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[GitHub] [arrow-datafusion] Jimexist opened a new pull request #631: WIP implement rank function and refactor built-in window function evaluation
Jimexist opened a new pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631
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[GitHub] [arrow-datafusion] alamb commented on a change in pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
alamb commented on a change in pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#discussion_r659306810
##########
File path: datafusion/src/physical_plan/expressions/rank.rs
##########
@@ -0,0 +1,172 @@
+// 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.
+
+//! Defines physical expressions that can evaluated at runtime during query execution
Review comment:
```suggestion
//! Defines rank and dense_rank physical expressions that can evaluated at runtime during query execution
```
##########
File path: datafusion/src/physical_plan/expressions/rank.rs
##########
@@ -0,0 +1,172 @@
+// 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.
+
+//! Defines physical expressions that can evaluated at runtime during query execution
+
+use crate::error::Result;
+use crate::physical_plan::window_functions::PartitionEvaluator;
+use crate::physical_plan::{window_functions::BuiltInWindowFunctionExpr, PhysicalExpr};
+use arrow::array::ArrayRef;
+use arrow::array::UInt64Array;
+use arrow::datatypes::{DataType, Field};
+use arrow::record_batch::RecordBatch;
+use std::any::Any;
+use std::iter;
+use std::ops::Range;
+use std::sync::Arc;
+
+/// Rank calculates the rank in the window function with order by
+#[derive(Debug)]
+pub struct Rank {
+ name: String,
+ dense: bool,
+}
+
+/// Create a rank window function
+pub fn rank(name: String) -> Rank {
+ Rank { name, dense: false }
+}
+
+/// Create a dense rank window function
+pub fn dense_rank(name: String) -> Rank {
+ Rank { name, dense: true }
+}
+
+impl BuiltInWindowFunctionExpr for Rank {
+ /// Return a reference to Any that can be used for downcasting
+ fn as_any(&self) -> &dyn Any {
+ self
+ }
+
+ fn field(&self) -> Result<Field> {
+ let nullable = false;
+ let data_type = DataType::UInt64;
+ Ok(Field::new(self.name(), data_type, nullable))
+ }
+
+ fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+ vec![]
+ }
+
+ fn name(&self) -> &str {
+ &self.name
+ }
+
+ fn create_evaluator(
+ &self,
+ _batch: &RecordBatch,
+ ) -> Result<Box<dyn PartitionEvaluator>> {
+ Ok(Box::new(RankEvaluator { dense: self.dense }))
+ }
+}
+
+pub(crate) struct RankEvaluator {
+ dense: bool,
+}
+
+impl PartitionEvaluator for RankEvaluator {
+ fn include_rank(&self) -> bool {
+ true
+ }
+
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef> {
+ unreachable!("rank evaluation must be called with evaluate_partition_with_rank")
+ }
+
+ fn evaluate_partition_with_rank(
+ &self,
+ _partition: Range<usize>,
+ ranks_in_partition: &[Range<usize>],
+ ) -> Result<ArrayRef> {
+ let result = if self.dense {
+ UInt64Array::from_iter_values(ranks_in_partition.iter().enumerate().flat_map(
+ |(index, range)| {
+ let len = range.end - range.start;
+ iter::repeat((index as u64) + 1).take(len)
Review comment:
👨🍳 ❤️ -- nice
##########
File path: datafusion/src/physical_plan/expressions/nth_value.rs
##########
@@ -111,25 +114,33 @@ impl BuiltInWindowFunctionExpr for NthValue {
&self.name
}
- fn evaluate(&self, num_rows: usize, values: &[ArrayRef]) -> Result<ArrayRef> {
- if values.is_empty() {
- return Err(DataFusionError::Execution(format!(
- "No arguments supplied to {}",
- self.name()
- )));
- }
- let value = &values[0];
- if value.len() != num_rows {
- return Err(DataFusionError::Execution(format!(
- "Invalid data supplied to {}, expect {} rows, got {} rows",
- self.name(),
- num_rows,
- value.len()
- )));
- }
- if num_rows == 0 {
- return Ok(new_empty_array(value.data_type()));
- }
+ fn create_evaluator(
+ &self,
+ batch: &RecordBatch,
+ ) -> Result<Box<dyn PartitionEvaluator>> {
+ let values = self
+ .expressions()
+ .iter()
+ .map(|e| e.evaluate(batch))
+ .map(|r| r.map(|v| v.into_array(batch.num_rows())))
+ .collect::<Result<Vec<_>>>()?;
+ Ok(Box::new(NthValueEvaluator {
+ kind: self.kind,
+ values,
+ }))
+ }
+}
+
+pub(crate) struct NthValueEvaluator {
+ kind: NthValueKind,
+ values: Vec<ArrayRef>,
+}
+
+impl PartitionEvaluator for NthValueEvaluator {
+ fn evaluate_partition(&self, partition: Range<usize>) -> Result<ArrayRef> {
Review comment:
This interface makes sense (to pass in the range of rows), though it may make more sense to explicitly pass in `values: Vec<ArrayRef>` rather than assume whatever implements the Evaluator was constructed in a way they can be found
##########
File path: datafusion/src/physical_plan/window_functions.rs
##########
@@ -208,11 +210,57 @@ pub(super) fn signature_for_built_in(fun: &BuiltInWindowFunction) -> Signature {
}
}
+/// Partition evaluator
+pub(crate) trait PartitionEvaluator {
+ /// Whether the evaluator should be evaluated with rank
+ fn include_rank(&self) -> bool {
+ false
+ }
+
+ /// evaluate the partition evaluator against the partitions
+ fn evaluate(&self, partition_points: Vec<Range<usize>>) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| self.evaluate_partition(partition))
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partitions with rank information
+ fn evaluate_with_rank(
+ &self,
+ partition_points: Vec<Range<usize>>,
+ sort_partition_points: Vec<Range<usize>>,
+ ) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| {
+ let ranks_in_partition =
+ find_ranges_in_range(&partition, &sort_partition_points);
+ self.evaluate_partition_with_rank(partition, ranks_in_partition)
+ })
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partition
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef>;
Review comment:
Another potential way to model this with a single evaluate function might be:
```suggestion
fn evaluate_partition(&self, _partition: Range<usize>, _ranks_in_partition: Option<&[Range<usize>])) -> Result<ArrayRef>;
```
Rather than having two separate functions with different signatures
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[GitHub] [arrow-datafusion] Jimexist commented on a change in pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
Jimexist commented on a change in pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#discussion_r659313911
##########
File path: datafusion/src/physical_plan/window_functions.rs
##########
@@ -208,11 +210,57 @@ pub(super) fn signature_for_built_in(fun: &BuiltInWindowFunction) -> Signature {
}
}
+/// Partition evaluator
+pub(crate) trait PartitionEvaluator {
+ /// Whether the evaluator should be evaluated with rank
+ fn include_rank(&self) -> bool {
+ false
+ }
+
+ /// evaluate the partition evaluator against the partitions
+ fn evaluate(&self, partition_points: Vec<Range<usize>>) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| self.evaluate_partition(partition))
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partitions with rank information
+ fn evaluate_with_rank(
+ &self,
+ partition_points: Vec<Range<usize>>,
+ sort_partition_points: Vec<Range<usize>>,
+ ) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| {
+ let ranks_in_partition =
+ find_ranges_in_range(&partition, &sort_partition_points);
+ self.evaluate_partition_with_rank(partition, ranks_in_partition)
+ })
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partition
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef>;
Review comment:
Actually I was trying to avoid generation sort partition points because a majority of the functions do not need that. Nth value not needing them, row number not needing values at all - just length info
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[GitHub] [arrow-datafusion] Jimexist commented on a change in pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
Jimexist commented on a change in pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#discussion_r659478843
##########
File path: datafusion/src/physical_plan/window_functions.rs
##########
@@ -208,11 +210,57 @@ pub(super) fn signature_for_built_in(fun: &BuiltInWindowFunction) -> Signature {
}
}
+/// Partition evaluator
+pub(crate) trait PartitionEvaluator {
+ /// Whether the evaluator should be evaluated with rank
+ fn include_rank(&self) -> bool {
+ false
+ }
+
+ /// evaluate the partition evaluator against the partitions
+ fn evaluate(&self, partition_points: Vec<Range<usize>>) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| self.evaluate_partition(partition))
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partitions with rank information
+ fn evaluate_with_rank(
+ &self,
+ partition_points: Vec<Range<usize>>,
+ sort_partition_points: Vec<Range<usize>>,
+ ) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| {
+ let ranks_in_partition =
+ find_ranges_in_range(&partition, &sort_partition_points);
+ self.evaluate_partition_with_rank(partition, ranks_in_partition)
+ })
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partition
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef>;
Review comment:
having thought of this for a while, i think let's merge this as is.
when arrow 4.4 is released, the partition points is migrated to be an iterator. at that time i can unify both functions and let the laziness do its work (i.e. pass in the iterator in all cases, letting the consumer to decide).
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[GitHub] [arrow-datafusion] Jimexist commented on pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
Jimexist commented on pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#issuecomment-869328638
> This looks good to me @Jimexist -- I had some suggestions on the code structure but I think this is also just fine as written.
>
> The only thing I suggest is adding end-to-end tests (maybe as a postgres integration test)
integration tests added in #638
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[GitHub] [arrow-datafusion] Dandandan commented on a change in pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
Dandandan commented on a change in pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#discussion_r659487202
##########
File path: datafusion/src/physical_plan/expressions/rank.rs
##########
@@ -0,0 +1,172 @@
+// 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.
+
+//! Defines physical expressions that can evaluated at runtime during query execution
+
+use crate::error::Result;
+use crate::physical_plan::window_functions::PartitionEvaluator;
+use crate::physical_plan::{window_functions::BuiltInWindowFunctionExpr, PhysicalExpr};
+use arrow::array::ArrayRef;
+use arrow::array::UInt64Array;
+use arrow::datatypes::{DataType, Field};
+use arrow::record_batch::RecordBatch;
+use std::any::Any;
+use std::iter;
+use std::ops::Range;
+use std::sync::Arc;
+
+/// Rank calculates the rank in the window function with order by
+#[derive(Debug)]
+pub struct Rank {
+ name: String,
+ dense: bool,
+}
+
+/// Create a rank window function
+pub fn rank(name: String) -> Rank {
+ Rank { name, dense: false }
+}
+
+/// Create a dense rank window function
+pub fn dense_rank(name: String) -> Rank {
+ Rank { name, dense: true }
+}
+
+impl BuiltInWindowFunctionExpr for Rank {
+ /// Return a reference to Any that can be used for downcasting
+ fn as_any(&self) -> &dyn Any {
+ self
+ }
+
+ fn field(&self) -> Result<Field> {
+ let nullable = false;
+ let data_type = DataType::UInt64;
+ Ok(Field::new(self.name(), data_type, nullable))
+ }
+
+ fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+ vec![]
+ }
+
+ fn name(&self) -> &str {
+ &self.name
+ }
+
+ fn create_evaluator(
+ &self,
+ _batch: &RecordBatch,
+ ) -> Result<Box<dyn PartitionEvaluator>> {
+ Ok(Box::new(RankEvaluator { dense: self.dense }))
+ }
+}
+
+pub(crate) struct RankEvaluator {
+ dense: bool,
+}
+
+impl PartitionEvaluator for RankEvaluator {
+ fn include_rank(&self) -> bool {
+ true
+ }
+
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef> {
+ unreachable!("rank evaluation must be called with evaluate_partition_with_rank")
+ }
+
+ fn evaluate_partition_with_rank(
+ &self,
+ _partition: Range<usize>,
+ ranks_in_partition: &[Range<usize>],
+ ) -> Result<ArrayRef> {
+ let result = if self.dense {
+ UInt64Array::from_iter_values(ranks_in_partition.iter().enumerate().flat_map(
+ |(index, range)| {
+ let len = range.end - range.start;
+ iter::repeat((index as u64) + 1).take(len)
+ },
Review comment:
```suggestion
UInt64Array::from_iter_values(ranks_in_partition.iter().zip(1u64..).flat_map(
|(index, range)| {
let len = range.end - range.start;
iter::repeat(index).take(len)
},
```
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[GitHub] [arrow-datafusion] alamb merged pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
alamb merged pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631
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[GitHub] [arrow-datafusion] alamb merged pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
alamb merged pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631
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[GitHub] [arrow-datafusion] Dandandan commented on a change in pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
Dandandan commented on a change in pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#discussion_r659485444
##########
File path: datafusion/src/physical_plan/expressions/rank.rs
##########
@@ -0,0 +1,172 @@
+// 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.
+
+//! Defines physical expressions that can evaluated at runtime during query execution
+
+use crate::error::Result;
+use crate::physical_plan::window_functions::PartitionEvaluator;
+use crate::physical_plan::{window_functions::BuiltInWindowFunctionExpr, PhysicalExpr};
+use arrow::array::ArrayRef;
+use arrow::array::UInt64Array;
+use arrow::datatypes::{DataType, Field};
+use arrow::record_batch::RecordBatch;
+use std::any::Any;
+use std::iter;
+use std::ops::Range;
+use std::sync::Arc;
+
+/// Rank calculates the rank in the window function with order by
+#[derive(Debug)]
+pub struct Rank {
+ name: String,
+ dense: bool,
+}
+
+/// Create a rank window function
+pub fn rank(name: String) -> Rank {
+ Rank { name, dense: false }
+}
+
+/// Create a dense rank window function
+pub fn dense_rank(name: String) -> Rank {
+ Rank { name, dense: true }
+}
+
+impl BuiltInWindowFunctionExpr for Rank {
+ /// Return a reference to Any that can be used for downcasting
+ fn as_any(&self) -> &dyn Any {
+ self
+ }
+
+ fn field(&self) -> Result<Field> {
+ let nullable = false;
+ let data_type = DataType::UInt64;
+ Ok(Field::new(self.name(), data_type, nullable))
+ }
+
+ fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+ vec![]
+ }
+
+ fn name(&self) -> &str {
+ &self.name
+ }
+
+ fn create_evaluator(
+ &self,
+ _batch: &RecordBatch,
+ ) -> Result<Box<dyn PartitionEvaluator>> {
+ Ok(Box::new(RankEvaluator { dense: self.dense }))
+ }
+}
+
+pub(crate) struct RankEvaluator {
+ dense: bool,
+}
+
+impl PartitionEvaluator for RankEvaluator {
+ fn include_rank(&self) -> bool {
+ true
+ }
+
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef> {
+ unreachable!("rank evaluation must be called with evaluate_partition_with_rank")
+ }
+
+ fn evaluate_partition_with_rank(
+ &self,
+ _partition: Range<usize>,
+ ranks_in_partition: &[Range<usize>],
+ ) -> Result<ArrayRef> {
+ let result = if self.dense {
+ UInt64Array::from_iter_values(ranks_in_partition.iter().enumerate().flat_map(
+ |(index, range)| {
+ let len = range.end - range.start;
+ iter::repeat((index as u64) + 1).take(len)
+ },
+ ))
+ } else {
+ UInt64Array::from_iter_values(
+ ranks_in_partition
+ .iter()
+ .scan(0_u64, |acc, range| {
+ let len = range.end - range.start;
+ let result = iter::repeat(*acc + 1).take(len);
+ *acc += len as u64;
+ Some(result)
Review comment:
```suggestion
.scan(1_u64, |acc, range| {
let len = range.end - range.start;
let result = iter::repeat(*acc).take(len);
*acc += len as u64;
Some(result)
```
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[GitHub] [arrow-datafusion] Jimexist commented on a change in pull request #631: implement rank and dense_rank function and refactor built-in window function evaluation
Posted by GitBox <gi...@apache.org>.
Jimexist commented on a change in pull request #631:
URL: https://github.com/apache/arrow-datafusion/pull/631#discussion_r659314345
##########
File path: datafusion/src/physical_plan/window_functions.rs
##########
@@ -208,11 +210,57 @@ pub(super) fn signature_for_built_in(fun: &BuiltInWindowFunction) -> Signature {
}
}
+/// Partition evaluator
+pub(crate) trait PartitionEvaluator {
+ /// Whether the evaluator should be evaluated with rank
+ fn include_rank(&self) -> bool {
+ false
+ }
+
+ /// evaluate the partition evaluator against the partitions
+ fn evaluate(&self, partition_points: Vec<Range<usize>>) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| self.evaluate_partition(partition))
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partitions with rank information
+ fn evaluate_with_rank(
+ &self,
+ partition_points: Vec<Range<usize>>,
+ sort_partition_points: Vec<Range<usize>>,
+ ) -> Result<Vec<ArrayRef>> {
+ partition_points
+ .into_iter()
+ .map(|partition| {
+ let ranks_in_partition =
+ find_ranges_in_range(&partition, &sort_partition_points);
+ self.evaluate_partition_with_rank(partition, ranks_in_partition)
+ })
+ .collect()
+ }
+
+ /// evaluate the partition evaluator against the partition
+ fn evaluate_partition(&self, _partition: Range<usize>) -> Result<ArrayRef>;
Review comment:
If it were to be consistent then the interface wouldn't need to exist - would reuse code with aggregation window functions.
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