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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2022/09/09 14:50:19 UTC
[GitHub] [arrow-datafusion] liukun4515 opened a new pull request, #3422: support cast/try_cast in prune
liukun4515 opened a new pull request, #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422
# Which issue does this PR close?
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Closes https://github.com/apache/arrow-datafusion/issues/3414
In order to merge this https://github.com/apache/arrow-datafusion/pull/3396, we should support the cast/try_cast in the prune first.
cc @andygrove @alamb
# Rationale for this change
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[GitHub] [arrow-datafusion] liukun4515 commented on pull request #3422: support cast/try_cast in prune
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1242600435
> Thank you @liukun4515 -- this is great
>
> I will look at this code in the morning as well, but I think we need to handle the case where casting won't preserve sortedness (aka UTF8 -> numeric), as described on #3377
I got your point.
We can add rule for cast/try_cast case.
For example, just support cast/try_cast from numeric to numeric data type, I think the data type of numeric has the order.
We can support other complex case in the future pr.
@alamb
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[GitHub] [arrow-datafusion] alamb commented on pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
alamb commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1242709855
To be explicit I think the code in this PR looks good to me, but I would like to see some actual tests with pruning expressions on data prior to merging it. I am happy to help write these tests too
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[GitHub] [arrow-datafusion] Dandandan commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
Dandandan commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r968035105
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -495,22 +500,30 @@ fn rewrite_expr_to_prunable(
}
match column_expr {
- // `col > lit()`
+ // col op lit()
Expr::Column(_) => Ok((column_expr.clone(), op, scalar_expr.clone())),
-
+ // `cast(col) op lit()`
+ Expr::Cast { expr, data_type } => {
+ let from_type = expr.get_type(&schema)?;
+ verify_support_type_for_prune(&from_type, data_type)?;
+ let (left, op, right) =
+ rewrite_expr_to_prunable(expr, op, scalar_expr, schema)?;
+ Ok((cast(left, data_type.clone()), op, right))
+ }
+ // `try_cast(col) op lit()`
+ Expr::TryCast { expr, data_type } => {
+ let from_type = expr.get_type(&schema)?;
+ verify_support_type_for_prune(&from_type, data_type)?;
+ let (left, op, right) =
+ rewrite_expr_to_prunable(expr, op, scalar_expr, schema)?;
+ Ok((try_cast(left, data_type.clone()), op, right))
+ }
+ // `col > lit()`
Review Comment:
This comment should be removed I think?
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[GitHub] [arrow-datafusion] liukun4515 commented on pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1242747814
> To be explicit I think the code in this PR looks good to me, but I would like to see some actual tests with pruning expressions on data prior to merging it. I am happy to help write these tests too
@alamb Thanks for you help.
It's too later for me to add test today, but welcome to add test for this pr, I can help to review your test and other changes tomorrow.
I will never do any changes before merged your test or your suggestion.
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967723715
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -552,6 +577,25 @@ fn is_compare_op(op: Operator) -> bool {
)
}
+// The pruning logic is based on the comparing the min/max bounds.
+// Must make sure the two type has order.
+// For example, casts from string to numbers is not correct.
+// Because the "13" is less than "3" with UTF8 comparison order.
+fn support_type_for_prune(from_type: &DataType, to_type: &DataType) -> bool {
+ // TODO: support other data type
Review Comment:
https://github.com/apache/arrow-datafusion/issues/3442
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[GitHub] [arrow-datafusion] alamb merged pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
alamb merged PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967617783
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1508,6 +1510,78 @@ mod tests {
Ok(())
}
+ #[test]
+ fn row_group_predicate_cast() -> Result<()> {
+ let schema = Schema::new(vec![Field::new("c1", DataType::Int32, false)]);
+ let expected_expr =
+ "CAST(#c1_min AS Int64) <= Int64(1) AND Int64(1) <= CAST(#c1_max AS Int64)";
+
+ // test column on the left
+ let expr = cast(col("c1"), DataType::Int64).eq(lit(ScalarValue::Int64(Some(1))));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ // test column on the right
+ let expr = lit(ScalarValue::Int64(Some(1))).eq(cast(col("c1"), DataType::Int64));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ let expected_expr = "TRY_CAST(#c1_max AS Int64) > Int64(1)";
+
+ // test column on the left
+ let expr =
+ try_cast(col("c1"), DataType::Int64).gt(lit(ScalarValue::Int64(Some(1))));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ // test column on the right
+ let expr =
+ lit(ScalarValue::Int64(Some(1))).lt(try_cast(col("c1"), DataType::Int64));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ Ok(())
+ }
+
+ #[test]
+ fn row_group_predicate_cast_list() -> Result<()> {
+ let schema = Schema::new(vec![Field::new("c1", DataType::Int32, false)]);
+ // test cast(c1 as int64) in int64(1, 2, 3)
+ let expr = Expr::InList {
+ expr: Box::new(cast(col("c1"), DataType::Int64)),
+ list: vec![
+ lit(ScalarValue::Int64(Some(1))),
+ lit(ScalarValue::Int64(Some(2))),
+ lit(ScalarValue::Int64(Some(3))),
+ ],
+ negated: false,
+ };
+ let expected_expr = "CAST(#c1_min AS Int64) <= Int64(1) AND Int64(1) <= CAST(#c1_max AS Int64) OR CAST(#c1_min AS Int64) <= Int64(2) AND Int64(2) <= CAST(#c1_max AS Int64) OR CAST(#c1_min AS Int64) <= Int64(3) AND Int64(3) <= CAST(#c1_max AS Int64)";
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ let expr = Expr::InList {
+ expr: Box::new(cast(col("c1"), DataType::Int64)),
+ list: vec![
+ lit(ScalarValue::Int64(Some(1))),
+ lit(ScalarValue::Int64(Some(2))),
+ lit(ScalarValue::Int64(Some(3))),
+ ],
+ negated: true,
+ };
+ let expected_expr = "CAST(#c1_min AS Int64) != Int64(1) OR Int64(1) != CAST(#c1_max AS Int64) AND CAST(#c1_min AS Int64) != Int64(2) OR Int64(2) != CAST(#c1_max AS Int64) AND CAST(#c1_min AS Int64) != Int64(3) OR Int64(3) != CAST(#c1_max AS Int64)";
Review Comment:
@andygrove I don't change any logical about the list.
you can get this from https://github.com/apache/arrow-datafusion/pull/2282#discussion_r855486677
I get the meanings from the code context.
if the min_c1 and max_c1 is 1, all the value in the column or chunk must be 1 or null, so the `c1 not in (1)` can skip this column or chunk.
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967722831
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -495,21 +501,40 @@ fn rewrite_expr_to_prunable(
}
match column_expr {
+ // col op lit()
+ Expr::Column(_) => Ok((column_expr.clone(), op, scalar_expr.clone())),
// `cast(col) op lit()`
Expr::Cast { expr, data_type } => {
- let (left, op, right) = rewrite_expr_to_prunable(expr, op, scalar_expr)?;
- Ok((cast(left, data_type.clone()), op, right))
+ let from_type = expr.get_type(&schema)?;
+ if support_type_for_prune(&from_type, data_type) {
+ let (left, op, right) =
+ rewrite_expr_to_prunable(expr, op, scalar_expr, schema)?;
+ Ok((cast(left, data_type.clone()), op, right))
+ } else {
+ return Err(DataFusionError::Plan(format!(
+ "Cast with from type {} to type {} is not supported",
+ from_type, data_type
+ )));
+ }
Review Comment:
good point
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[GitHub] [arrow-datafusion] liukun4515 commented on pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1242686512
cc @alamb
The cast/try_cast just support signed integer/decimal to signed integer/decimal.
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r968329166
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -495,22 +500,30 @@ fn rewrite_expr_to_prunable(
}
match column_expr {
- // `col > lit()`
+ // col op lit()
Expr::Column(_) => Ok((column_expr.clone(), op, scalar_expr.clone())),
-
+ // `cast(col) op lit()`
+ Expr::Cast { expr, data_type } => {
+ let from_type = expr.get_type(&schema)?;
+ verify_support_type_for_prune(&from_type, data_type)?;
+ let (left, op, right) =
+ rewrite_expr_to_prunable(expr, op, scalar_expr, schema)?;
+ Ok((cast(left, data_type.clone()), op, right))
+ }
+ // `try_cast(col) op lit()`
+ Expr::TryCast { expr, data_type } => {
+ let from_type = expr.get_type(&schema)?;
+ verify_support_type_for_prune(&from_type, data_type)?;
+ let (left, op, right) =
+ rewrite_expr_to_prunable(expr, op, scalar_expr, schema)?;
+ Ok((try_cast(left, data_type.clone()), op, right))
+ }
+ // `col > lit()`
Review Comment:
Done
removed
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[GitHub] [arrow-datafusion] ursabot commented on pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
ursabot commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1244085625
Benchmark runs are scheduled for baseline = 3b8a20a94eb12ec393072694e7e278e32223fcd6 and contender = 69d05aa0c563a478e28502c0a4e7822095859b28. 69d05aa0c563a478e28502c0a4e7822095859b28 is a master commit associated with this PR. Results will be available as each benchmark for each run completes.
Conbench compare runs links:
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on ec2-t3-xlarge-us-east-2] [ec2-t3-xlarge-us-east-2](https://conbench.ursa.dev/compare/runs/75e3f5a234134c4faf7cc5f4729cba24...d3378d8e9f614d2792e91881e045b411/)
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on test-mac-arm] [test-mac-arm](https://conbench.ursa.dev/compare/runs/d654ca62429040409c0a13bfdc2b11ad...54fe81f8bfa941e18efe03f0127d7c11/)
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on ursa-i9-9960x] [ursa-i9-9960x](https://conbench.ursa.dev/compare/runs/c54755ec9bbb47a99c4fbec4a4c22039...0c3b235735c7479095f2223a906e8ce3/)
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on ursa-thinkcentre-m75q] [ursa-thinkcentre-m75q](https://conbench.ursa.dev/compare/runs/5fa141748a2f4f46b1d8a2b4563d9678...ebdf8e35153641d78e06e2c2b3ad0da5/)
Buildkite builds:
Supported benchmarks:
ec2-t3-xlarge-us-east-2: Supported benchmark langs: Python, R. Runs only benchmarks with cloud = True
test-mac-arm: Supported benchmark langs: C++, Python, R
ursa-i9-9960x: Supported benchmark langs: Python, R, JavaScript
ursa-thinkcentre-m75q: Supported benchmark langs: C++, Java
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[GitHub] [arrow-datafusion] andygrove commented on a diff in pull request #3422: support cast/try_cast in prune
Posted by GitBox <gi...@apache.org>.
andygrove commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967425770
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1508,6 +1510,78 @@ mod tests {
Ok(())
}
+ #[test]
+ fn row_group_predicate_cast() -> Result<()> {
+ let schema = Schema::new(vec![Field::new("c1", DataType::Int32, false)]);
+ let expected_expr =
+ "CAST(#c1_min AS Int64) <= Int64(1) AND Int64(1) <= CAST(#c1_max AS Int64)";
+
+ // test column on the left
+ let expr = cast(col("c1"), DataType::Int64).eq(lit(ScalarValue::Int64(Some(1))));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ // test column on the right
+ let expr = lit(ScalarValue::Int64(Some(1))).eq(cast(col("c1"), DataType::Int64));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ let expected_expr = "TRY_CAST(#c1_max AS Int64) > Int64(1)";
+
+ // test column on the left
+ let expr =
+ try_cast(col("c1"), DataType::Int64).gt(lit(ScalarValue::Int64(Some(1))));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ // test column on the right
+ let expr =
+ lit(ScalarValue::Int64(Some(1))).lt(try_cast(col("c1"), DataType::Int64));
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ Ok(())
+ }
+
+ #[test]
+ fn row_group_predicate_cast_list() -> Result<()> {
+ let schema = Schema::new(vec![Field::new("c1", DataType::Int32, false)]);
+ // test cast(c1 as int64) in int64(1, 2, 3)
+ let expr = Expr::InList {
+ expr: Box::new(cast(col("c1"), DataType::Int64)),
+ list: vec![
+ lit(ScalarValue::Int64(Some(1))),
+ lit(ScalarValue::Int64(Some(2))),
+ lit(ScalarValue::Int64(Some(3))),
+ ],
+ negated: false,
+ };
+ let expected_expr = "CAST(#c1_min AS Int64) <= Int64(1) AND Int64(1) <= CAST(#c1_max AS Int64) OR CAST(#c1_min AS Int64) <= Int64(2) AND Int64(2) <= CAST(#c1_max AS Int64) OR CAST(#c1_min AS Int64) <= Int64(3) AND Int64(3) <= CAST(#c1_max AS Int64)";
+ let predicate_expr =
+ build_predicate_expression(&expr, &schema, &mut RequiredStatColumns::new())?;
+ assert_eq!(format!("{:?}", predicate_expr), expected_expr);
+
+ let expr = Expr::InList {
+ expr: Box::new(cast(col("c1"), DataType::Int64)),
+ list: vec![
+ lit(ScalarValue::Int64(Some(1))),
+ lit(ScalarValue::Int64(Some(2))),
+ lit(ScalarValue::Int64(Some(3))),
+ ],
+ negated: true,
+ };
+ let expected_expr = "CAST(#c1_min AS Int64) != Int64(1) OR Int64(1) != CAST(#c1_max AS Int64) AND CAST(#c1_min AS Int64) != Int64(2) OR Int64(2) != CAST(#c1_max AS Int64) AND CAST(#c1_min AS Int64) != Int64(3) OR Int64(3) != CAST(#c1_max AS Int64)";
Review Comment:
I'm not sure I understand what is happening with the negated case. Could you add a comment here explaining this? Why is there `!=` in here rather than `<=` or `>=`?
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[GitHub] [arrow-datafusion] codecov-commenter commented on pull request #3422: support cast/try_cast in prune
Posted by GitBox <gi...@apache.org>.
codecov-commenter commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1242140100
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@@ Coverage Diff @@
## master #3422 +/- ##
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Files 296 296
Lines 54252 54544 +292
==========================================
+ Hits 46432 46723 +291
- Misses 7820 7821 +1
```
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[GitHub] [arrow-datafusion] alamb commented on pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
alamb commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1244028826
Follow on PR is https://github.com/apache/arrow-datafusion/pull/3454
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967720763
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1995,4 +2039,66 @@ mod tests {
let result = p.prune(&statistics).unwrap();
assert_eq!(result, expected_ret);
}
+
+ #[test]
+ fn test_rewrite_expr_to_prunable() {
+ let schema = Schema::new(vec![Field::new("a", DataType::Int32, true)]);
+ let df_schema = DFSchema::try_from(schema).unwrap();
+ // column op lit
+ let left_input = col("a");
+ let right_input = lit(ScalarValue::Int32(Some(12)));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Eq,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // cast op lit
+ let left_input = cast(col("a"), DataType::Decimal128(20, 3));
+ let right_input = lit(ScalarValue::Decimal128(Some(12), 20, 3));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Gt,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // try_cast op lit
+ let left_input = try_cast(col("a"), DataType::Int64);
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let (result_left, _, result_right) =
+ rewrite_expr_to_prunable(&left_input, Operator::Gt, &right_input, df_schema)
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // TODO: add test for other case and op
Review Comment:
I find i have added the test for executing with real data in the `prune_cast_column_scalar` function.
You can check them, it just include cast(col, int64) and try_cast(col,int64). If you think it is not enough, you can add more test for it or give some advices for the test.
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967720852
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1995,4 +2039,66 @@ mod tests {
let result = p.prune(&statistics).unwrap();
assert_eq!(result, expected_ret);
}
+
+ #[test]
+ fn test_rewrite_expr_to_prunable() {
+ let schema = Schema::new(vec![Field::new("a", DataType::Int32, true)]);
+ let df_schema = DFSchema::try_from(schema).unwrap();
+ // column op lit
+ let left_input = col("a");
+ let right_input = lit(ScalarValue::Int32(Some(12)));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Eq,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // cast op lit
+ let left_input = cast(col("a"), DataType::Decimal128(20, 3));
+ let right_input = lit(ScalarValue::Decimal128(Some(12), 20, 3));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Gt,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // try_cast op lit
+ let left_input = try_cast(col("a"), DataType::Int64);
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let (result_left, _, result_right) =
+ rewrite_expr_to_prunable(&left_input, Operator::Gt, &right_input, df_schema)
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // TODO: add test for other case and op
Review Comment:
https://github.com/apache/arrow-datafusion/blob/4b1a4052d079c8149ad232a05e9965cceb05b5fc/datafusion/core/src/physical_optimizer/pruning.rs#L2011
https://github.com/apache/arrow-datafusion/blob/4b1a4052d079c8149ad232a05e9965cceb05b5fc/datafusion/core/src/physical_optimizer/pruning.rs#L2022
https://github.com/apache/arrow-datafusion/blob/4b1a4052d079c8149ad232a05e9965cceb05b5fc/datafusion/core/src/physical_optimizer/pruning.rs#L2028
FYI @alamb
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[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967721530
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -429,11 +429,16 @@ impl<'a> PruningExpressionBuilder<'a> {
}
};
- let (column_expr, correct_operator, scalar_expr) =
- match rewrite_expr_to_prunable(column_expr, correct_operator, scalar_expr) {
- Ok(ret) => ret,
- Err(e) => return Err(e),
- };
+ let df_schema = DFSchema::try_from(schema.clone())?;
+ let (column_expr, correct_operator, scalar_expr) = match rewrite_expr_to_prunable(
+ column_expr,
+ correct_operator,
+ scalar_expr,
+ df_schema,
+ ) {
+ Ok(ret) => ret,
+ Err(e) => return Err(e),
+ };
Review Comment:
update manually
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[GitHub] [arrow-datafusion] liukun4515 commented on pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
liukun4515 commented on PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#issuecomment-1244905928
Thanks for your careful review. @alamb
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[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #3422: support cast/try_cast in prune with signed integer and decimal
Posted by GitBox <gi...@apache.org>.
alamb commented on code in PR #3422:
URL: https://github.com/apache/arrow-datafusion/pull/3422#discussion_r967635359
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -487,6 +492,7 @@ fn rewrite_expr_to_prunable(
column_expr: &Expr,
op: Operator,
scalar_expr: &Expr,
+ schema: DFSchema,
Review Comment:
Could you please update the list of rules in the docstring of this function to include the new logic for casts?
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -552,6 +577,25 @@ fn is_compare_op(op: Operator) -> bool {
)
}
+// The pruning logic is based on the comparing the min/max bounds.
+// Must make sure the two type has order.
+// For example, casts from string to numbers is not correct.
+// Because the "13" is less than "3" with UTF8 comparison order.
+fn support_type_for_prune(from_type: &DataType, to_type: &DataType) -> bool {
+ // TODO: support other data type
Review Comment:
I think it would be good to file a follow on ticket listing the other types (you did so in https://github.com/apache/arrow-datafusion/issues/3414#issuecomment-1242659478)
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -429,11 +429,16 @@ impl<'a> PruningExpressionBuilder<'a> {
}
};
- let (column_expr, correct_operator, scalar_expr) =
- match rewrite_expr_to_prunable(column_expr, correct_operator, scalar_expr) {
- Ok(ret) => ret,
- Err(e) => return Err(e),
- };
+ let df_schema = DFSchema::try_from(schema.clone())?;
+ let (column_expr, correct_operator, scalar_expr) = match rewrite_expr_to_prunable(
+ column_expr,
+ correct_operator,
+ scalar_expr,
+ df_schema,
+ ) {
+ Ok(ret) => ret,
+ Err(e) => return Err(e),
+ };
Review Comment:
Very minor suggestion (can do this as a follow on too)
```suggestion
let (column_expr, correct_operator, scalar_expr) = rewrite_expr_to_prunable(
column_expr,
correct_operator,
scalar_expr,
df_schema,
)?;
```
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1995,4 +2039,66 @@ mod tests {
let result = p.prune(&statistics).unwrap();
assert_eq!(result, expected_ret);
}
+
+ #[test]
+ fn test_rewrite_expr_to_prunable() {
+ let schema = Schema::new(vec![Field::new("a", DataType::Int32, true)]);
+ let df_schema = DFSchema::try_from(schema).unwrap();
+ // column op lit
+ let left_input = col("a");
+ let right_input = lit(ScalarValue::Int32(Some(12)));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Eq,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // cast op lit
+ let left_input = cast(col("a"), DataType::Decimal128(20, 3));
+ let right_input = lit(ScalarValue::Decimal128(Some(12), 20, 3));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Gt,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // try_cast op lit
+ let left_input = try_cast(col("a"), DataType::Int64);
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let (result_left, _, result_right) =
+ rewrite_expr_to_prunable(&left_input, Operator::Gt, &right_input, df_schema)
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // TODO: add test for other case and op
Review Comment:
Rather than extending this test for other ops, I think we should add tests which actually run the pruning expressions on values (see my other comments).
Would it be helpful for me to write some more tests? I can do so tomorrow morning
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -552,6 +577,25 @@ fn is_compare_op(op: Operator) -> bool {
)
}
+// The pruning logic is based on the comparing the min/max bounds.
+// Must make sure the two type has order.
+// For example, casts from string to numbers is not correct.
Review Comment:
π thank you for these comments
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -495,21 +501,40 @@ fn rewrite_expr_to_prunable(
}
match column_expr {
+ // col op lit()
+ Expr::Column(_) => Ok((column_expr.clone(), op, scalar_expr.clone())),
// `cast(col) op lit()`
Expr::Cast { expr, data_type } => {
- let (left, op, right) = rewrite_expr_to_prunable(expr, op, scalar_expr)?;
- Ok((cast(left, data_type.clone()), op, right))
+ let from_type = expr.get_type(&schema)?;
+ if support_type_for_prune(&from_type, data_type) {
+ let (left, op, right) =
+ rewrite_expr_to_prunable(expr, op, scalar_expr, schema)?;
+ Ok((cast(left, data_type.clone()), op, right))
+ } else {
+ return Err(DataFusionError::Plan(format!(
+ "Cast with from type {} to type {} is not supported",
+ from_type, data_type
+ )));
+ }
Review Comment:
We could probably avoid the duplication of the error generation by moving the error return into `support_type_for_prune` like:
```suggestion
verify_support_type_for_prune(&from_type, data_type)?;
let (left, op, right) = rewrite_expr_to_prunable(expr, op, scalar_expr)?;
Ok((cast(left, data_type.clone()), op, right))
```
This is not needed for correctness, just a code structure suggestion
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1995,4 +2039,66 @@ mod tests {
let result = p.prune(&statistics).unwrap();
assert_eq!(result, expected_ret);
}
+
+ #[test]
+ fn test_rewrite_expr_to_prunable() {
+ let schema = Schema::new(vec![Field::new("a", DataType::Int32, true)]);
+ let df_schema = DFSchema::try_from(schema).unwrap();
+ // column op lit
+ let left_input = col("a");
+ let right_input = lit(ScalarValue::Int32(Some(12)));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Eq,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // cast op lit
+ let left_input = cast(col("a"), DataType::Decimal128(20, 3));
+ let right_input = lit(ScalarValue::Decimal128(Some(12), 20, 3));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Gt,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // try_cast op lit
+ let left_input = try_cast(col("a"), DataType::Int64);
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let (result_left, _, result_right) =
+ rewrite_expr_to_prunable(&left_input, Operator::Gt, &right_input, df_schema)
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // TODO: add test for other case and op
+ }
+
+ #[test]
+ fn test_rewrite_expr_to_prunable_error() {
+ // cast string value to numeric value
+ // this cast is not supported
+ let schema = Schema::new(vec![Field::new("a", DataType::Utf8, true)]);
+ let df_schema = DFSchema::try_from(schema).unwrap();
+ let left_input = cast(col("a"), DataType::Int64);
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let result = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Gt,
+ &right_input,
+ df_schema.clone(),
+ );
+ assert!(result.is_err());
+ // other expr
+ let left_input = is_null(col("a"));
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let result =
+ rewrite_expr_to_prunable(&left_input, Operator::Gt, &right_input, df_schema);
+ assert!(result.is_err());
+ }
}
Review Comment:
I think we should add some tests that exercise the pruning code with actual values too
For example, perhaps you can take `prune_int32_cast` from https://github.com/apache/arrow-datafusion/pull/3378?
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1995,4 +2039,66 @@ mod tests {
let result = p.prune(&statistics).unwrap();
assert_eq!(result, expected_ret);
}
+
+ #[test]
+ fn test_rewrite_expr_to_prunable() {
+ let schema = Schema::new(vec![Field::new("a", DataType::Int32, true)]);
+ let df_schema = DFSchema::try_from(schema).unwrap();
+ // column op lit
+ let left_input = col("a");
+ let right_input = lit(ScalarValue::Int32(Some(12)));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Eq,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // cast op lit
+ let left_input = cast(col("a"), DataType::Decimal128(20, 3));
+ let right_input = lit(ScalarValue::Decimal128(Some(12), 20, 3));
+ let (result_left, _, result_right) = rewrite_expr_to_prunable(
+ &left_input,
+ Operator::Gt,
+ &right_input,
+ df_schema.clone(),
+ )
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // try_cast op lit
+ let left_input = try_cast(col("a"), DataType::Int64);
+ let right_input = lit(ScalarValue::Int64(Some(12)));
+ let (result_left, _, result_right) =
+ rewrite_expr_to_prunable(&left_input, Operator::Gt, &right_input, df_schema)
+ .unwrap();
+ assert_eq!(result_left, left_input);
+ assert_eq!(result_right, right_input);
+ // TODO: add test for other case and op
+ }
+
+ #[test]
+ fn test_rewrite_expr_to_prunable_error() {
+ // cast string value to numeric value
+ // this cast is not supported
Review Comment:
π
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