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8 changes: 8 additions & 0 deletions datafusion/spark/src/function/datetime/mod.rs
Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ pub mod last_day;
pub mod make_dt_interval;
pub mod make_interval;
pub mod next_day;
pub mod quarter;
pub mod time_trunc;
pub mod to_utc_timestamp;
pub mod trunc;
Expand Down Expand Up @@ -72,6 +73,7 @@ make_udf_function!(
unix_seconds,
unix::SparkUnixTimestamp::seconds
);
make_udf_function!(quarter::SparkQuarter, quarter);

pub mod expr_fn {
use datafusion_functions::export_functions;
Expand Down Expand Up @@ -179,6 +181,11 @@ pub mod expr_fn {
"Returns the number of seconds since epoch (1970-01-01 00:00:00 UTC) for the given timestamp `ts`.",
ts
));
export_functions!((
quarter,
"Returns the quarter of the year for date, in the range 1 to 4.",
arg1
));
}

pub fn functions() -> Vec<Arc<ScalarUDF>> {
Expand All @@ -204,5 +211,6 @@ pub fn functions() -> Vec<Arc<ScalarUDF>> {
unix_micros(),
unix_millis(),
unix_seconds(),
quarter(),
]
}
100 changes: 100 additions & 0 deletions datafusion/spark/src/function/datetime/quarter.rs
Original file line number Diff line number Diff line change
@@ -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.

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;

#[derive(Debug, PartialEq, Eq, Hash)]
pub struct SparkQuarter {
signature: Signature,
}

impl Default for SparkQuarter {
fn default() -> Self {
Self::new()
}
}

impl SparkQuarter {
pub fn new() -> Self {
Self {
signature: Signature::one_of(
vec![
TypeSignature::Coercible(vec![Coercion::new_exact(
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Nice progress here, the simplification to date_part cleans things up a lot 👍

One thing that still looks unresolved is the string literal coercion path. Right now quarter only advertises timestamp and date signatures, and since DataFusion validates arguments before simplify() 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 to date_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.

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,
),
}
}
}

impl ScalarUDFImpl for SparkQuarter {
fn name(&self) -> &str {
"quarter"
}

fn signature(&self) -> &Signature {
&self.signature
}

fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
internal_err!("return_field_from_args should be used instead")
}

fn return_field_from_args(&self, _args: ReturnFieldArgs) -> Result<FieldRef> {
Ok(Arc::new(Field::new(self.name(), DataType::Int32, true)))
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quarter still reports its output as nullable in all cases here.

Could we derive the output nullability from the input expression instead?
For non-null DATE/TIMESTAMP inputs, Spark/DataFusion should be able to preserve not-null metadata. As written, this weakens schema information and means the earlier nullability review comment is not fully resolved.

}

fn invoke_with_args(&self, _args: ScalarFunctionArgs) -> Result<ColumnarValue> {
internal_err!("spark quarter should have been simplified to standard date_part")
}

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);

let date_part_expr = Expr::ScalarFunction(ScalarFunction::new_udf(
date_part(),
vec![part_expr, date_expr],
));
Ok(ExprSimplifyResult::Simplified(date_part_expr))
}
}
69 changes: 59 additions & 10 deletions datafusion/sqllogictest/test_files/spark/datetime/quarter.slt
Original file line number Diff line number Diff line change
Expand Up @@ -15,13 +15,62 @@
# specific language governing permissions and limitations
# under the License.

# 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

## 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
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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: SELECT quarter('2016-08-31');

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.

SELECT quarter('2009-01-12'::date);
----
1

query I
SELECT quarter('1970-01-01'::date);
----
1

query I
SELECT quarter('1870-01-01'::date);
----
1

query I
SELECT quarter('2011-04-21'::date);
----
2

query I
SELECT quarter('2024-08-14'::date);
----
3

query I
SELECT quarter('2016-12-12'::date);
----
4

query I
SELECT quarter(NULL::date);
----
NULL

query I
SELECT quarter(NULL::timestamp);
----
NULL

query I
SELECT quarter('2009-01-12 10:00:00'::timestamp);
----
1

query I
SELECT quarter('2020-09-08T12:00:12.12345678+00:00'::timestamp);
----
3

query I
SELECT quarter('2016-12-12');
----
4

query I
SELECT quarter('2009-01-12 10:00:00');
----
1
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