Omega359 commented on code in PR #9181:
URL: https://github.com/apache/arrow-datafusion/pull/9181#discussion_r1486434185
##########
datafusion/physical-expr/src/datetime_expressions.rs:
##########
@@ -502,6 +503,176 @@ pub fn make_current_time(
move |_arg| Ok(ColumnarValue::Scalar(ScalarValue::Time64Nanosecond(nano)))
}
+/// Returns a string representation of a date, time, timestamp or duration
based
+/// on a Chrono pattern.
+///
+/// The syntax for the patterns can be found at
+/// <https://docs.rs/chrono/latest/chrono/format/strftime/index.html>
+///
+/// # Examples
+///
+/// ```
+/// # use chrono::prelude::*;
+/// # use datafusion::prelude::*;
+/// # use datafusion::error::Result;
+/// # use datafusion_common::ScalarValue::TimestampNanosecond;
+/// # use std::sync::Arc;
+/// # use arrow_array::{Date32Array, RecordBatch, StringArray};
+/// # use arrow_schema::{DataType, Field, Schema};
+/// # #[tokio::main]
+/// # async fn main() -> Result<()> {
+/// let schema = Arc::new(Schema::new(vec![
+/// Field::new("values", DataType::Date32, false),
+/// Field::new("patterns", DataType::Utf8, false),
+/// ]));
+///
+/// let batch = RecordBatch::try_new(
+/// schema,
+/// vec![
+/// Arc::new(Date32Array::from(vec![
+/// 18506,
+/// 18507,
+/// 18508,
+/// 18509,
+/// ])),
+/// Arc::new(StringArray::from(vec![
+/// "%Y-%m-%d",
+/// "%Y:%m:%d",
+/// "%Y%m%d",
+/// "%d-%m-%Y",
+/// ])),
+/// ],
+/// )?;
+///
+/// let ctx = SessionContext::new();
+/// ctx.register_batch("t", batch)?;
+/// let df = ctx.table("t").await?;
+///
+/// // use the to_char function to convert col 'values',
+/// // to strings using patterns in col 'patterns'
+/// let df = df.with_column(
+/// "date_str",
+/// to_char(col("values"), col("patterns"))
+/// )?;
+/// // Note that providing a scalar value for the pattern
+/// // is more performant
+/// let df = df.with_column(
+/// "date_str2",
+/// to_char(col("values"), lit("%d-%m-%Y"))
+/// )?;
+/// // literals can be used as well with dataframe calls
+/// let timestamp = "2026-07-08T09:10:11"
+/// .parse::<NaiveDateTime>()
+/// .unwrap()
+/// .with_nanosecond(56789)
+/// .unwrap()
+/// .timestamp_nanos_opt()
+/// .unwrap();
+/// let df = df.with_column(
+/// "timestamp_str",
+/// to_char(lit(TimestampNanosecond(Some(timestamp), None)), lit("%d-%m-%Y
%H:%M:%S"))
+/// )?;
+///
+/// df.show().await?;
+///
+/// # Ok(())
+/// # }
+/// ```
+pub fn to_char(args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ if args.len() != 2 {
+ return exec_err!("to_char function requires 2 arguments, got {}",
args.len());
+ }
+
+ let is_scalar = args
Review Comment:
Great suggestion. So after implementing this the benchmark results improved:
```
❯ cargo criterion --bench to_char
Finished bench [optimized] target(s) in 0.20s
Gnuplot not found, using plotters backend
to_char_array_array_1000
time: [247.58 µs 250.45 µs 253.54 µs]
change: [-1.5593% +0.3878% +2.3496%] (p = 0.70 >
0.05)
No change in performance detected.
to_char_array_scalar_1000
time: [210.05 µs 211.33 µs 212.68 µs]
change: [-1.4277% +0.2494% +1.9011%] (p = 0.77 >
0.05)
No change in performance detected.
to_char_scalar_scalar_1000
time: [565.92 ns 571.56 ns 577.38 ns]
change: [-2.7551% -0.4663% +1.7012%] (p = 0.69 >
0.05)
No change in performance detected.
```
I'll be pushing these changes shortly.
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