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[GitHub] [arrow-datafusion] HaoYang670 opened a new pull request #1734: Create built-in scalar functions programmatically
HaoYang670 opened a new pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734
# Which issue does this PR close?
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Closes #1718.
Re #1727.
# Rationale for this change
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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.
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We create a cleaner way to call built-in scalar functions by calling `call_builtin_scalar_fn`.
# What changes are included in this PR?
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# Are there any user-facing changes?
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A new function `call_builtin_scalar_fn`
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[GitHub] [arrow-datafusion] HaoYang670 commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
HaoYang670 commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r800000256
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
Thank you @Jimexist !
And alamb 's opinion of the trade-off between macro and function call is here:
https://github.com/apache/arrow-datafusion/issues/1718#issuecomment-1026239024
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[GitHub] [arrow-datafusion] HaoYang670 commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
HaoYang670 commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r800000256
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
Agree with you @Jimexist !
And alamb 's opinion of the trade-off between macro and function call is here:
https://github.com/apache/arrow-datafusion/issues/1718#issuecomment-1026239024
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[GitHub] [arrow-datafusion] alamb commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
alamb commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r798933169
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
One way to write this more idiomatically is
```suggestion
name.as_ref().parse::<functions::BuiltinScalarFunction>()
.map(|fun| Expr::ScalarFunction { fun, args }),
}
```
(not required, I am just pointing it out because it took me a while to get my head around working with Option and Results, and we are all learning together)
##########
File path: datafusion/src/logical_plan/mod.rs
##########
@@ -37,17 +37,17 @@ pub use dfschema::{DFField, DFSchema, DFSchemaRef, ToDFSchema};
pub use display::display_schema;
pub use expr::{
abs, acos, and, approx_distinct, approx_percentile_cont, array, ascii, asin, atan,
- avg, binary_expr, bit_length, btrim, case, ceil, character_length, chr, col,
- columnize_expr, combine_filters, concat, concat_ws, cos, count, count_distinct,
- create_udaf, create_udf, date_part, date_trunc, digest, exp, exprlist_to_fields,
- floor, in_list, initcap, left, length, lit, lit_timestamp_nano, ln, log10, log2,
- lower, lpad, ltrim, max, md5, min, normalize_col, normalize_cols, now, octet_length,
- or, random, regexp_match, regexp_replace, repeat, replace, replace_col, reverse,
- rewrite_sort_cols_by_aggs, right, round, rpad, rtrim, sha224, sha256, sha384, sha512,
- signum, sin, split_part, sqrt, starts_with, strpos, substr, sum, tan, to_hex,
- translate, trim, trunc, unalias, unnormalize_col, unnormalize_cols, upper, when,
- Column, Expr, ExprRewriter, ExpressionVisitor, Literal, Recursion, RewriteRecursion,
- SimplifyInfo,
+ avg, binary_expr, bit_length, btrim, call_builtin_scalar_fn, case, ceil,
Review comment:
Seeing all these other names, what would you think of changing the name from `call_builtin_scalar_fn` to `call_fn` to make it less verbose 🤔
##########
File path: datafusion/src/optimizer/simplify_expressions.rs
##########
@@ -1010,46 +1010,34 @@ mod tests {
#[test]
fn test_const_evaluator_scalar_functions() {
// concat("foo", "bar") --> "foobar"
- let expr = Expr::ScalarFunction {
- args: vec![lit("foo"), lit("bar")],
- fun: BuiltinScalarFunction::Concat,
- };
+ let expr =
Review comment:
well that is certainly nicer looking 👍
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[GitHub] [arrow-datafusion] HaoYang670 commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
HaoYang670 commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r800338088
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
Is it needed to implement both?
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[GitHub] [arrow-datafusion] HaoYang670 commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
HaoYang670 commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r800370911
##########
File path: datafusion/src/logical_plan/mod.rs
##########
@@ -37,17 +37,17 @@ pub use dfschema::{DFField, DFSchema, DFSchemaRef, ToDFSchema};
pub use display::display_schema;
pub use expr::{
abs, acos, and, approx_distinct, approx_percentile_cont, array, ascii, asin, atan,
- avg, binary_expr, bit_length, btrim, case, ceil, character_length, chr, col,
- columnize_expr, combine_filters, concat, concat_ws, cos, count, count_distinct,
- create_udaf, create_udf, date_part, date_trunc, digest, exp, exprlist_to_fields,
- floor, in_list, initcap, left, length, lit, lit_timestamp_nano, ln, log10, log2,
- lower, lpad, ltrim, max, md5, min, normalize_col, normalize_cols, now, octet_length,
- or, random, regexp_match, regexp_replace, repeat, replace, replace_col, reverse,
- rewrite_sort_cols_by_aggs, right, round, rpad, rtrim, sha224, sha256, sha384, sha512,
- signum, sin, split_part, sqrt, starts_with, strpos, substr, sum, tan, to_hex,
- translate, trim, trunc, unalias, unnormalize_col, unnormalize_cols, upper, when,
- Column, Expr, ExprRewriter, ExpressionVisitor, Literal, Recursion, RewriteRecursion,
- SimplifyInfo,
+ avg, binary_expr, bit_length, btrim, call_builtin_scalar_fn, case, ceil,
Review comment:
Thank you, I will change it
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[GitHub] [arrow-datafusion] alamb commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
alamb commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r800584834
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
No I don't think so. If we want a macro we can always add that as a follow on PR.
Thanks @HaoYang670 !
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[GitHub] [arrow-datafusion] Jimexist commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
Jimexist commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r799970574
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
i wonder if it makes sense to make it a macro rather than a function call so that nonexisteng built-in functions will be caught during compile time not runtime
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[GitHub] [arrow-datafusion] houqp commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
houqp commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r799992435
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
I agree, `let expr = call_builtin_scalar_fn!(ToTimestamp, vec![lit("2020-09-08T12:00:00+00:00")])` is as readable.
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[GitHub] [arrow-datafusion] alamb merged pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
alamb merged pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734
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[GitHub] [arrow-datafusion] houqp commented on a change in pull request #1734: Create built-in scalar functions programmatically
Posted by GitBox <gi...@apache.org>.
houqp commented on a change in pull request #1734:
URL: https://github.com/apache/arrow-datafusion/pull/1734#discussion_r799992435
##########
File path: datafusion/src/logical_plan/expr.rs
##########
@@ -2189,6 +2189,20 @@ pub fn exprlist_to_fields<'a>(
expr.into_iter().map(|e| e.to_field(input_schema)).collect()
}
+/// Calls a named built in function
+/// ```
+/// use datafusion::logical_plan::*;
+///
+/// // create the expression sin(x) < 0.2
+/// let expr = call_builtin_scalar_fn("sin", vec![col("x")]).unwrap().lt(lit(0.2));
+/// ```
+pub fn call_builtin_scalar_fn(name: impl AsRef<str>, args: Vec<Expr>) -> Result<Expr> {
+ match name.as_ref().parse::<functions::BuiltinScalarFunction>() {
+ Ok(fun) => Ok(Expr::ScalarFunction { fun, args }),
+ Err(e) => Err(e),
+ }
Review comment:
I agree, `call_builtin_scalar_fn!(ToTimestamp, vec![lit("2020-09-08T12:00:00+00:00")])` is as readable.
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