Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Minor: support complex expr as the arg in the ApproxPercentileCont function #8580

Merged
merged 2 commits into from
Dec 20, 2023
Merged

Minor: support complex expr as the arg in the ApproxPercentileCont function #8580

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0ddf443
support complex lit expr for the arg
liukun4515 Dec 19, 2023
2cf286c
enchancement the percentile
liukun4515 Dec 19, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 20 additions & 0 deletions datafusion/core/tests/dataframe/dataframe_functions.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@ use datafusion::prelude::*;
use datafusion::execution::context::SessionContext;

use datafusion::assert_batches_eq;
use datafusion_expr::expr::Alias;
use datafusion_expr::{approx_median, cast};

async fn create_test_table() -> Result<DataFrame> {
Expand Down Expand Up @@ -186,6 +187,25 @@ async fn test_fn_approx_percentile_cont() -> Result<()> {

assert_batches_eq!(expected, &batches);

// the arg2 parameter is a complex expr, but it can be evaluated to the literal value
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think this may be related to aliasing rather than simply complex expressions

❯ create table t (x int) as values (1), (2);
0 rows in set. Query took 0.001 seconds.

❯ select APPROX_PERCENTILE_CONT(x,  cast(1 as double)) from t;
+--------------------------------------+
| APPROX_PERCENTILE_CONT(t.x,Int64(1)) |
+--------------------------------------+
| 2                                    |
+--------------------------------------+
1 row in set. Query took 0.002 seconds.

However, I couldn't express the same alias using SQL.

I wonder if the expression simplifier code could be generalized to handle this case, so it applied to all arguments, not just the arguments to APPROX_PERCENTILE_CONT 🤔

Copy link
Contributor Author

@liukun4515 liukun4515 Dec 20, 2023 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

yes, the rule of simplfy_expression can not handle the alisa expr.
But the cast(1 as float) will be simplified.
I think this is the diff between the cast and alisa

let alias_expr = Expr::Alias(Alias::new(
cast(lit(0.5), DataType::Float32),
None::<&str>,
"arg_2".to_string(),
));
let expr = approx_percentile_cont(col("b"), alias_expr);
let df = create_test_table().await?;
let expected = [
"+--------------------------------------+",
"| APPROX_PERCENTILE_CONT(test.b,arg_2) |",
"+--------------------------------------+",
"| 10 |",
"+--------------------------------------+",
];
let batches = df.aggregate(vec![], vec![expr]).unwrap().collect().await?;

assert_batches_eq!(expected, &batches);

Ok(())
}

Expand Down
45 changes: 21 additions & 24 deletions datafusion/physical-expr/src/aggregate/approx_percentile_cont.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
use crate::aggregate::tdigest::TryIntoF64;
use crate::aggregate::tdigest::{TDigest, DEFAULT_MAX_SIZE};
use crate::aggregate::utils::down_cast_any_ref;
use crate::expressions::{format_state_name, Literal};
use crate::expressions::format_state_name;
use crate::{AggregateExpr, PhysicalExpr};
use arrow::{
array::{
Expand All @@ -27,11 +27,13 @@ use arrow::{
},
datatypes::{DataType, Field},
};
use arrow_array::RecordBatch;
use arrow_schema::Schema;
use datafusion_common::{
downcast_value, exec_err, internal_err, not_impl_err, plan_err, DataFusionError,
Result, ScalarValue,
};
use datafusion_expr::Accumulator;
use datafusion_expr::{Accumulator, ColumnarValue};
use std::{any::Any, iter, sync::Arc};

/// APPROX_PERCENTILE_CONT aggregate expression
Expand Down Expand Up @@ -131,18 +133,22 @@ impl PartialEq for ApproxPercentileCont {
}
}

fn get_lit_value(expr: &Arc<dyn PhysicalExpr>) -> Result<ScalarValue> {
let empty_schema = Schema::empty();
let empty_batch = RecordBatch::new_empty(Arc::new(empty_schema));
let result = expr.evaluate(&empty_batch)?;
match result {
ColumnarValue::Array(_) => Err(DataFusionError::Internal(format!(
"The expr {:?} can't be evaluated to scalar value",
expr
))),
ColumnarValue::Scalar(scalar_value) => Ok(scalar_value),
}
}

fn validate_input_percentile_expr(expr: &Arc<dyn PhysicalExpr>) -> Result<f64> {
// Extract the desired percentile literal
let lit = expr
.as_any()
.downcast_ref::<Literal>()
.ok_or_else(|| {
DataFusionError::Internal(
"desired percentile argument must be float literal".to_string(),
)
})?
.value();
let percentile = match lit {
let lit = get_lit_value(expr)?;
let percentile = match &lit {
ScalarValue::Float32(Some(q)) => *q as f64,
ScalarValue::Float64(Some(q)) => *q,
got => return not_impl_err!(
Expand All @@ -161,17 +167,8 @@ fn validate_input_percentile_expr(expr: &Arc<dyn PhysicalExpr>) -> Result<f64> {
}

fn validate_input_max_size_expr(expr: &Arc<dyn PhysicalExpr>) -> Result<usize> {
// Extract the desired percentile literal
let lit = expr
.as_any()
.downcast_ref::<Literal>()
.ok_or_else(|| {
DataFusionError::Internal(
"desired percentile argument must be float literal".to_string(),
)
})?
.value();
let max_size = match lit {
let lit = get_lit_value(expr)?;
let max_size = match &lit {
ScalarValue::UInt8(Some(q)) => *q as usize,
ScalarValue::UInt16(Some(q)) => *q as usize,
ScalarValue::UInt32(Some(q)) => *q as usize,
Expand Down