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Posted to commits@datafusion.apache.org by gi...@apache.org on 2026/08/11 03:43:41 UTC
(datafusion) branch gh-readonly-queue/main/pr-24234-a9b61abf04b59ff08e7254b598b07f4fa058c5d1 created (now 9f2a23f038)
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at 9f2a23f038 feat(dataframe): add f16 support to dataframe! macro (#24234)
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new 9f2a23f038 feat(dataframe): add f16 support to dataframe! macro (#24234)
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(datafusion) 01/01: feat(dataframe): add f16 support to dataframe! macro (#24234)
Posted by gi...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
github-merge-queue[bot] pushed a commit to branch gh-readonly-queue/main/pr-24234-a9b61abf04b59ff08e7254b598b07f4fa058c5d1
in repository https://gitbox.apache.org/repos/asf/datafusion.git
commit 9f2a23f03807c2bd67592b221ecbbdf03ca00900
Author: Sergey Zhukov <62...@users.noreply.github.com>
AuthorDate: Tue Aug 11 05:43:17 2026 +0200
feat(dataframe): add f16 support to dataframe! macro (#24234)
## Which issue does this PR close?
<!--
We generally require a GitHub issue to be filed for all bug fixes and
enhancements and this helps us generate change logs for our releases.
You can link an issue to this PR using the GitHub syntax. For example
`Closes #123` indicates that this PR will close issue #123.
-->
- Closes https://github.com/apache/datafusion/issues/24232.
## Rationale for this change
The `dataframe!` macro supports most primitive numeric types but was
missing `f16` support. This change addresses the existing `TODO` and
adds support for `f16` values.
<!--
Why are you proposing this change? If this is already explained clearly
in the issue then this section is not needed.
Explaining clearly why changes are proposed helps reviewers understand
your changes and offer better suggestions for fixes.
Please explain the problem you are trying to solve in terms of the
user-visible
behavior, rather than the implementation.
For example, "The code in `foo.rs` doesn't handle nulls" is a symptom of
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nulls" is the user-visible problem.
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## What changes are included in this PR?
- Add `IntoArrayRef` implementations for `half::f16`:
- `Vec<half::f16>`
- `Vec<Option<half::f16>>`
- `&[half::f16]`
- `&[Option<half::f16>]`
- Improve `test_dataframe_macro` to cover all supported primitive types
and the different input forms supported by the macro.
- Improve `test_dataframe_from_columns` to cover the supported Arrow
data types, including `Float16`.
- No breaking changes.
<!--
There is no need to duplicate the description in the issue here, but it
is sometimes worth providing a summary of the individual changes in this
PR.
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## Are these changes tested?
Yes. The existing `test_dataframe_macro` and
`test_dataframe_from_columns` tests have been extended to verify the
expected data types and resulting dataframe contents.
<!--
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1. Prevent the code from being accidentally broken by subsequent changes
2. Serve as another way to document the expected behavior of the code
If tests are not included in your PR, please explain why (for example,
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## Are there any user-facing changes?
Yes. The `dataframe!` macro now supports `f16` values.
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---
Cargo.lock | 1 +
datafusion/common/src/test_util.rs | 24 ++++-
datafusion/core/Cargo.toml | 1 +
datafusion/core/tests/dataframe/mod.rs | 174 +++++++++++++++++++++++++++------
4 files changed, 171 insertions(+), 29 deletions(-)
diff --git a/Cargo.lock b/Cargo.lock
index 3e320d5de9..6be91ee133 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -1752,6 +1752,7 @@ dependencies = [
"flate2",
"futures",
"glob",
+ "half",
"indexmap 2.14.0",
"insta",
"itertools 0.15.0",
diff --git a/datafusion/common/src/test_util.rs b/datafusion/common/src/test_util.rs
index 3d645c4254..c0353ff408 100644
--- a/datafusion/common/src/test_util.rs
+++ b/datafusion/common/src/test_util.rs
@@ -621,7 +621,29 @@ pub mod array_conversion {
}
}
- //#TODO add impl for f16
+ impl IntoArrayRef for Vec<half::f16> {
+ fn into_array_ref(self) -> ArrayRef {
+ create_array!(Float16, self)
+ }
+ }
+
+ impl IntoArrayRef for Vec<Option<half::f16>> {
+ fn into_array_ref(self) -> ArrayRef {
+ create_array!(Float16, self)
+ }
+ }
+
+ impl IntoArrayRef for &[half::f16] {
+ fn into_array_ref(self) -> ArrayRef {
+ create_array!(Float16, self.to_vec())
+ }
+ }
+
+ impl IntoArrayRef for &[Option<half::f16>] {
+ fn into_array_ref(self) -> ArrayRef {
+ create_array!(Float16, self.to_vec())
+ }
+ }
impl IntoArrayRef for Vec<f32> {
fn into_array_ref(self) -> ArrayRef {
diff --git a/datafusion/core/Cargo.toml b/datafusion/core/Cargo.toml
index 16db28a6d7..0fe48ddf6a 100644
--- a/datafusion/core/Cargo.toml
+++ b/datafusion/core/Cargo.toml
@@ -173,6 +173,7 @@ bytes = { workspace = true }
env_logger = { workspace = true }
glob = { workspace = true }
insta = { workspace = true }
+half = { workspace = true }
rand = { workspace = true, features = ["small_rng"] }
rand_distr = "0.5"
recursive = { workspace = true }
diff --git a/datafusion/core/tests/dataframe/mod.rs b/datafusion/core/tests/dataframe/mod.rs
index a51c752c5c..4966c7aa9a 100644
--- a/datafusion/core/tests/dataframe/mod.rs
+++ b/datafusion/core/tests/dataframe/mod.rs
@@ -21,9 +21,10 @@ mod describe;
use arrow::array::{
Array, ArrayRef, BooleanArray, DictionaryArray, FixedSizeListArray,
- FixedSizeListBuilder, Float32Array, Float64Array, Int8Array, Int32Array,
- Int32Builder, LargeListArray, ListArray, ListBuilder, RecordBatch, StringArray,
- StringBuilder, StructBuilder, UInt32Array, UInt32Builder, UnionArray, record_batch,
+ FixedSizeListBuilder, Float16Array, Float32Array, Float64Array, Int8Array,
+ Int16Array, Int32Array, Int32Builder, Int64Array, LargeListArray, ListArray,
+ ListBuilder, RecordBatch, StringArray, StringBuilder, StructBuilder, UInt8Array,
+ UInt16Array, UInt32Array, UInt32Builder, UInt64Array, UnionArray, record_batch,
};
use arrow::buffer::ScalarBuffer;
use arrow::datatypes::{
@@ -6941,24 +6942,80 @@ async fn test_insert_into_casting_support() -> Result<()> {
#[tokio::test]
async fn test_dataframe_from_columns() -> Result<()> {
- let a: ArrayRef = Arc::new(Int32Array::from(vec![1, 2, 3]));
- let b: ArrayRef = Arc::new(BooleanArray::from(vec![true, true, false]));
- let c: ArrayRef = Arc::new(StringArray::from(vec![Some("foo"), Some("bar"), None]));
- let df = DataFrame::from_columns(vec![("a", a), ("b", b), ("c", c)])?;
+ let bools: ArrayRef = Arc::new(BooleanArray::from(vec![true, false, true]));
+ let i8s: ArrayRef = Arc::new(Int8Array::from(vec![-1, 0, 1]));
+ let i16s: ArrayRef = Arc::new(Int16Array::from(vec![-1, 0, 1]));
+ let i32s: ArrayRef = Arc::new(Int32Array::from(vec![-1, 0, 1]));
+ let i64s: ArrayRef = Arc::new(Int64Array::from(vec![-1, 0, 1]));
+
+ let u8s: ArrayRef = Arc::new(UInt8Array::from(vec![0, 1, 2]));
+ let u16s: ArrayRef = Arc::new(UInt16Array::from(vec![0, 1, 2]));
+ let u32s: ArrayRef = Arc::new(UInt32Array::from(vec![0, 1, 2]));
+ let u64s: ArrayRef = Arc::new(UInt64Array::from(vec![0, 1, 2]));
+
+ let f16s: ArrayRef = Arc::new(Float16Array::from(vec![
+ half::f16::from_f64(1.0),
+ half::f16::from_f64(2.0),
+ half::f16::from_f64(3.0),
+ ]));
+ let f32s: ArrayRef = Arc::new(Float32Array::from(vec![1.0, 2.0, 3.0]));
+ let f64s: ArrayRef = Arc::new(Float64Array::from(vec![1.0, 2.0, 3.0]));
+
+ let strings: ArrayRef =
+ Arc::new(StringArray::from(vec![Some("foo"), Some("bar"), None]));
+
+ let df = DataFrame::from_columns(vec![
+ ("bool", bools),
+ ("i8", i8s),
+ ("i16", i16s),
+ ("i32", i32s),
+ ("i64", i64s),
+ ("u8", u8s),
+ ("u16", u16s),
+ ("u32", u32s),
+ ("u64", u64s),
+ ("f16", f16s),
+ ("f32", f32s),
+ ("f64", f64s),
+ ("str", strings),
+ ])?;
- assert_eq!(df.schema().fields().len(), 3);
+ assert_eq!(df.schema().fields().len(), 13);
assert_eq!(df.clone().count().await?, 3);
- let rows = df.sort(vec![col("a").sort(true, true)])?;
+ let expected_types = [
+ ("bool", DataType::Boolean),
+ ("i8", DataType::Int8),
+ ("i16", DataType::Int16),
+ ("i32", DataType::Int32),
+ ("i64", DataType::Int64),
+ ("u8", DataType::UInt8),
+ ("u16", DataType::UInt16),
+ ("u32", DataType::UInt32),
+ ("u64", DataType::UInt64),
+ ("f16", DataType::Float16),
+ ("f32", DataType::Float32),
+ ("f64", DataType::Float64),
+ ("str", DataType::Utf8),
+ ];
+
+ let schema = df.schema();
+
+ for (name, data_type) in expected_types {
+ assert_eq!(schema.field_with_name(None, name)?.data_type(), &data_type);
+ }
+
+ let rows = df.sort(vec![col("i32").sort(true, true)])?;
+
assert_batches_eq!(
&[
- "+---+-------+-----+",
- "| a | b | c |",
- "+---+-------+-----+",
- "| 1 | true | foo |",
- "| 2 | true | bar |",
- "| 3 | false | |",
- "+---+-------+-----+",
+ "+-------+----+-----+-----+-----+----+-----+-----+-----+-----+-----+-----+-----+",
+ "| bool | i8 | i16 | i32 | i64 | u8 | u16 | u32 | u64 | f16 | f32 | f64 | str |",
+ "+-------+----+-----+-----+-----+----+-----+-----+-----+-----+-----+-----+-----+",
+ "| true | -1 | -1 | -1 | -1 | 0 | 0 | 0 | 0 | 1 | 1.0 | 1.0 | foo |",
+ "| false | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 2 | 2.0 | 2.0 | bar |",
+ "| true | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 3 | 3.0 | 3.0 | |",
+ "+-------+----+-----+-----+-----+----+-----+-----+-----+-----+-----+-----+-----+",
],
&rows.collect().await?
);
@@ -6968,25 +7025,86 @@ async fn test_dataframe_from_columns() -> Result<()> {
#[tokio::test]
async fn test_dataframe_macro() -> Result<()> {
+ let bools = [true, false, true];
+ let i8s = [-1_i8, 0, 1];
+ let i16s = [-1_i16, 0, 1];
+ let i32s = [-1_i32, 0, 1];
+ let i64s = [-1_i64, 0, 1];
+
+ let u8s = [0_u8, 1, 2];
+ let u16s = [0_u16, 1, 2];
+ let u32s = [0_u32, 1, 2];
+ let u64s = [0_u64, 1, 2];
+
+ let f16s = [
+ half::f16::from_f64(1.0),
+ half::f16::from_f64(2.0),
+ half::f16::from_f64(3.0),
+ ];
+ let f32s = [1.0_f32, 2.0, 3.0];
+ let f64s = [1.0_f64, 2.0, 3.0];
+
+ let strings = ["foo", "bar", "baz"];
+
let df = dataframe!(
- "a" => [1, 2, 3],
- "b" => [true, true, false],
- "c" => [Some("foo"), Some("bar"), None]
+ // Vec<T>
+ "bool" => bools.to_vec(),
+ "i8" => i8s.to_vec(),
+ "i16" => i16s.to_vec(),
+ "i32" => i32s.to_vec(),
+
+ // Vec<Option<T>>
+ "i64" => vec![Some(i64s[0]), None, Some(i64s[2])],
+ "u8" => vec![Some(u8s[0]), None, Some(u8s[2])],
+ "u16" => vec![Some(u16s[0]), None, Some(u16s[2])],
+
+ // &[T]
+ "u32" => &u32s,
+ "u64" => &u64s,
+ "f16" => &f16s,
+
+ // &[Option<T>]
+ "f32" => &[Some(f32s[0]), None, Some(f32s[2])],
+ "f64" => &[Some(f64s[0]), None, Some(f64s[2])],
+ "str" => &[Some(strings[0]), None, Some(strings[2])],
)?;
- assert_eq!(df.schema().fields().len(), 3);
+ assert_eq!(df.schema().fields().len(), 13);
assert_eq!(df.clone().count().await?, 3);
- let rows = df.sort(vec![col("a").sort(true, true)])?;
+ let expected_types = [
+ ("bool", DataType::Boolean),
+ ("i8", DataType::Int8),
+ ("i16", DataType::Int16),
+ ("i32", DataType::Int32),
+ ("i64", DataType::Int64),
+ ("u8", DataType::UInt8),
+ ("u16", DataType::UInt16),
+ ("u32", DataType::UInt32),
+ ("u64", DataType::UInt64),
+ ("f16", DataType::Float16),
+ ("f32", DataType::Float32),
+ ("f64", DataType::Float64),
+ ("str", DataType::Utf8),
+ ];
+
+ let schema = df.schema();
+
+ for (name, data_type) in expected_types {
+ assert_eq!(schema.field_with_name(None, name)?.data_type(), &data_type);
+ }
+
+ let rows = df.sort(vec![col("i32").sort(true, true)])?;
+
assert_batches_eq!(
&[
- "+---+-------+-----+",
- "| a | b | c |",
- "+---+-------+-----+",
- "| 1 | true | foo |",
- "| 2 | true | bar |",
- "| 3 | false | |",
- "+---+-------+-----+",
+ "+-------+----+-----+-----+-----+----+-----+-----+-----+-----+-----+-----+-----+",
+ "| bool | i8 | i16 | i32 | i64 | u8 | u16 | u32 | u64 | f16 | f32 | f64 | str |",
+ "+-------+----+-----+-----+-----+----+-----+-----+-----+-----+-----+-----+-----+",
+ "| true | -1 | -1 | -1 | -1 | 0 | 0 | 0 | 0 | 1 | 1.0 | 1.0 | foo |",
+ "| false | 0 | 0 | 0 | | | | 1 | 1 | 2 | | | |",
+ "| true | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 3 | 3.0 | 3.0 | baz |",
+ "+-------+----+-----+-----+-----+----+-----+-----+-----+-----+-----+-----+-----+",
],
&rows.collect().await?
);
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