feat: Support ANSI mode in CAST from String to Bool

common/src/main/scala/org/apache/comet/CometConf.scala

@@ -357,6 +357,14 @@ object CometConf {
       .toSequence
       .createWithDefault(Seq("Range,InMemoryTableScan"))
 
+  val COMET_ANSI_MODE_ENABLED: ConfigEntry[Boolean] = conf("spark.comet.ansi.enabled")
+    .doc(
+      "Comet does not respect ANSI mode in most cases and by default will not accelerate " +
+        "queries when ansi mode is enabled. Enable this setting to test Comet's experimental " +
+        "support for ANSI mode. This should not be used in production.")
+    .booleanConf
+    .createWithDefault(false)
+
 }
 
 object ConfigHelpers {

core/src/errors.rs

@@ -60,6 +60,18 @@ pub enum CometError {
     #[error("Comet Internal Error: {0}")]
     Internal(String),
 
+    // TODO this error message is likely to change between Spark versions and it would be better
+    // to have the full error in Scala and just pass the invalid value back here
+    #[error("[[CAST_INVALID_INPUT] The value '{value}' of the type \"{from_type}\" cannot be cast to \"{to_type}\" \
+        because it is malformed. Correct the value as per the syntax, or change its target type. \
+        Use `try_cast` to tolerate malformed input and return NULL instead. If necessary \
+        set \"spark.sql.ansi.enabled\" to \"false\" to bypass this error")]
+    CastInvalidValue {
+        value: String,
+        from_type: String,
+        to_type: String,
+    },
+
     #[error(transparent)]
     Arrow {
         #[from]
@@ -183,6 +195,10 @@ impl jni::errors::ToException for CometError {
                 class: "java/lang/NullPointerException".to_string(),
                 msg: self.to_string(),
             },
+            CometError::CastInvalidValue { .. } => Exception {
+                class: "org/apache/spark/SparkException".to_string(),
+                msg: self.to_string(),
+            },
             CometError::NumberIntFormat { source: s } => Exception {
                 class: "java/lang/NumberFormatException".to_string(),
                 msg: s.to_string(),

core/src/execution/datafusion/expressions/cast.rs

@@ -22,6 +22,7 @@ use std::{
     sync::Arc,
 };
 
+use crate::errors::{CometError, CometResult};
 use arrow::{
     compute::{cast_with_options, CastOptions},
     record_batch::RecordBatch,
@@ -49,26 +50,38 @@ static CAST_OPTIONS: CastOptions = CastOptions {
 pub struct Cast {
     pub child: Arc<dyn PhysicalExpr>,
     pub data_type: DataType,
+    pub ansi_mode: bool,
 
     /// 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,
 }
 
 impl Cast {
-    pub fn new(child: Arc<dyn PhysicalExpr>, data_type: DataType, timezone: String) -> Self {
+    pub fn new(
+        child: Arc<dyn PhysicalExpr>,
+        data_type: DataType,
+        ansi_mode: bool,
+        timezone: String,
+    ) -> Self {
         Self {
             child,
             data_type,
             timezone,
+            ansi_mode,
         }
     }
 
-    pub fn new_without_timezone(child: Arc<dyn PhysicalExpr>, data_type: DataType) -> Self {
+    pub fn new_without_timezone(
+        child: Arc<dyn PhysicalExpr>,
+        data_type: DataType,
+        ansi_mode: bool,
+    ) -> Self {
         Self {
             child,
             data_type,
             timezone: "".to_string(),
+            ansi_mode,
         }
     }
 
@@ -77,17 +90,22 @@ impl Cast {
         let array = array_with_timezone(array, self.timezone.clone(), Some(to_type));
         let from_type = array.data_type();
         let cast_result = match (from_type, to_type) {
-            (DataType::Utf8, DataType::Boolean) => Self::spark_cast_utf8_to_boolean::<i32>(&array),
+            (DataType::Utf8, DataType::Boolean) => {
+                Self::spark_cast_utf8_to_boolean::<i32>(&array, self.ansi_mode)?
+            }
             (DataType::LargeUtf8, DataType::Boolean) => {
-                Self::spark_cast_utf8_to_boolean::<i64>(&array)
+                Self::spark_cast_utf8_to_boolean::<i64>(&array, self.ansi_mode)?
             }
             _ => cast_with_options(&array, to_type, &CAST_OPTIONS)?,
         };
         let result = spark_cast(cast_result, from_type, to_type);
         Ok(result)
     }
 
-    fn spark_cast_utf8_to_boolean<OffsetSize>(from: &dyn Array) -> ArrayRef
+    fn spark_cast_utf8_to_boolean<OffsetSize>(
+        from: &dyn Array,
+        ansi_mode: bool,
+    ) -> CometResult<ArrayRef>
     where
         OffsetSize: OffsetSizeTrait,
     {
@@ -100,15 +118,22 @@ impl Cast {
             .iter()
             .map(|value| match value {
                 Some(value) => match value.to_ascii_lowercase().trim() {
-                    "t" | "true" | "y" | "yes" | "1" => Some(true),
-                    "f" | "false" | "n" | "no" | "0" => Some(false),
-                    _ => None,
+                    "t" | "true" | "y" | "yes" | "1" => Ok(Some(true)),
+                    "f" | "false" | "n" | "no" | "0" => Ok(Some(false)),
+                    other if ansi_mode => {
+                        Err(CometError::CastInvalidValue {
+                            value: other.to_string(),
+                            from_type: "STRING".to_string(),
+                            to_type: "BOOLEAN".to_string(),
+                        })
+                    }
+                    _ => Ok(None),
                 },
-                _ => None,
+                _ => Ok(None),
             })
-            .collect::<BooleanArray>();
+            .collect::<Result<BooleanArray, _>>()?;
 
-        Arc::new(output_array)
+        Ok(Arc::new(output_array))
     }
 }
 
@@ -174,6 +199,7 @@ impl PhysicalExpr for Cast {
         Ok(Arc::new(Cast::new(
             children[0].clone(),
             self.data_type.clone(),
+            self.ansi_mode,
             self.timezone.clone(),
         )))
     }

core/src/execution/datafusion/planner.rs

@@ -112,27 +112,17 @@ pub struct PhysicalPlanner {
     exec_context_id: i64,
     execution_props: ExecutionProps,
     session_ctx: Arc<SessionContext>,
-}
-
-impl Default for PhysicalPlanner {
-    fn default() -> Self {
-        let session_ctx = Arc::new(SessionContext::new());
-        let execution_props = ExecutionProps::new();
-        Self {
-            exec_context_id: TEST_EXEC_CONTEXT_ID,
-            execution_props,
-            session_ctx,
-        }
-    }
+    ansi_mode: bool,
 }
 
 impl PhysicalPlanner {
-    pub fn new(session_ctx: Arc<SessionContext>) -> Self {
+    pub fn new(session_ctx: Arc<SessionContext>, ansi_mode: bool) -> Self {
         let execution_props = ExecutionProps::new();
         Self {
             exec_context_id: TEST_EXEC_CONTEXT_ID,
             execution_props,
             session_ctx,
+            ansi_mode,
         }
     }
 
@@ -141,6 +131,7 @@ impl PhysicalPlanner {
             exec_context_id,
             execution_props: self.execution_props,
             session_ctx: self.session_ctx.clone(),
+            ansi_mode: self.ansi_mode,
         }
     }
 
@@ -343,7 +334,12 @@ impl PhysicalPlanner {
                 let child = self.create_expr(expr.child.as_ref().unwrap(), input_schema)?;
                 let datatype = to_arrow_datatype(expr.datatype.as_ref().unwrap());
                 let timezone = expr.timezone.clone();
-                Ok(Arc::new(Cast::new(child, datatype, timezone)))
+                Ok(Arc::new(Cast::new(
+                    child,
+                    datatype,
+                    self.ansi_mode,
+                    timezone,
+                )))
             }
             ExprStruct::Hour(expr) => {
                 let child = self.create_expr(expr.child.as_ref().unwrap(), input_schema)?;
@@ -638,13 +634,19 @@ impl PhysicalPlanner {
                 let left = Arc::new(Cast::new_without_timezone(
                     left,
                     DataType::Decimal256(p1, s1),
+                    self.ansi_mode,
                 ));
                 let right = Arc::new(Cast::new_without_timezone(
                     right,
                     DataType::Decimal256(p2, s2),
+                    self.ansi_mode,
                 ));
                 let child = Arc::new(BinaryExpr::new(left, op, right));
-                Ok(Arc::new(Cast::new_without_timezone(child, data_type)))
+                Ok(Arc::new(Cast::new_without_timezone(
+                    child,
+                    data_type,
+                    self.ansi_mode,
+                )))
             }
             (
                 DataFusionOperator::Divide,
@@ -1432,7 +1434,9 @@ mod tests {
 
     use arrow_array::{DictionaryArray, Int32Array, StringArray};
     use arrow_schema::DataType;
-    use datafusion::{physical_plan::common::collect, prelude::SessionContext};
+    use datafusion::{
+        execution::context::ExecutionProps, physical_plan::common::collect, prelude::SessionContext,
+    };
     use tokio::sync::mpsc;
 
     use crate::execution::{
@@ -1442,9 +1446,23 @@ mod tests {
         spark_operator,
     };
 
+    use crate::execution::datafusion::planner::TEST_EXEC_CONTEXT_ID;
     use spark_expression::expr::ExprStruct::*;
     use spark_operator::{operator::OpStruct, Operator};
 
+    impl Default for PhysicalPlanner {
+        fn default() -> Self {
+            let session_ctx = Arc::new(SessionContext::new());
+            let execution_props = ExecutionProps::new();
+            Self {
+                exec_context_id: TEST_EXEC_CONTEXT_ID,
+                execution_props,
+                session_ctx,
+                ansi_mode: false,
+            }
+        }
+    }
+
     #[test]
     fn test_unpack_dictionary_primitive() {
         let op_scan = Operator {

core/src/execution/jni_api.rs

@@ -317,11 +317,14 @@ pub unsafe extern "system" fn Java_org_apache_comet_Native_executePlan(
 
         let exec_context_id = exec_context.id;
 
+        let ansi_mode =
+            matches!(exec_context.conf.get("ansi_mode"), Some(value) if value == "true");
+
         // Initialize the execution stream.
         // Because we don't know if input arrays are dictionary-encoded when we create
         // query plan, we need to defer stream initialization to first time execution.
         if exec_context.root_op.is_none() {
-            let planner = PhysicalPlanner::new(exec_context.session_ctx.clone())
+            let planner = PhysicalPlanner::new(exec_context.session_ctx.clone(), ansi_mode)
                 .with_exec_id(exec_context_id);
             let (scans, root_op) = planner.create_plan(
                 &exec_context.spark_plan,

spark/src/main/scala/org/apache/comet/CometExecIterator.scala

@@ -20,7 +20,9 @@
 package org.apache.comet
 
 import org.apache.spark._
+import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.comet.CometMetricNode
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.vectorized._
 
 import org.apache.comet.CometConf.{COMET_BATCH_SIZE, COMET_DEBUG_ENABLED, COMET_EXEC_MEMORY_FRACTION}
@@ -44,7 +46,8 @@ class CometExecIterator(
     val id: Long,
     inputs: Seq[Iterator[ColumnarBatch]],
     protobufQueryPlan: Array[Byte],
-    nativeMetrics: CometMetricNode)
+    nativeMetrics: CometMetricNode,
+    ansiEnabled: Boolean)
     extends Iterator[ColumnarBatch] {
 
   private val nativeLib = new Native()
@@ -99,6 +102,7 @@ class CometExecIterator(
     result.put("memory_fraction", String.valueOf(COMET_EXEC_MEMORY_FRACTION.get()))
     result.put("batch_size", String.valueOf(COMET_BATCH_SIZE.get()))
     result.put("debug_native", String.valueOf(COMET_DEBUG_ENABLED.get()))
+    result.put("ansi_mode", String.valueOf(ansiEnabled))
 
     // Strip mandatory prefix spark. which is not required for DataFusion session params
     conf.getAll.foreach {

spark/src/main/scala/org/apache/comet/CometSparkSessionExtensions.scala

@@ -568,8 +568,12 @@ class CometSparkSessionExtensions
       // DataFusion doesn't have ANSI mode. For now we just disable CometExec if ANSI mode is
       // enabled.
       if (isANSIEnabled(conf)) {
-        logInfo("Comet extension disabled for ANSI mode")
-        return plan
+        if (COMET_ANSI_MODE_ENABLED.get()) {
+          logWarning("Using Comet's experimental support for ANSI mode.")
+        } else {
+          logInfo("Comet extension disabled for ANSI mode")
+          return plan
+        }
       }
 
       // We shouldn't transform Spark query plan if Comet is disabled.

spark/src/main/scala/org/apache/spark/sql/comet/CometCollectLimitExec.scala

@@ -21,10 +21,12 @@ package org.apache.spark.sql.comet
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
+import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.comet.execution.shuffle.{CometShuffledBatchRDD, CometShuffleExchangeExec}
 import org.apache.spark.sql.execution.{ColumnarToRowExec, SparkPlan, UnaryExecNode, UnsafeRowSerializer}
 import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics, SQLShuffleReadMetricsReporter, SQLShuffleWriteMetricsReporter}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.vectorized.ColumnarBatch
 
 import com.google.common.base.Objects
@@ -73,7 +75,11 @@ case class CometCollectLimitExec(
         childRDD
       } else {
         val localLimitedRDD = if (limit >= 0) {
-          CometExecUtils.getNativeLimitRDD(childRDD, output, limit)
+          CometExecUtils.getNativeLimitRDD(
+            childRDD,
+            output,
+            limit,
+            SparkSession.active.conf.get(SQLConf.ANSI_ENABLED))
         } else {
           childRDD
         }
@@ -88,7 +94,11 @@ case class CometCollectLimitExec(
 
         new CometShuffledBatchRDD(dep, readMetrics)
       }
-      CometExecUtils.getNativeLimitRDD(singlePartitionRDD, output, limit)
+      CometExecUtils.getNativeLimitRDD(
+        singlePartitionRDD,
+        output,
+        limit,
+        SparkSession.active.conf.get(SQLConf.ANSI_ENABLED))
     }
   }
 

spark/src/main/scala/org/apache/spark/sql/comet/CometExecUtils.scala

@@ -50,10 +50,11 @@ object CometExecUtils {
   def getNativeLimitRDD(
       childPlan: RDD[ColumnarBatch],
       outputAttribute: Seq[Attribute],
-      limit: Int): RDD[ColumnarBatch] = {
+      limit: Int,
+      ansiMode: Boolean): RDD[ColumnarBatch] = {
     childPlan.mapPartitionsInternal { iter =>
       val limitOp = CometExecUtils.getLimitNativePlan(outputAttribute, limit).get
-      CometExec.getCometIterator(Seq(iter), limitOp)
+      CometExec.getCometIterator(Seq(iter), limitOp, ansiMode)
     }
   }