feat: Implement basic version of string to float/double/decimal

docs/source/user-guide/compatibility.md

@@ -136,6 +136,9 @@ The following cast operations are not compatible with Spark for all inputs and a
 |-|-|-|
 | integer | decimal  | No overflow check |
 | long | decimal  | No overflow check |
+| string | float  | Does not support inputs ending with 'd' or 'f'. Does not support 'inf'. Does not support ANSI mode. |
+| string | double  | Does not support inputs ending with 'd' or 'f'. Does not support 'inf'. Does not support ANSI mode. |
+| string | decimal  | Does not support inputs ending with 'd' or 'f'. Does not support 'inf'. Does not support ANSI mode. Returns 0.0 instead of null if input contains no digits |
 | string | timestamp  | Not all valid formats are supported |
 | binary | string  | Only works for binary data representing valid UTF-8 strings |
 

native/core/src/execution/datafusion/planner.rs

@@ -382,8 +382,13 @@ impl PhysicalPlanner {
                 let datatype = to_arrow_datatype(expr.datatype.as_ref().unwrap());
                 let timezone = expr.timezone.clone();
                 let eval_mode = from_protobuf_eval_mode(expr.eval_mode)?;
-
-                Ok(Arc::new(Cast::new(child, datatype, eval_mode, timezone)))
+                Ok(Arc::new(Cast::new(
+                    child,
+                    datatype,
+                    eval_mode,
+                    timezone,
+                    expr.allow_incompat,
+                )))
             }
             ExprStruct::Hour(expr) => {
                 let child = self.create_expr(expr.child.as_ref().unwrap(), input_schema)?;
@@ -723,17 +728,20 @@ impl PhysicalPlanner {
                     left,
                     DataType::Decimal256(p1, s1),
                     EvalMode::Legacy,
+                    false,
                 ));
                 let right = Arc::new(Cast::new_without_timezone(
                     right,
                     DataType::Decimal256(p2, s2),
                     EvalMode::Legacy,
+                    false,
                 ));
                 let child = Arc::new(BinaryExpr::new(left, op, right));
                 Ok(Arc::new(Cast::new_without_timezone(
                     child,
                     data_type,
                     EvalMode::Legacy,
+                    false,
                 )))
             }
             (

native/proto/src/proto/expr.proto

@@ -254,8 +254,8 @@ message Cast {
   Expr child = 1;
   DataType datatype = 2;
   string timezone = 3;
-  EvalMode eval_mode = 4; 
-
+  EvalMode eval_mode = 4;
+  bool allow_incompat = 5;
 }
 
 message Equal {

native/spark-expr/benches/cast_from_string.rs

@@ -31,20 +31,23 @@ fn criterion_benchmark(c: &mut Criterion) {
         DataType::Int8,
         EvalMode::Legacy,
         timezone.clone(),
+        false,
     );
     let cast_string_to_i16 = Cast::new(
         expr.clone(),
         DataType::Int16,
         EvalMode::Legacy,
         timezone.clone(),
+        false,
     );
     let cast_string_to_i32 = Cast::new(
         expr.clone(),
         DataType::Int32,
         EvalMode::Legacy,
         timezone.clone(),
+        false,
     );
-    let cast_string_to_i64 = Cast::new(expr, DataType::Int64, EvalMode::Legacy, timezone);
+    let cast_string_to_i64 = Cast::new(expr, DataType::Int64, EvalMode::Legacy, timezone, false);
 
     let mut group = c.benchmark_group("cast_string_to_int");
     group.bench_function("cast_string_to_i8", |b| {

native/spark-expr/benches/cast_numeric.rs

@@ -31,14 +31,16 @@ fn criterion_benchmark(c: &mut Criterion) {
         DataType::Int8,
         EvalMode::Legacy,
         timezone.clone(),
+        false,
     );
     let cast_i32_to_i16 = Cast::new(
         expr.clone(),
         DataType::Int16,
         EvalMode::Legacy,
         timezone.clone(),
+        false,
     );
-    let cast_i32_to_i64 = Cast::new(expr, DataType::Int64, EvalMode::Legacy, timezone);
+    let cast_i32_to_i64 = Cast::new(expr, DataType::Int64, EvalMode::Legacy, timezone, false);
 
     let mut group = c.benchmark_group("cast_int_to_int");
     group.bench_function("cast_i32_to_i8", |b| {

native/spark-expr/src/cast.rs

@@ -142,6 +142,8 @@ pub struct Cast {
     /// When cast from/to timezone related types, we need timezone, which will be resolved with
     /// session local timezone by an analyzer in Spark.
     pub timezone: String,
+
+    pub allow_incompat: bool,
 }
 
 macro_rules! cast_utf8_to_int {
@@ -545,25 +547,29 @@ impl Cast {
         data_type: DataType,
         eval_mode: EvalMode,
         timezone: String,
+        allow_incompat: bool,
     ) -> Self {
         Self {
             child,
             data_type,
             timezone,
             eval_mode,
+            allow_incompat,
         }
     }
 
     pub fn new_without_timezone(
         child: Arc<dyn PhysicalExpr>,
         data_type: DataType,
         eval_mode: EvalMode,
+        allow_incompat: bool,
     ) -> Self {
         Self {
             child,
             data_type,
             timezone: "".to_string(),
             eval_mode,
+            allow_incompat,
         }
     }
 }
@@ -576,21 +582,29 @@ pub fn spark_cast(
     data_type: &DataType,
     eval_mode: EvalMode,
     timezone: String,
+    allow_incompat: bool,
 ) -> DataFusionResult<ColumnarValue> {
     match arg {
         ColumnarValue::Array(array) => Ok(ColumnarValue::Array(cast_array(
             array,
             data_type,
             eval_mode,
             timezone.to_owned(),
+            allow_incompat,
         )?)),
         ColumnarValue::Scalar(scalar) => {
             // Note that normally CAST(scalar) should be fold in Spark JVM side. However, for
             // some cases e.g., scalar subquery, Spark will not fold it, so we need to handle it
             // here.
             let array = scalar.to_array()?;
             let scalar = ScalarValue::try_from_array(
-                &cast_array(array, data_type, eval_mode, timezone.to_owned())?,
+                &cast_array(
+                    array,
+                    data_type,
+                    eval_mode,
+                    timezone.to_owned(),
+                    allow_incompat,
+                )?,
                 0,
             )?;
             Ok(ColumnarValue::Scalar(scalar))
@@ -603,6 +617,7 @@ fn cast_array(
     to_type: &DataType,
     eval_mode: EvalMode,
     timezone: String,
+    allow_incompat: bool,
 ) -> DataFusionResult<ArrayRef> {
     let array = array_with_timezone(array, timezone.clone(), Some(to_type))?;
     let from_type = array.data_type().clone();
@@ -624,6 +639,7 @@ fn cast_array(
                     to_type,
                     eval_mode,
                     timezone,
+                    allow_incompat,
                 )?,
             );
 
@@ -693,7 +709,7 @@ fn cast_array(
         {
             spark_cast_nonintegral_numeric_to_integral(&array, eval_mode, from_type, to_type)
         }
-        _ if is_datafusion_spark_compatible(from_type, to_type) => {
+        _ if is_datafusion_spark_compatible(from_type, to_type, allow_incompat) => {
             // use DataFusion cast only when we know that it is compatible with Spark
             Ok(cast_with_options(&array, to_type, &CAST_OPTIONS)?)
         }
@@ -711,7 +727,11 @@ fn cast_array(
 
 /// Determines if DataFusion supports the given cast in a way that is
 /// compatible with Spark
-fn is_datafusion_spark_compatible(from_type: &DataType, to_type: &DataType) -> bool {
+fn is_datafusion_spark_compatible(
+    from_type: &DataType,
+    to_type: &DataType,
+    allow_incompat: bool,
+) -> bool {
     if from_type == to_type {
         return true;
     }
@@ -764,6 +784,10 @@ fn is_datafusion_spark_compatible(from_type: &DataType, to_type: &DataType) -> b
                 | DataType::Decimal128(_, _)
                 | DataType::Decimal256(_, _)
         ),
+        DataType::Utf8 if allow_incompat => matches!(
+            to_type,
+            DataType::Binary | DataType::Float32 | DataType::Float64 | DataType::Decimal128(_, _)
+        ),
         DataType::Utf8 => matches!(to_type, DataType::Binary),
         DataType::Date32 => matches!(to_type, DataType::Utf8),
         DataType::Timestamp(_, _) => {
@@ -1385,7 +1409,13 @@ impl PhysicalExpr for Cast {
 
     fn evaluate(&self, batch: &RecordBatch) -> DataFusionResult<ColumnarValue> {
         let arg = self.child.evaluate(batch)?;
-        spark_cast(arg, &self.data_type, self.eval_mode, self.timezone.clone())
+        spark_cast(
+            arg,
+            &self.data_type,
+            self.eval_mode,
+            self.timezone.clone(),
+            self.allow_incompat,
+        )
     }
 
     fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
@@ -1402,6 +1432,7 @@ impl PhysicalExpr for Cast {
                 self.data_type.clone(),
                 self.eval_mode,
                 self.timezone.clone(),
+                self.allow_incompat,
             ))),
             _ => internal_err!("Cast should have exactly one child"),
         }
@@ -1413,6 +1444,7 @@ impl PhysicalExpr for Cast {
         self.data_type.hash(&mut s);
         self.timezone.hash(&mut s);
         self.eval_mode.hash(&mut s);
+        self.allow_incompat.hash(&mut s);
         self.hash(&mut s);
     }
 }
@@ -1996,6 +2028,7 @@ mod tests {
             &DataType::Timestamp(TimeUnit::Microsecond, Some(timezone.clone().into())),
             EvalMode::Legacy,
             timezone.clone(),
+            false,
         )?;
         assert_eq!(
             *result.data_type(),
@@ -2205,6 +2238,7 @@ mod tests {
             &DataType::Date32,
             EvalMode::Legacy,
             "UTC".to_owned(),
+            false,
         );
         assert!(result.is_err())
     }
@@ -2217,6 +2251,7 @@ mod tests {
             &DataType::Date32,
             EvalMode::Legacy,
             "Not a valid timezone".to_owned(),
+            false,
         );
         assert!(result.is_err())
     }

spark/src/main/scala/org/apache/comet/expressions/CometCast.scala

@@ -113,10 +113,15 @@ object CometCast {
         Compatible()
       case DataTypes.FloatType | DataTypes.DoubleType =>
         // https://github.com/apache/datafusion-comet/issues/326
-        Unsupported
+        Incompatible(
+          Some(
+            "Does not support inputs ending with 'd' or 'f'. Does not support 'inf'. " +
+              "Does not support ANSI mode."))
       case _: DecimalType =>
         // https://github.com/apache/datafusion-comet/issues/325
-        Unsupported
+        Incompatible(
+          Some("Does not support inputs ending with 'd' or 'f'. Does not support 'inf'. " +
+            "Does not support ANSI mode. Returns 0.0 instead of null if input contains no digits"))
       case DataTypes.DateType =>
         // https://github.com/apache/datafusion-comet/issues/327
         Compatible(Some("Only supports years between 262143 BC and 262142 AD"))

spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala

@@ -792,7 +792,7 @@ object QueryPlanSerde extends Logging with ShimQueryPlanSerde with CometExprShim
         castBuilder.setChild(childExpr.get)
         castBuilder.setDatatype(dataType.get)
         castBuilder.setEvalMode(evalModeToProto(evalMode))
-
+        castBuilder.setAllowIncompat(CometConf.COMET_CAST_ALLOW_INCOMPATIBLE.get())
         val timeZone = timeZoneId.getOrElse("UTC")
         castBuilder.setTimezone(timeZone)
 
@@ -1506,6 +1506,7 @@ object QueryPlanSerde extends Logging with ShimQueryPlanSerde with CometExprShim
                     .setChild(e)
                     .setDatatype(serializeDataType(IntegerType).get)
                     .setEvalMode(ExprOuterClass.EvalMode.LEGACY)
+                    .setAllowIncompat(false)
                     .build())
                 .build()
             })