(a + Utf8(\"1\"))a + Utf8(\"1\")Utf8(\"1\")a"),
(4, "SUM(a + Utf8(\"1\"))a + Utf8(\"1\")Utf8(\"1\")a"),
(3, "a + Utf8(\"1\")Utf8(\"1\")a"),
(9, "SUM(a + Int32(1)) - AVG(c) * Int32(2)Int32(2)SUM(a + Int32(1)) - AVG(c)AVG(c)cSUM(a + Int32(1))a + Int32(1)Int32(1)a"),
(7, "SUM(a + Int32(1)) - AVG(c)AVG(c)cSUM(a + Int32(1))a + Int32(1)Int32(1)a"),
(4, "SUM(a + Int32(1))a + Int32(1)Int32(1)a"),
(3, "a + Int32(1)Int32(1)a"),
(1, ""),
(2, ""),
(6, "AVG(c)c"),
Expand Down Expand Up @@ -796,4 +860,55 @@ mod test {
assert!(field_set.insert(field.qualified_name()));
}
}

#[test]
fn eliminated_subexpr_datatype() {
use datafusion_expr::cast;

let schema = Schema::new(vec![
Field::new("a", DataType::UInt64, false),
Field::new("b", DataType::UInt64, false),
Field::new("c", DataType::UInt64, false),
]);

let plan = table_scan(Some("table"), &schema, None)
.unwrap()
.filter(
cast(col("a"), DataType::Int64)
.lt(lit(1_i64))
.and(cast(col("a"), DataType::Int64).not_eq(lit(1_i64))),
)
.unwrap()
.build()
.unwrap();
let rule = CommonSubexprEliminate {};
let optimized_plan = rule.optimize(&plan, &mut OptimizerConfig::new()).unwrap();

let schema = optimized_plan.schema();
let fields_with_datatypes: Vec<_> = schema
.fields()
.iter()
.map(|field| (field.name(), field.data_type()))
.collect();
let formatted_fields_with_datatype = format!("{fields_with_datatypes:#?}");
let expected = r###"[
(
"CAST(table.a AS Int64)table.a",
Int64,
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This used to be just Boolean before the fix.

),
(
"a",
UInt64,
),
(
"b",
UInt64,
),
(
"c",
UInt64,
),
]"###;
assert_eq!(expected, formatted_fields_with_datatype);
}
}