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[GitHub] [arrow-datafusion] realno opened a new pull request #1547: Add batch operations to stddev
realno opened a new pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547
# Which issue does this PR close?
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Closes #1546 .
# Rationale for this change
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Explaining clearly why changes are proposed helps reviewers understand your changes and offer better suggestions for fixes.
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For more efficient calculation, we want to implement the batch methods.
# What changes are included in this PR?
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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 there any user-facing changes?
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No user facing changes.
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No break changes.
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[GitHub] [arrow-datafusion] realno commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
realno commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r782594743
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
Review comment:
After some investigation, this approach does work as expected. The reason for the null check is because `downcast_ref` replace the `None` values into `0_f64` so we need to check in the original array when a 0 is observed. The proposed code checks the array after the type cast so it can't catch the nulls. I tried to find a good way to do similar things on the original array but yet to have any luck. I will dig a bit deeper later, please let me know if you know there's a way.
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[GitHub] [arrow-datafusion] alamb commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
alamb commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r783085256
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
Review comment:
> After some investigation, this approach does work as expected.
FWIW I tried making these changes locally and all the tests passed for me
> The reason for the null check is because downcast_ref replace the None values into 0_f64 so we need to check in the original array when a 0 is observed.
I am not sure this is accurate. The way arrow works is that the values and "validity" are tracked in separate structures.
Thus for elements that are `NULL` there is some arbitrary value in the array (which will likely be `0.0f`, though that is not guaranteed by the arrow spec).
The construct of `arr.iter()` returns an iterator of `Option<f64>` that is `None` if the element is NULL, and `Some(f64_value)` if the element is non-NULL.
the use of `filter_map` then filters out the `None` elements, somewhat non obviously
This
```rust
for value in arr.iter().filter_map(|v| v) {
```
Is effectively the same as
```rust
for value in arr.iter() {
let value = match value {
Some(v) => v,
None => continue,
};
```
So I actually think there is a bug in this code as written with nulls -- the check should be
```rust
if values.is_null(i) {
```
Rather than
```rust
if value == 0_f64 && values.is_null(i) {
```
(as null values are not guaranteed to be 0.0f)
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[GitHub] [arrow-datafusion] alamb commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
alamb commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r782496558
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
Review comment:
Here is a more idiomatic way to iterate over the array and skip nulls (and also faster as it doesn't check the bounds on each access to `arr.value(i)`:
```suggestion
// NB: filter map skips `None` (null) values
for value in arr.iter().filter_map(|v| v) {
let new_count = self.count + 1;
```
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
+ let delta1 = value - self.mean;
+ let new_mean = delta1 / new_count as f64 + self.mean;
+ let delta2 = value - new_mean;
+ let new_m2 = self.m2 + delta1 * delta2;
+
+ self.count += 1;
+ self.mean = new_mean;
+ self.m2 = new_m2;
+ }
+
+ Ok(())
+ }
+
+ fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+ let counts = states[0].as_any().downcast_ref::<UInt64Array>().unwrap();
+ let means = states[1].as_any().downcast_ref::<Float64Array>().unwrap();
+ let m2s = states[2].as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..counts.len() {
+ let c = counts.value(i);
+ if c == 0_u64 {
+ continue;
+ }
+ let new_count = self.count + c;
Review comment:
```suggestion
let non_null_counts = counts
.iter()
.enumerate()
.filter_map(|(i, c)| c.map(|c| (i, c)));
for (i,c) in non_null_counts {
let new_count = self.count + c;
```
By the same logic as above this also skips checking bounds on each row. Though for sure I would say this is less readable :(
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[GitHub] [arrow-datafusion] alamb merged pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
alamb merged pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547
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[GitHub] [arrow-datafusion] realno commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
realno commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r782594743
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
Review comment:
After some investigation, this approach does work as expected. The reason for the null check is because `downcast_ref` replace the `None` values into `0_f64` so we need to check in the original array when a 0 is observed. The proposed code checks the array after the type cast so it can't catch the nulls. I tried to find a good way to do similar things on the original array but yet to have any luck. I will dig a bit deeper later, please let me know if you a way to achieve this.
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[GitHub] [arrow-datafusion] alamb commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
alamb commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r783085457
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
+ let delta1 = value - self.mean;
+ let new_mean = delta1 / new_count as f64 + self.mean;
+ let delta2 = value - new_mean;
+ let new_m2 = self.m2 + delta1 * delta2;
+
+ self.count += 1;
+ self.mean = new_mean;
+ self.m2 = new_m2;
+ }
+
+ Ok(())
+ }
+
+ fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+ let counts = states[0].as_any().downcast_ref::<UInt64Array>().unwrap();
+ let means = states[1].as_any().downcast_ref::<Float64Array>().unwrap();
+ let m2s = states[2].as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..counts.len() {
+ let c = counts.value(i);
+ if c == 0_u64 {
+ continue;
+ }
+ let new_count = self.count + c;
Review comment:
seems reasonable to me
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[GitHub] [arrow-datafusion] alamb commented on pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
alamb commented on pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#issuecomment-1009893054
Thank you @realno -- I will try and review this later today
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[GitHub] [arrow-datafusion] alamb commented on pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
alamb commented on pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#issuecomment-1010392869
See https://github.com/apache/arrow-datafusion/pull/1550 for a cleanup of some of this code.
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[GitHub] [arrow-datafusion] realno commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
realno commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r783314459
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
Review comment:
Thanks for clarifying, I will do some more testing locally and follow up with a PR (or more questions :D).
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[GitHub] [arrow-datafusion] realno commented on pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
realno commented on pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#issuecomment-1009569253
@alamb per our discussion, this is to add batch operations for stddev and var.
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[GitHub] [arrow-datafusion] Dandandan commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
Dandandan commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r782525174
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -209,8 +214,8 @@ impl AggregateExpr for VariancePop {
#[derive(Debug)]
pub struct VarianceAccumulator {
- m2: ScalarValue,
- mean: ScalarValue,
+ m2: f64,
Review comment:
👍
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[GitHub] [arrow-datafusion] realno commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
realno commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r782570088
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
+ let delta1 = value - self.mean;
+ let new_mean = delta1 / new_count as f64 + self.mean;
+ let delta2 = value - new_mean;
+ let new_m2 = self.m2 + delta1 * delta2;
+
+ self.count += 1;
+ self.mean = new_mean;
+ self.m2 = new_m2;
+ }
+
+ Ok(())
+ }
+
+ fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+ let counts = states[0].as_any().downcast_ref::<UInt64Array>().unwrap();
+ let means = states[1].as_any().downcast_ref::<Float64Array>().unwrap();
+ let m2s = states[2].as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..counts.len() {
+ let c = counts.value(i);
+ if c == 0_u64 {
+ continue;
+ }
+ let new_count = self.count + c;
Review comment:
Great suggestion! For this part of the code the length of the array is pretty small (number of partitions to merge), so maybe we can opt for readability here.
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[GitHub] [arrow-datafusion] realno commented on a change in pull request #1547: Add batch operations to stddev
Posted by GitBox <gi...@apache.org>.
realno commented on a change in pull request #1547:
URL: https://github.com/apache/arrow-datafusion/pull/1547#discussion_r782569299
##########
File path: datafusion/src/physical_plan/expressions/variance.rs
##########
@@ -230,93 +235,186 @@ impl VarianceAccumulator {
self.count
}
- pub fn get_mean(&self) -> ScalarValue {
- self.mean.clone()
+ pub fn get_mean(&self) -> f64 {
+ self.mean
}
- pub fn get_m2(&self) -> ScalarValue {
- self.m2.clone()
+ pub fn get_m2(&self) -> f64 {
+ self.m2
}
}
impl Accumulator for VarianceAccumulator {
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.count),
- self.mean.clone(),
- self.m2.clone(),
+ ScalarValue::from(self.mean),
+ ScalarValue::from(self.m2),
])
}
+ fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+ let values = &cast(&values[0], &DataType::Float64)?;
+ let arr = values.as_any().downcast_ref::<Float64Array>().unwrap();
+
+ for i in 0..arr.len() {
+ let value = arr.value(i);
+
+ if value == 0_f64 && values.is_null(i) {
+ continue;
+ }
+ let new_count = self.count + 1;
Review comment:
Great suggestion!
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