first draft

Signed-off-by: jayzhan211 <[email protected]>

Author: jayzhan211

datafusion/core/tests/user_defined/user_defined_aggregates.rs

@@ -255,6 +255,20 @@ async fn simple_udaf() -> Result<()> {
     Ok(())
 }
 
+#[tokio::test]
+async fn test_struct_join() {
+    let TestContext { ctx, test_state: _ } = TestContext::new();
+    let sql = "SElECT l.result as result FROM (SELECT first(value, time) as result from t) as l JOIN (SELECT first(value, time) as result from t) as r ON l.result = r.result";
+    let expected = [
+        "+------------------------------------------------+",
+        "| result                                         |",
+        "+------------------------------------------------+",
+        "| {value: 2.0, time: 1970-01-01T00:00:00.000002} |",
+        "+------------------------------------------------+",
+    ];
+    assert_batches_eq!(expected, &execute(&ctx, sql).await.unwrap());
+}
+
 #[tokio::test]
 async fn case_sensitive_identifiers_user_defined_aggregates() -> Result<()> {
     let ctx = SessionContext::new();

datafusion/core/tests/user_defined/user_defined_scalar_functions.rs

@@ -16,7 +16,10 @@
 // under the License.
 
 use arrow::compute::kernels::numeric::add;
-use arrow_array::{Array, ArrayRef, Float64Array, Int32Array, RecordBatch, UInt8Array};
+use arrow_array::{
+    Array, ArrayRef, Float32Array, Float64Array, Int32Array, RecordBatch,
+    UInt8Array,
+};
 use arrow_schema::DataType::Float64;
 use arrow_schema::{DataType, Field, Schema};
 use datafusion::prelude::*;
@@ -26,12 +29,15 @@ use datafusion_common::{
     assert_batches_eq, assert_batches_sorted_eq, cast::as_int32_array, not_impl_err,
     plan_err, DataFusionError, ExprSchema, Result, ScalarValue,
 };
+use datafusion_common::{DFField, DFSchema};
 use datafusion_expr::{
     create_udaf, create_udf, Accumulator, ColumnarValue, ExprSchemable,
-    LogicalPlanBuilder, ScalarUDF, ScalarUDFImpl, Signature, Volatility,
+    LogicalPlanBuilder, ScalarUDF, ScalarUDFImpl, Signature, Simplified, Volatility,
 };
+
 use rand::{thread_rng, Rng};
 use std::any::Any;
+use std::collections::HashMap;
 use std::iter;
 use std::sync::Arc;
 
@@ -498,6 +504,81 @@ async fn test_user_defined_functions_zero_argument() -> Result<()> {
     Ok(())
 }
 
+#[derive(Debug)]
+struct CastToI64UDF {
+    signature: Signature,
+}
+
+impl CastToI64UDF {
+    fn new() -> Self {
+        Self {
+            signature: Signature::any(1, Volatility::Immutable),
+        }
+    }
+}
+
+impl ScalarUDFImpl for CastToI64UDF {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+    fn name(&self) -> &str {
+        "cast_to_i64"
+    }
+    fn signature(&self) -> &Signature {
+        &self.signature
+    }
+    fn return_type(&self, _args: &[DataType]) -> Result<DataType> {
+        Ok(DataType::Int64)
+    }
+    // Wrap with Expr::Cast() to Int64
+    fn simplify(&self, args: Vec<Expr>) -> Result<Simplified> {
+        let dfs = DFSchema::new_with_metadata(
+            vec![DFField::new(Some("t"), "x", DataType::Float32, true)],
+            HashMap::default(),
+        )?;
+        let e = args[0].clone();
+        let casted_expr = e.cast_to(&DataType::Int64, &dfs)?;
+        Ok(Simplified::Rewritten(casted_expr))
+    }
+    fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
+        Ok(args.get(0).unwrap().clone())
+    }
+}
+
+#[tokio::test]
+async fn test_user_defined_functions_cast_to_i64() -> Result<()> {
+    let ctx = SessionContext::new();
+
+    let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Float32, false)]));
+
+    let batch = RecordBatch::try_new(
+        schema,
+        vec![Arc::new(Float32Array::from(vec![1.0, 2.0, 3.0]))],
+    )?;
+
+    ctx.register_batch("t", batch)?;
+
+    let cast_to_i64_udf = ScalarUDF::from(CastToI64UDF::new());
+    ctx.register_udf(cast_to_i64_udf);
+
+    let result = plan_and_collect(&ctx, "SELECT cast_to_i64(x) FROM t").await?;
+
+    assert_batches_eq!(
+        &[
+            "+------------------+",
+            "| cast_to_i64(t.x) |",
+            "+------------------+",
+            "| 1                |",
+            "| 2                |",
+            "| 3                |",
+            "+------------------+"
+        ],
+        &result
+    );
+
+    Ok(())
+}
+
 #[derive(Debug)]
 struct TakeUDF {
     signature: Signature,

datafusion/expr/src/lib.rs

@@ -80,7 +80,7 @@ pub use signature::{
 };
 pub use table_source::{TableProviderFilterPushDown, TableSource, TableType};
 pub use udaf::{AggregateUDF, AggregateUDFImpl};
-pub use udf::{ScalarUDF, ScalarUDFImpl};
+pub use udf::{ScalarUDF, ScalarUDFImpl, Simplified};
 pub use udwf::{WindowUDF, WindowUDFImpl};
 pub use window_frame::{WindowFrame, WindowFrameBound, WindowFrameUnits};
 

datafusion/expr/src/udf.rs

@@ -30,6 +30,15 @@ use std::fmt::Debug;
 use std::fmt::Formatter;
 use std::sync::Arc;
 
+/// Was the expression simplified?
+pub enum Simplified {
+    /// The function call was simplified to an entirely new Expr
+    Rewritten(Expr),
+    /// the function call could not be simplified, and the arguments
+    /// are return unmodified
+    Original(Vec<Expr>),
+}
+
 /// Logical representation of a Scalar User Defined Function.
 ///
 /// A scalar function produces a single row output for each row of input. This
@@ -160,6 +169,10 @@ impl ScalarUDF {
         self.inner.return_type_from_exprs(args, schema)
     }
 
+    pub fn simplify(&self, args: Vec<Expr>) -> Result<Simplified> {
+        self.inner.simplify(args)
+    }
+
     /// Invoke the function on `args`, returning the appropriate result.
     ///
     /// See [`ScalarUDFImpl::invoke`] for more details.
@@ -337,6 +350,10 @@ pub trait ScalarUDFImpl: Debug + Send + Sync {
     fn monotonicity(&self) -> Result<Option<FuncMonotonicity>> {
         Ok(None)
     }
+
+    fn simplify(&self, args: Vec<Expr>) -> Result<Simplified> {
+        Ok(Simplified::Original(args))
+    }
 }
 
 /// ScalarUDF that adds an alias to the underlying function. It is better to

datafusion/physical-expr/src/planner.rs

@@ -258,34 +258,44 @@ pub fn create_physical_expr(
             )))
         }
 
-        Expr::ScalarFunction(ScalarFunction { func_def, args }) => {
-            let physical_args = args
-                .iter()
-                .map(|e| create_physical_expr(e, input_dfschema, execution_props))
-                .collect::<Result<Vec<_>>>()?;
-            match func_def {
-                ScalarFunctionDefinition::BuiltIn(fun) => {
-                    functions::create_physical_expr(
-                        fun,
-                        &physical_args,
-                        input_schema,
-                        execution_props,
-                    )
-                }
-                ScalarFunctionDefinition::UDF(fun) => {
-                    let return_type = fun.return_type_from_exprs(args, input_dfschema)?;
+        Expr::ScalarFunction(ScalarFunction { func_def, args }) => match func_def {
+            ScalarFunctionDefinition::BuiltIn(fun) => {
+                let physical_args = args
+                    .iter()
+                    .map(|e| create_physical_expr(e, input_dfschema, execution_props))
+                    .collect::<Result<Vec<_>>>()?;
 
-                    udf::create_physical_expr(
-                        fun.clone().as_ref(),
-                        &physical_args,
-                        return_type,
-                    )
-                }
-                ScalarFunctionDefinition::Name(_) => {
-                    internal_err!("Function `Expr` with name should be resolved.")
-                }
+                functions::create_physical_expr(
+                    fun,
+                    &physical_args,
+                    input_schema,
+                    execution_props,
+                )
             }
-        }
+            ScalarFunctionDefinition::UDF(fun) => {
+                let args = match fun.simplify(args.to_owned())? {
+                    datafusion_expr::Simplified::Original(args) => args,
+                    datafusion_expr::Simplified::Rewritten(expr) => vec![expr],
+                };
+
+                let physical_args = args
+                    .iter()
+                    .map(|e| create_physical_expr(e, input_dfschema, execution_props))
+                    .collect::<Result<Vec<_>>>()?;
+
+                let return_type =
+                    fun.return_type_from_exprs(args.as_slice(), input_dfschema)?;
+
+                udf::create_physical_expr(
+                    fun.clone().as_ref(),
+                    &physical_args,
+                    return_type,
+                )
+            }
+            ScalarFunctionDefinition::Name(_) => {
+                internal_err!("Function `Expr` with name should be resolved.")
+            }
+        },
         Expr::Between(Between {
             expr,
             negated,