maropu commented on a change in pull request #31349: URL: https://github.com/apache/spark/pull/31349#discussion_r570661910
########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,316 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import scala.annotation.tailrec + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: + WidenSetOperationTypes :: + PromoteStringLiterals :: + DecimalPrecision :: + FunctionArgumentConversion :: + ConcatCoercion :: + MapZipWithCoercion :: + EltCoercion :: + CaseWhenCoercion :: + IfCoercion :: + StackCoercion :: + Division :: + IntegralDivision :: + ImplicitTypeCasts :: + DateTimeOperations :: + WindowFrameCoercion :: + StringLiteralCoercion :: + Nil + + /** + * Find the tightest common type of two types that might be used in a binary expression. + */ + override def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = { + (t1, t2) match { + case (t1, t2) if t1 == t2 => Some(t1) + case (NullType, t1) => Some(t1) + case (t1, NullType) => Some(t1) + + case (t1: NumericType, t2: NumericType) => + findTightestCommonNumericType(t1, t2) + + case (_: TimestampType, _: DateType) | (_: DateType, _: TimestampType) => + Some(TimestampType) + + case (t1, t2) => findTypeForComplex(t1, t2, findTightestCommonType) + } + } + + @tailrec + private def findTightestCommonNumericType(t1: NumericType, t2: NumericType): Option[DataType] = { + (t1, t2) match { + case (i: IntegralType, d: DecimalType) => + if (d.isWiderThan(i)) { + Some(t2) + } else { + findTightestCommonNumericType(DecimalType.forType(i), d) + } + + case (t1: DecimalType, t2: IntegralType) => + findTightestCommonNumericType(t2, t1) + + case (t1: DecimalType, t2: DecimalType) => + Some(DecimalPrecision.widerDecimalType(t1, t2)) Review comment: Ah, ok. The latest change looks good. ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: + WidenSetOperationTypes :: + PromoteStringLiterals :: + DecimalPrecision :: + FunctionArgumentConversion :: + ConcatCoercion :: + MapZipWithCoercion :: + EltCoercion :: + CaseWhenCoercion :: + IfCoercion :: + StackCoercion :: + Division :: + IntegralDivision :: + ImplicitTypeCasts :: + DateTimeOperations :: + WindowFrameCoercion :: + StringLiteralCoercion :: + Nil + + override def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = { + (t1, t2) match { + case (t1, t2) if t1 == t2 => Some(t1) + case (NullType, t1) => Some(t1) + case (t1, NullType) => Some(t1) + + case (t1: IntegralType, t2: DecimalType) if t2.isWiderThan(t1) => + Some(t2) + case (t1: DecimalType, t2: IntegralType) if t1.isWiderThan(t2) => + Some(t1) + + case (t1: NumericType, t2: NumericType) + if !t1.isInstanceOf[DecimalType] && !t2.isInstanceOf[DecimalType] => + val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2) + val widerType = numericPrecedence(index) + if (widerType == FloatType) { + // If the input type is an Integral type and a Float type, simply return Double type as + // the tightest common type to avoid potential precision loss on converting the Integral + // type as Float type. + Some(DoubleType) Review comment: Just out of curiosity; I know the other DBMSs, e.g., postgresql, has the same behaviour. The standard says something about it, too? ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) Review comment: what does `odd` means? ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: Review comment: We need to update it, too? https://github.com/apache/spark/blob/961c85166a259cd4e5343e54f27228767a841a88/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala#L478-L482 ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: + WidenSetOperationTypes :: + PromoteStringLiterals :: + DecimalPrecision :: + FunctionArgumentConversion :: + ConcatCoercion :: + MapZipWithCoercion :: + EltCoercion :: + CaseWhenCoercion :: + IfCoercion :: + StackCoercion :: + Division :: + IntegralDivision :: + ImplicitTypeCasts :: + DateTimeOperations :: + WindowFrameCoercion :: + StringLiteralCoercion :: + Nil + + override def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = { + (t1, t2) match { + case (t1, t2) if t1 == t2 => Some(t1) + case (NullType, t1) => Some(t1) + case (t1, NullType) => Some(t1) + + case (t1: IntegralType, t2: DecimalType) if t2.isWiderThan(t1) => + Some(t2) + case (t1: DecimalType, t2: IntegralType) if t1.isWiderThan(t2) => + Some(t1) + + case (t1: NumericType, t2: NumericType) + if !t1.isInstanceOf[DecimalType] && !t2.isInstanceOf[DecimalType] => + val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2) + val widerType = numericPrecedence(index) + if (widerType == FloatType) { + // If the input type is an Integral type and a Float type, simply return Double type as + // the tightest common type to avoid potential precision loss on converting the Integral + // type as Float type. + Some(DoubleType) + } else { + Some(widerType) + } + + case (_: TimestampType, _: DateType) | (_: DateType, _: TimestampType) => + Some(TimestampType) + + case (t1, t2) => findTypeForComplex(t1, t2, findTightestCommonType) + } + } + + override def findWiderTypeForTwo(t1: DataType, t2: DataType): Option[DataType] = { + findTightestCommonType(t1, t2) + .orElse(findWiderTypeForDecimal(t1, t2)) Review comment: Widening decimal types is only accepted in the ANSI mode? What does the standard says? (I noticed this entry was added in the latest commit) ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: + WidenSetOperationTypes :: Review comment: It looks like we need to update the `WidenSetOperationTypes` comment, too: https://github.com/apache/spark/blob/961c85166a259cd4e5343e54f27228767a841a88/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala#L304-L327 ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala ########## @@ -206,39 +132,22 @@ object TypeCoercion { } Review comment: `hasStringType` is only used for `TypeCoercion`, too? ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,316 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import scala.annotation.tailrec + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: + WidenSetOperationTypes :: + PromoteStringLiterals :: + DecimalPrecision :: + FunctionArgumentConversion :: + ConcatCoercion :: + MapZipWithCoercion :: + EltCoercion :: + CaseWhenCoercion :: + IfCoercion :: + StackCoercion :: + Division :: + IntegralDivision :: + ImplicitTypeCasts :: + DateTimeOperations :: + WindowFrameCoercion :: + StringLiteralCoercion :: + Nil + + /** + * Find the tightest common type of two types that might be used in a binary expression. + */ + override def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = { + (t1, t2) match { + case (t1, t2) if t1 == t2 => Some(t1) + case (NullType, t1) => Some(t1) + case (t1, NullType) => Some(t1) + + case (t1: NumericType, t2: NumericType) => + findTightestCommonNumericType(t1, t2) + + case (_: TimestampType, _: DateType) | (_: DateType, _: TimestampType) => + Some(TimestampType) + + case (t1, t2) => findTypeForComplex(t1, t2, findTightestCommonType) + } + } + + @tailrec + private def findTightestCommonNumericType(t1: NumericType, t2: NumericType): Option[DataType] = { + (t1, t2) match { + case (i: IntegralType, d: DecimalType) => + if (d.isWiderThan(i)) { + Some(t2) + } else { + findTightestCommonNumericType(DecimalType.forType(i), d) + } + + case (t1: DecimalType, t2: IntegralType) => + findTightestCommonNumericType(t2, t1) + + case (t1: DecimalType, t2: DecimalType) => + Some(DecimalPrecision.widerDecimalType(t1, t2)) + + case (_: FractionalType, _: DecimalType) | (_: DecimalType, _: FractionalType) => + Some(DoubleType) + + // Promote numeric types to the highest of the two + case _ => + // The cases that t1 or t2 is DecimalType should be handled already. + assert(!t1.isInstanceOf[DecimalType] && !t2.isInstanceOf[DecimalType]) + val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2) + val widerType = numericPrecedence(index) + if (widerType == FloatType) { + // If the input type is an Integral type and a Float type, simply return Double type as + // the tightest common type to avoid potential precision loss on converting the Integral + // type as Float type. + Some(DoubleType) + } else { + Some(widerType) + } + } + } + + override def findWiderTypeForTwo(t1: DataType, t2: DataType): Option[DataType] = { + findTightestCommonType(t1, t2) + } + + override def findWiderCommonType(types: Seq[DataType]): Option[DataType] = { + types.foldLeft[Option[DataType]](Some(NullType))((r, c) => + r match { + case Some(d) => findWiderTypeForTwo(d, c) + case _ => None + }) + } + + override def implicitCast(e: Expression, expectedType: AbstractDataType): Option[Expression] = { + (e, expectedType) match { + // This type coercion system will allow implicit converting String type literals as other + // primitive types, in case of breaking too many existing Spark SQL queries. + case (_ @ StringType(), a: AtomicType) if e.foldable && a != BooleanType && a != StringType => + Some(Cast(e, a)) + + case (_ @ StringType(), NumericType) if e.foldable => + Some(Cast(e, DoubleType)) Review comment: All the numeric types are also atomic types, so this match cannot happen? Am I missing something? ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/AnsiTypeCoercion.scala ########## @@ -0,0 +1,292 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.analysis + +import org.apache.spark.sql.catalyst.analysis.TypeCoercion.numericPrecedence +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types._ + +/** + * In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input + * data types. + * As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011 + * Information technology - Database languages - SQL - Part 2: Foundation (SQL/Foundation)", + * the type precedence lists of primitive data types are as following: + * * Byte: Byte, Short, Int, Long, Decimal, Float, Double + * * Short: Short, Int, Long, Decimal, Float, Double + * * Int: Int, Long, Decimal, Float, Double + * * Long: Long, Decimal, Float, Double + * * Decimal: Any wider Numeric type + * * Float: Float, Double + * * Double: Double + * * String: String + * * Date: Date, Timestamp + * * Timestamp: Timestamp + * * Binary: Binary + * * Boolean: Boolean + * * Interval: Interval + * As for complex data types, Spark will determine the precedent list recursively based on their + * sub-types. + * + * With the definition of type precedent list, the general type coercion rules are as following: + * * Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S + * * Comparison is allowed iff the data type precedence list of both sides has at least one common + * element. When evaluating the comparison, Spark casts both sides as the tightest common data + * type of their precedent lists. + * * There should be at least one common data type among all the children's precedence lists for + * the following operators. The data type of the operator is the tightest common precedent + * data type. + * * In + * * Except(odd) + * * Intersect + * * Greatest + * * Least + * * Union + * * If + * * CaseWhen + * * CreateArray + * * Array Concat + * * Sequence + * * MapConcat + * * CreateMap + * * For complex types (struct, array, map), Spark recursively looks into the element type and + * applies the rules above. If the element nullability is converted from true to false, add + * runtime null check to the elements. + * Note: this new type coercion system will allow implicit converting String type literals as other + * primitive types, in case of breaking too many existing Spark SQL queries. This is a special + * rule and it is not from the ANSI SQL standard. + */ +object AnsiTypeCoercion extends TypeCoercionBase { + override def typeCoercionRules: List[Rule[LogicalPlan]] = + InConversion :: + WidenSetOperationTypes :: + PromoteStringLiterals :: + DecimalPrecision :: + FunctionArgumentConversion :: + ConcatCoercion :: + MapZipWithCoercion :: + EltCoercion :: + CaseWhenCoercion :: + IfCoercion :: + StackCoercion :: + Division :: + IntegralDivision :: + ImplicitTypeCasts :: + DateTimeOperations :: + WindowFrameCoercion :: + StringLiteralCoercion :: + Nil + + override def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = { + (t1, t2) match { + case (t1, t2) if t1 == t2 => Some(t1) + case (NullType, t1) => Some(t1) + case (t1, NullType) => Some(t1) + + case (t1: IntegralType, t2: DecimalType) if t2.isWiderThan(t1) => + Some(t2) + case (t1: DecimalType, t2: IntegralType) if t1.isWiderThan(t2) => + Some(t1) + + case (t1: NumericType, t2: NumericType) + if !t1.isInstanceOf[DecimalType] && !t2.isInstanceOf[DecimalType] => + val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2) + val widerType = numericPrecedence(index) + if (widerType == FloatType) { + // If the input type is an Integral type and a Float type, simply return Double type as + // the tightest common type to avoid potential precision loss on converting the Integral + // type as Float type. + Some(DoubleType) + } else { + Some(widerType) + } + + case (_: TimestampType, _: DateType) | (_: DateType, _: TimestampType) => + Some(TimestampType) + + case (t1, t2) => findTypeForComplex(t1, t2, findTightestCommonType) + } + } + + override def findWiderTypeForTwo(t1: DataType, t2: DataType): Option[DataType] = { + findTightestCommonType(t1, t2) + .orElse(findWiderTypeForDecimal(t1, t2)) + .orElse(findTypeForComplex(t1, t2, findWiderTypeForTwo)) + } + + override def findWiderCommonType(types: Seq[DataType]): Option[DataType] = { + types.foldLeft[Option[DataType]](Some(NullType))((r, c) => + r match { + case Some(d) => findWiderTypeForTwo(d, c) + case _ => None + }) + } + + override def implicitCast(e: Expression, expectedType: AbstractDataType): Option[Expression] = { + (e, expectedType) match { + // This type coercion system will allow implicit converting String type literals as other + // primitive types, in case of breaking too many existing Spark SQL queries. + case (StringType(), a: AtomicType) if e.foldable && a != BooleanType && a != StringType => + Some(Cast(e, a)) + + case (_ @ StringType(), NumericType) if e.foldable => + Some(Cast(e, DoubleType)) + + case _ => + implicitCast(e.dataType, expectedType).map { dt => + if (dt == e.dataType) e else Cast(e, dt) + } + } + } + + /** + * In Ansi mode, the implicit cast is only allow when `expectedType` is in the type precedent + * list of `inType`. + */ + private def implicitCast(inType: DataType, expectedType: AbstractDataType): Option[DataType] = { + (inType, expectedType) match { + // If the expected type equals the input type, no need to cast. + case _ if expectedType.acceptsType(inType) => Some(inType) + + // Cast null type (usually from null literals) into target types + case (NullType, target) => Some(target.defaultConcreteType) + + // If input is a numeric type but not decimal, and we expect a decimal type, + // cast the input to decimal. + case (d: NumericType, DecimalType) => Some(DecimalType.forType(d)) + + case (n1: NumericType, n2: NumericType) => + val widerType = findWiderTypeForTwo(n1, n2) + widerType match { + // if the expected type is Float type, we should still return Float type. + case Some(DoubleType) if n1 != DoubleType && n2 == FloatType => Some(FloatType) + + case Some(dt) if dt == n2 => Some(dt) + + case _ => None + } + + case (DateType, TimestampType) => Some(TimestampType) + + // When we reach here, input type is not acceptable for any types in this type collection, + // try to find the first one we can implicitly cast. + case (_, TypeCollection(types)) => + types.flatMap(implicitCast(inType, _)).headOption + + // Implicit cast between array types. + // + // Compare the nullabilities of the from type and the to type, check whether the cast of + // the nullability is resolvable by the following rules: + // 1. If the nullability of the to type is true, the cast is always allowed; + // 2. If the nullabilities of both the from type and the to type are false, the cast is + // allowed. + // 3. Otherwise, the cast is not allowed + case (ArrayType(fromType, containsNullFrom), ArrayType(toType: DataType, containsNullTo)) + if Cast.resolvableNullability(containsNullFrom, containsNullTo) => + implicitCast(fromType, toType).map(ArrayType(_, containsNullTo)) + + // Implicit cast between Map types. + // Follows the same semantics of implicit casting between two array types. + // Refer to documentation above. + case (MapType(fromKeyType, fromValueType, fn), MapType(toKeyType, toValueType, tn)) + if Cast.resolvableNullability(fn, tn) => + val newKeyType = implicitCast(fromKeyType, toKeyType) + val newValueType = implicitCast(fromValueType, toValueType) + if (newKeyType.isDefined && newValueType.isDefined) { + Some(MapType(newKeyType.get, newValueType.get, tn)) + } else { + None + } + + case _ => None + } + } + + override def canCast(from: DataType, to: DataType): Boolean = AnsiCast.canCast(from, to) + + /** + * Promotes string literals that appear in arithmetic and comparison expressions. + */ + object PromoteStringLiterals extends TypeCoercionRule { Review comment: Could you add more tests for this new rule? ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala ########## @@ -206,39 +132,22 @@ object TypeCoercion { } Review comment: Could we move `findCommonTypeDifferentOnlyInNullFlags` into `object TypeCoercion`? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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