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Appease new clippy #4101

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Nov 3, 2022
Merged

Appease new clippy #4101

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4 changes: 2 additions & 2 deletions datafusion-examples/examples/dataframe_in_memory.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -37,8 +37,8 @@ async fn main() -> Result<()> {
let batch = RecordBatch::try_new(
schema,
vec![
Arc::new(StringArray::from_slice(&["a", "b", "c", "d"])),
Arc::new(Int32Array::from_slice(&[1, 10, 10, 100])),
Arc::new(StringArray::from_slice(["a", "b", "c", "d"])),
Arc::new(Int32Array::from_slice([1, 10, 10, 100])),
],
)?;

Expand Down
4 changes: 2 additions & 2 deletions datafusion-examples/examples/simple_udaf.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -38,11 +38,11 @@ fn create_context() -> Result<SessionContext> {
// define data in two partitions
let batch1 = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Float32Array::from_slice(&[2.0, 4.0, 8.0]))],
vec![Arc::new(Float32Array::from_slice([2.0, 4.0, 8.0]))],
)?;
let batch2 = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Float32Array::from_slice(&[64.0]))],
vec![Arc::new(Float32Array::from_slice([64.0]))],
)?;

// declare a new context. In spark API, this corresponds to a new spark SQLsession
Expand Down
4 changes: 2 additions & 2 deletions datafusion-examples/examples/simple_udf.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,8 @@ fn create_context() -> Result<SessionContext> {
let batch = RecordBatch::try_new(
schema,
vec![
Arc::new(Float32Array::from_slice(&[2.1, 3.1, 4.1, 5.1])),
Arc::new(Float64Array::from_slice(&[1.0, 2.0, 3.0, 4.0])),
Arc::new(Float32Array::from_slice([2.1, 3.1, 4.1, 5.1])),
Arc::new(Float64Array::from_slice([1.0, 2.0, 3.0, 4.0])),
],
)?;

Expand Down
26 changes: 12 additions & 14 deletions datafusion/common/src/bisect.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -98,10 +98,10 @@ mod tests {
#[test]
fn test_bisect_left_and_right() {
let arrays: Vec<ArrayRef> = vec![
Arc::new(Float64Array::from_slice(&[5.0, 7.0, 8.0, 9., 10.])),
Arc::new(Float64Array::from_slice(&[2.0, 3.0, 3.0, 4.0, 5.0])),
Arc::new(Float64Array::from_slice(&[5.0, 7.0, 8.0, 10., 11.0])),
Arc::new(Float64Array::from_slice(&[15.0, 13.0, 8.0, 5., 0.0])),
Arc::new(Float64Array::from_slice([5.0, 7.0, 8.0, 9., 10.])),
Arc::new(Float64Array::from_slice([2.0, 3.0, 3.0, 4.0, 5.0])),
Arc::new(Float64Array::from_slice([5.0, 7.0, 8.0, 10., 11.0])),
Arc::new(Float64Array::from_slice([15.0, 13.0, 8.0, 5., 0.0])),
];
let search_tuple: Vec<ScalarValue> = vec![
ScalarValue::Float64(Some(8.0)),
Expand Down Expand Up @@ -169,7 +169,7 @@ mod tests {
#[test]
fn test_bisect_left_and_right_diff_sort() {
// Descending, left
let arrays: Vec<ArrayRef> = vec![Arc::new(Float64Array::from_slice(&[
let arrays: Vec<ArrayRef> = vec![Arc::new(Float64Array::from_slice([
4.0, 3.0, 2.0, 1.0, 0.0,
]))];
let search_tuple: Vec<ScalarValue> = vec![ScalarValue::Float64(Some(4.0))];
Expand All @@ -181,7 +181,7 @@ mod tests {
assert_eq!(res, 0);

// Descending, right
let arrays: Vec<ArrayRef> = vec![Arc::new(Float64Array::from_slice(&[
let arrays: Vec<ArrayRef> = vec![Arc::new(Float64Array::from_slice([
4.0, 3.0, 2.0, 1.0, 0.0,
]))];
let search_tuple: Vec<ScalarValue> = vec![ScalarValue::Float64(Some(4.0))];
Expand All @@ -193,9 +193,8 @@ mod tests {
assert_eq!(res, 1);

// Ascending, left
let arrays: Vec<ArrayRef> = vec![Arc::new(Float64Array::from_slice(&[
5.0, 7.0, 8.0, 9., 10.,
]))];
let arrays: Vec<ArrayRef> =
vec![Arc::new(Float64Array::from_slice([5.0, 7.0, 8.0, 9., 10.]))];
let search_tuple: Vec<ScalarValue> = vec![ScalarValue::Float64(Some(7.0))];
let ords = [SortOptions {
descending: false,
Expand All @@ -205,9 +204,8 @@ mod tests {
assert_eq!(res, 1);

// Ascending, right
let arrays: Vec<ArrayRef> = vec![Arc::new(Float64Array::from_slice(&[
5.0, 7.0, 8.0, 9., 10.,
]))];
let arrays: Vec<ArrayRef> =
vec![Arc::new(Float64Array::from_slice([5.0, 7.0, 8.0, 9., 10.]))];
let search_tuple: Vec<ScalarValue> = vec![ScalarValue::Float64(Some(7.0))];
let ords = [SortOptions {
descending: false,
Expand All @@ -217,8 +215,8 @@ mod tests {
assert_eq!(res, 2);

let arrays: Vec<ArrayRef> = vec![
Arc::new(Float64Array::from_slice(&[5.0, 7.0, 8.0, 8.0, 9., 10.])),
Arc::new(Float64Array::from_slice(&[10.0, 9.0, 8.0, 7.5, 7., 6.])),
Arc::new(Float64Array::from_slice([5.0, 7.0, 8.0, 8.0, 9., 10.])),
Arc::new(Float64Array::from_slice([10.0, 9.0, 8.0, 7.5, 7., 6.])),
];
let search_tuple: Vec<ScalarValue> = vec![
ScalarValue::Float64(Some(8.0)),
Expand Down
2 changes: 1 addition & 1 deletion datafusion/common/src/pyarrow.rs
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Expand Up @@ -39,7 +39,7 @@ impl PyArrowConvert for ScalarValue {

// construct pyarrow array from the python value and pyarrow type
let factory = py.import("pyarrow")?.getattr("array")?;
let args = PyList::new(py, &[val]);
let args = PyList::new(py, [val]);
let array = factory.call1((args, typ))?;

// convert the pyarrow array to rust array using C data interface
Expand Down
22 changes: 11 additions & 11 deletions datafusion/common/src/scalar.rs
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Expand Up @@ -3432,26 +3432,26 @@ mod tests {
let expected = Arc::new(StructArray::from(vec![
(
field_a.clone(),
Arc::new(Int32Array::from_slice(&[23, 23])) as ArrayRef,
Arc::new(Int32Array::from_slice([23, 23])) as ArrayRef,
),
(
field_b.clone(),
Arc::new(BooleanArray::from_slice(&[false, false])) as ArrayRef,
Arc::new(BooleanArray::from_slice([false, false])) as ArrayRef,
),
(
field_c.clone(),
Arc::new(StringArray::from_slice(&["Hello", "Hello"])) as ArrayRef,
Arc::new(StringArray::from_slice(["Hello", "Hello"])) as ArrayRef,
),
(
field_d.clone(),
Arc::new(StructArray::from(vec![
(
field_e.clone(),
Arc::new(Int16Array::from_slice(&[2, 2])) as ArrayRef,
Arc::new(Int16Array::from_slice([2, 2])) as ArrayRef,
),
(
field_f.clone(),
Arc::new(Int64Array::from_slice(&[3, 3])) as ArrayRef,
Arc::new(Int64Array::from_slice([3, 3])) as ArrayRef,
),
])) as ArrayRef,
),
Expand Down Expand Up @@ -3527,27 +3527,27 @@ mod tests {
let expected = Arc::new(StructArray::from(vec![
(
field_a,
Arc::new(Int32Array::from_slice(&[23, 7, -1000])) as ArrayRef,
Arc::new(Int32Array::from_slice([23, 7, -1000])) as ArrayRef,
),
(
field_b,
Arc::new(BooleanArray::from_slice(&[false, true, true])) as ArrayRef,
Arc::new(BooleanArray::from_slice([false, true, true])) as ArrayRef,
),
(
field_c,
Arc::new(StringArray::from_slice(&["Hello", "World", "!!!!!"]))
Arc::new(StringArray::from_slice(["Hello", "World", "!!!!!"]))
as ArrayRef,
),
(
field_d,
Arc::new(StructArray::from(vec![
(
field_e,
Arc::new(Int16Array::from_slice(&[2, 4, 6])) as ArrayRef,
Arc::new(Int16Array::from_slice([2, 4, 6])) as ArrayRef,
),
(
field_f,
Arc::new(Int64Array::from_slice(&[3, 5, 7])) as ArrayRef,
Arc::new(Int64Array::from_slice([3, 5, 7])) as ArrayRef,
),
])) as ArrayRef,
),
Expand Down Expand Up @@ -3608,7 +3608,7 @@ mod tests {
let expected = StructArray::from(vec![
(
field_a.clone(),
Arc::new(StringArray::from_slice(&["First", "Second", "Third"]))
Arc::new(StringArray::from_slice(["First", "Second", "Third"]))
as ArrayRef,
),
(
Expand Down
4 changes: 2 additions & 2 deletions datafusion/core/src/datasource/listing/table.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -143,7 +143,7 @@ impl ListingTableConfig {
pub async fn infer_options(self, ctx: &SessionState) -> Result<Self> {
let store = ctx
.runtime_env
.object_store(&self.table_paths.get(0).unwrap())?;
.object_store(self.table_paths.get(0).unwrap())?;

let file = self
.table_paths
Expand Down Expand Up @@ -442,7 +442,7 @@ impl ListingTable {
) -> Result<(Vec<Vec<PartitionedFile>>, Statistics)> {
let store = ctx
.runtime_env
.object_store(&self.table_paths.get(0).unwrap())?;
.object_store(self.table_paths.get(0).unwrap())?;
// list files (with partitions)
let file_list = future::try_join_all(self.table_paths.iter().map(|table_path| {
pruned_partition_list(
Expand Down
40 changes: 20 additions & 20 deletions datafusion/core/src/datasource/memory.rs
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Expand Up @@ -172,9 +172,9 @@ mod tests {
let batch = RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
Arc::new(Int32Array::from(vec![None, None, Some(9)])),
],
)?;
Expand Down Expand Up @@ -209,9 +209,9 @@ mod tests {
let batch = RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
],
)?;

Expand Down Expand Up @@ -241,9 +241,9 @@ mod tests {
let batch = RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
],
)?;

Expand Down Expand Up @@ -284,9 +284,9 @@ mod tests {
let batch = RecordBatch::try_new(
schema1,
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
],
)?;

Expand Down Expand Up @@ -317,8 +317,8 @@ mod tests {
let batch = RecordBatch::try_new(
schema1,
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[7, 5, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([7, 5, 9])),
],
)?;

Expand Down Expand Up @@ -362,18 +362,18 @@ mod tests {
let batch1 = RecordBatch::try_new(
Arc::new(schema1),
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
],
)?;

let batch2 = RecordBatch::try_new(
Arc::new(schema2),
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
],
)?;

Expand Down
4 changes: 2 additions & 2 deletions datafusion/core/src/physical_optimizer/coalesce_batches.rs
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Expand Up @@ -46,7 +46,7 @@ impl PhysicalOptimizerRule for CoalesceBatches {
fn optimize(
&self,
plan: Arc<dyn crate::physical_plan::ExecutionPlan>,
config: &crate::execution::context::SessionConfig,
_config: &crate::execution::context::SessionConfig,
) -> Result<Arc<dyn crate::physical_plan::ExecutionPlan>> {
if plan.children().is_empty() {
// leaf node, children cannot be replaced
Expand All @@ -56,7 +56,7 @@ impl PhysicalOptimizerRule for CoalesceBatches {
let children = plan
.children()
.iter()
.map(|child| self.optimize(child.clone(), config))
.map(|child| self.optimize(child.clone(), _config))
.collect::<Result<Vec<_>>>()?;
let plan = with_new_children_if_necessary(plan, children)?;
// The goal here is to detect operators that could produce small batches and only
Expand Down
4 changes: 2 additions & 2 deletions datafusion/core/src/physical_optimizer/merge_exec.rs
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Expand Up @@ -41,7 +41,7 @@ impl PhysicalOptimizerRule for AddCoalescePartitionsExec {
fn optimize(
&self,
plan: Arc<dyn crate::physical_plan::ExecutionPlan>,
config: &crate::execution::context::SessionConfig,
_config: &crate::execution::context::SessionConfig,
) -> Result<Arc<dyn crate::physical_plan::ExecutionPlan>> {
if plan.children().is_empty() {
// leaf node, children cannot be replaced
Expand All @@ -50,7 +50,7 @@ impl PhysicalOptimizerRule for AddCoalescePartitionsExec {
let children = plan
.children()
.iter()
.map(|child| self.optimize(child.clone(), config))
.map(|child| self.optimize(child.clone(), _config))
.collect::<Result<Vec<_>>>()?;
match plan.required_child_distribution() {
Distribution::UnspecifiedDistribution => {
Expand Down
2 changes: 1 addition & 1 deletion datafusion/core/src/physical_optimizer/pruning.rs
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Expand Up @@ -1219,7 +1219,7 @@ mod tests {

// Note the statistics return binary (which can't be cast to string)
let statistics = OneContainerStats {
min_values: Some(Arc::new(BinaryArray::from_slice(&[&[255u8] as &[u8]]))),
min_values: Some(Arc::new(BinaryArray::from_slice([&[255u8] as &[u8]]))),
max_values: None,
num_containers: 1,
};
Expand Down
8 changes: 4 additions & 4 deletions datafusion/core/src/physical_plan/aggregates/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -683,16 +683,16 @@ mod tests {
RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(UInt32Array::from_slice(&[2, 3, 4, 4])),
Arc::new(Float64Array::from_slice(&[1.0, 2.0, 3.0, 4.0])),
Arc::new(UInt32Array::from_slice([2, 3, 4, 4])),
Arc::new(Float64Array::from_slice([1.0, 2.0, 3.0, 4.0])),
],
)
.unwrap(),
RecordBatch::try_new(
schema,
vec![
Arc::new(UInt32Array::from_slice(&[2, 3, 3, 4])),
Arc::new(Float64Array::from_slice(&[1.0, 2.0, 3.0, 4.0])),
Arc::new(UInt32Array::from_slice([2, 3, 3, 4])),
Arc::new(Float64Array::from_slice([1.0, 2.0, 3.0, 4.0])),
],
)
.unwrap(),
Expand Down
4 changes: 2 additions & 2 deletions datafusion/core/src/physical_plan/common.rs
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Expand Up @@ -366,8 +366,8 @@ mod tests {
let batch = RecordBatch::try_new(
Arc::clone(&schema),
vec![
Arc::new(Float32Array::from_slice(&[1., 2., 3.])),
Arc::new(Float64Array::from_slice(&[9., 8., 7.])),
Arc::new(Float32Array::from_slice([1., 2., 3.])),
Arc::new(Float64Array::from_slice([9., 8., 7.])),
],
)?;
let actual =
Expand Down
6 changes: 3 additions & 3 deletions datafusion/core/src/physical_plan/memory.rs
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Expand Up @@ -238,10 +238,10 @@ mod tests {
let batch = RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_slice(&[1, 2, 3])),
Arc::new(Int32Array::from_slice(&[4, 5, 6])),
Arc::new(Int32Array::from_slice([1, 2, 3])),
Arc::new(Int32Array::from_slice([4, 5, 6])),
Arc::new(Int32Array::from(vec![None, None, Some(9)])),
Arc::new(Int32Array::from_slice(&[7, 8, 9])),
Arc::new(Int32Array::from_slice([7, 8, 9])),
],
)?;

Expand Down
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