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Spark quarter function implementation #20808
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,100 @@ | ||
| // 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. | ||
|
|
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| use arrow::datatypes::{DataType, Field, FieldRef}; | ||
| use datafusion_common::types::{NativeType, logical_date, logical_string}; | ||
| use datafusion_common::utils::take_function_args; | ||
| use datafusion_common::{Result, ScalarValue, internal_err}; | ||
| use datafusion_expr::expr::ScalarFunction; | ||
| use datafusion_expr::simplify::{ExprSimplifyResult, SimplifyContext}; | ||
| use datafusion_expr::{ | ||
| Coercion, ColumnarValue, Expr, ReturnFieldArgs, ScalarFunctionArgs, ScalarUDFImpl, | ||
| Signature, TypeSignature, TypeSignatureClass, Volatility, | ||
| }; | ||
| use datafusion_functions::datetime::date_part; | ||
| use std::sync::Arc; | ||
|
|
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| #[derive(Debug, PartialEq, Eq, Hash)] | ||
| pub struct SparkQuarter { | ||
| signature: Signature, | ||
| } | ||
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| impl Default for SparkQuarter { | ||
| fn default() -> Self { | ||
| Self::new() | ||
| } | ||
| } | ||
|
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| impl SparkQuarter { | ||
| pub fn new() -> Self { | ||
| Self { | ||
| signature: Signature::one_of( | ||
| vec![ | ||
| TypeSignature::Coercible(vec![Coercion::new_exact( | ||
| TypeSignatureClass::Timestamp, | ||
| )]), | ||
| TypeSignature::Coercible(vec![Coercion::new_exact( | ||
| TypeSignatureClass::Native(logical_date()), | ||
| )]), | ||
| TypeSignature::Coercible(vec![Coercion::new_implicit( | ||
| TypeSignatureClass::Native(logical_date()), | ||
| vec![TypeSignatureClass::Native(logical_string())], | ||
| NativeType::Date, | ||
| )]), | ||
| ], | ||
| Volatility::Immutable, | ||
| ), | ||
| } | ||
| } | ||
| } | ||
|
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| impl ScalarUDFImpl for SparkQuarter { | ||
| fn name(&self) -> &str { | ||
| "quarter" | ||
| } | ||
|
|
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| fn signature(&self) -> &Signature { | ||
| &self.signature | ||
| } | ||
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| fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> { | ||
| internal_err!("return_field_from_args should be used instead") | ||
| } | ||
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| fn return_field_from_args(&self, _args: ReturnFieldArgs) -> Result<FieldRef> { | ||
| Ok(Arc::new(Field::new(self.name(), DataType::Int32, true))) | ||
|
Contributor
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The nullability here doesn't look correct; quarter in Spark seems to depend on input nullability, see:
Contributor
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
Could we derive the output nullability from the input expression instead? |
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| } | ||
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| fn invoke_with_args(&self, _args: ScalarFunctionArgs) -> Result<ColumnarValue> { | ||
| internal_err!("spark quarter should have been simplified to standard date_part") | ||
| } | ||
|
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| fn simplify( | ||
| &self, | ||
| args: Vec<Expr>, | ||
| _info: &SimplifyContext, | ||
| ) -> Result<ExprSimplifyResult> { | ||
| let [date_expr] = take_function_args(self.name(), args)?; | ||
| let part_expr = Expr::Literal(ScalarValue::new_utf8("quarter"), None); | ||
|
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| let date_part_expr = Expr::ScalarFunction(ScalarFunction::new_udf( | ||
| date_part(), | ||
| vec![part_expr, date_expr], | ||
| )); | ||
| Ok(ExprSimplifyResult::Simplified(date_part_expr)) | ||
| } | ||
| } | ||
| Original file line number | Diff line number | Diff line change |
|---|---|---|
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|
@@ -15,13 +15,62 @@ | |
| # specific language governing permissions and limitations | ||
| # under the License. | ||
|
|
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| # This file was originally created by a porting script from: | ||
| # https://github.com/lakehq/sail/tree/43b6ed8221de5c4c4adbedbb267ae1351158b43c/crates/sail-spark-connect/tests/gold_data/function | ||
| # This file is part of the implementation of the datafusion-spark function library. | ||
| # For more information, please see: | ||
| # https://github.com/apache/datafusion/issues/15914 | ||
|
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| ## Original Query: SELECT quarter('2016-08-31'); | ||
| ## PySpark 3.5.5 Result: {'quarter(2016-08-31)': 3, 'typeof(quarter(2016-08-31))': 'int', 'typeof(2016-08-31)': 'string'} | ||
| #query | ||
| #SELECT quarter('2016-08-31'::string); | ||
| query I | ||
|
Contributor
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The added coverage for DATE and TIMESTAMP inputs looks good 👍 That said, we’re still missing the specific Spark regression case that was called out earlier: Since the implementation still doesn’t accept plain string literals, not having this exact case in the SLT means the mismatch isn’t being caught. It would be great to add this test back in so we lock in the expected Spark behavior and prevent regressions once the coercion issue is fixed. |
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| SELECT quarter('2009-01-12'::date); | ||
| ---- | ||
| 1 | ||
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| query I | ||
| SELECT quarter('1970-01-01'::date); | ||
| ---- | ||
| 1 | ||
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| query I | ||
| SELECT quarter('1870-01-01'::date); | ||
| ---- | ||
| 1 | ||
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| query I | ||
| SELECT quarter('2011-04-21'::date); | ||
| ---- | ||
| 2 | ||
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| query I | ||
| SELECT quarter('2024-08-14'::date); | ||
| ---- | ||
| 3 | ||
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| query I | ||
| SELECT quarter('2016-12-12'::date); | ||
| ---- | ||
| 4 | ||
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| query I | ||
| SELECT quarter(NULL::date); | ||
| ---- | ||
| NULL | ||
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| query I | ||
| SELECT quarter(NULL::timestamp); | ||
| ---- | ||
| NULL | ||
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| query I | ||
| SELECT quarter('2009-01-12 10:00:00'::timestamp); | ||
| ---- | ||
| 1 | ||
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| query I | ||
| SELECT quarter('2020-09-08T12:00:12.12345678+00:00'::timestamp); | ||
| ---- | ||
| 3 | ||
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| query I | ||
| SELECT quarter('2016-12-12'); | ||
| ---- | ||
| 4 | ||
|
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| query I | ||
| SELECT quarter('2009-01-12 10:00:00'); | ||
| ---- | ||
| 1 | ||
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Nice progress here, the simplification to
date_partcleans things up a lot 👍One thing that still looks unresolved is the string literal coercion path. Right now
quarteronly advertises timestamp and date signatures, and since DataFusion validates arguments beforesimplify()runs, string literals get rejected early.That means cases like
quarter('2016-08-31')or timestamp-shaped strings still dont make it far enough to benefit from the rewrite todate_part('quarter', ...), which does support implicit string coercion.So in practice, the Spark-compatible behavior for string inputs is still missing. We probably need to either broaden the signature or otherwise allow these inputs through so the rewrite can happen.
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I added the ability to pass strings as input to the function, but currently there is no support for Spark ANSI mode.