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Posted to github@arrow.apache.org by "crepererum (via GitHub)" <gi...@apache.org> on 2023/02/27 15:11:32 UTC
[GitHub] [arrow-datafusion] crepererum opened a new pull request, #5419: refactor: ParquetExec logical expr. => phys. expr.
crepererum opened a new pull request, #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419
# Which issue does this PR close?
Closes #4695.
# Rationale for this change
Use `Arc<dyn PhysicalExpr>` instead of `Expr` within `ParquetExec` and move lowering from logical to physical expression into plan lowering (e.g. `ListingTable`).
This is in line w/ all other physical plan nodes (e.g. `FilterExpr`) and simplifies reasoning within physical optimizer but also allows correct passing of `ExecutionProps` into the conversion.
# What changes are included in this PR?
Basically a bunch of type changes. The tests still use logical expressions because they are easier to construct and its a simple conversion from logical to phys. (at least in the test cases).
# Are these changes tested?
All existing tests pass. Removed tests that no longer apply. Added one to demonstrate `PruningPredicate` behavior when no columns are referenced (see https://github.com/apache/arrow-datafusion/pull/5386#discussion_r1117641476 ).
# Are there any user-facing changes?
- Phys. plan printouts look slightly differently.
- **BREAKING:** Serialization has changed.
- **BREAKING:** `ParquetExec` arguments changed.
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[GitHub] [arrow-datafusion] alamb merged pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb merged PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419
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[GitHub] [arrow-datafusion] crepererum commented on pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#issuecomment-1446519622
I've also fixed a few test expressions that where buggy (or at least would require type coercion which would run on the logical plan), most notably:
- `negate(cast(some_int to UTF8))` => `cast(negate(some_int) to UTF8)`
- `some_bool.and/or(some_int % 2)` => `some_bool.and/or(some_int % 2 == 0)`
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[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1120449504
##########
datafusion/core/src/physical_plan/file_format/parquet/row_groups.rs:
##########
@@ -324,12 +329,15 @@ mod tests {
fn row_group_pruning_predicate_partial_expr() {
use datafusion_expr::{col, lit};
// test row group predicate with partially supported expression
- // int > 1 and int % 2 => c1_max > 1 and true
- let expr = col("c1").gt(lit(15)).and(col("c2").modulus(lit(2)));
+ // (int > 1) and ((int % 2) = 0) => c1_max > 1 and true
Review Comment:
thank you - this is a good change
##########
datafusion/physical-expr/src/utils.rs:
##########
@@ -235,6 +239,80 @@ pub fn ordering_satisfy_concrete<F: FnOnce() -> EquivalenceProperties>(
}
}
+/// Extract referenced [`Column`]s within a [`PhysicalExpr`].
+///
+/// This works recursively.
+pub fn get_phys_expr_columns(pred: &Arc<dyn PhysicalExpr>) -> HashSet<Column> {
+ let mut rewriter = ColumnCollector::default();
Review Comment:
👍 this is quite clever
##########
datafusion/core/src/datasource/file_format/mod.rs:
##########
@@ -84,7 +84,7 @@ pub trait FileFormat: Send + Sync + fmt::Debug {
&self,
state: &SessionState,
conf: FileScanConfig,
- filters: &[Expr],
+ filters: Option<&Arc<dyn PhysicalExpr>>,
Review Comment:
I think it is a good change to have this take a single `PhysicalExpr` rather than a slice as it is more consistent with `TableProvider::scan` 👍
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -187,7 +170,7 @@ impl PruningPredicate {
// in the predicate means we don't know for sure if the row
// group can be filtered out or not. To maintain correctness
// the row group must be kept and thus `true` is returned.
- match self.predicate_expr.evaluate(&statistics_batch)? {
+ match self.predicate_expr.evaluate(&statistics_batch).expect("bug") {
Review Comment:
I think it would be nicer here to return the error rather than panic, but I also think panic'ing is ok
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -794,51 +853,81 @@ fn build_predicate_expression(
build_statistics_expr(&mut expr_builder).unwrap_or(unhandled)
}
-fn build_statistics_expr(expr_builder: &mut PruningExpressionBuilder) -> Result<Expr> {
- let statistics_expr =
+fn build_statistics_expr(
+ expr_builder: &mut PruningExpressionBuilder,
+) -> Result<Arc<dyn PhysicalExpr>> {
+ let statistics_expr: Arc<dyn PhysicalExpr> =
match expr_builder.op() {
Operator::NotEq => {
// column != literal => (min, max) = literal =>
// !(min != literal && max != literal) ==>
// min != literal || literal != max
let min_column_expr = expr_builder.min_column_expr()?;
let max_column_expr = expr_builder.max_column_expr()?;
- min_column_expr
- .not_eq(expr_builder.scalar_expr().clone())
- .or(expr_builder.scalar_expr().clone().not_eq(max_column_expr))
+ Arc::new(phys_expr::BinaryExpr::new(
+ Arc::new(phys_expr::BinaryExpr::new(
+ min_column_expr,
+ Operator::NotEq,
+ expr_builder.scalar_expr().clone(),
+ )),
+ Operator::Or,
+ Arc::new(phys_expr::BinaryExpr::new(
+ expr_builder.scalar_expr().clone(),
+ Operator::NotEq,
+ max_column_expr,
+ )),
+ ))
}
Operator::Eq => {
// column = literal => (min, max) = literal => min <= literal && literal <= max
// (column / 2) = 4 => (column_min / 2) <= 4 && 4 <= (column_max / 2)
let min_column_expr = expr_builder.min_column_expr()?;
let max_column_expr = expr_builder.max_column_expr()?;
- min_column_expr
- .lt_eq(expr_builder.scalar_expr().clone())
- .and(expr_builder.scalar_expr().clone().lt_eq(max_column_expr))
+ Arc::new(phys_expr::BinaryExpr::new(
Review Comment:
it is unfortunate there isn't as nice "fluent" style APIs for building physical expressions. Maybe we can add a `PhysicalExprExt` trait that knew how to do so.
I'll file a ticket to track doing so as a follow on PR
##########
datafusion/core/src/physical_plan/file_format/parquet/row_filter.rs:
##########
@@ -211,16 +211,16 @@ impl<'a> FilterCandidateBuilder<'a> {
}
}
-impl<'a> ExprRewriter for FilterCandidateBuilder<'a> {
- fn pre_visit(&mut self, expr: &Expr) -> Result<RewriteRecursion> {
- if let Expr::Column(column) = expr {
- if let Ok(idx) = self.file_schema.index_of(&column.name) {
+impl<'a> TreeNodeRewriter<Arc<dyn PhysicalExpr>> for FilterCandidateBuilder<'a> {
Review Comment:
This is cool to see that there is an equivalent logical and physical rewriter
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -128,31 +126,16 @@ impl PruningPredicate {
/// For example, the filter expression `(column / 2) = 4` becomes
/// the pruning predicate
/// `(column_min / 2) <= 4 && 4 <= (column_max / 2))`
- pub fn try_new(expr: Expr, schema: SchemaRef) -> Result<Self> {
+ pub fn try_new(expr: Arc<dyn PhysicalExpr>, schema: SchemaRef) -> Result<Self> {
// build predicate expression once
let mut required_columns = RequiredStatColumns::new();
- let logical_predicate_expr =
+ let predicate_expr =
build_predicate_expression(&expr, schema.as_ref(), &mut required_columns);
- let stat_fields = required_columns
- .iter()
- .map(|(_, _, f)| f.clone())
- .collect::<Vec<_>>();
- let stat_schema = Schema::new(stat_fields);
- let stat_dfschema = DFSchema::try_from(stat_schema.clone())?;
-
- // TODO allow these properties to be passed in
Review Comment:
👍 now it uses the real thing . Nice!
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1840,12 +2034,34 @@ mod tests {
(schema, statistics, expected_true, expected_false)
}
+ #[test]
+ fn prune_bool_const_expr() {
+ let (schema, statistics, _, _) = bool_setup();
+
+ // true
+ let expr = lit(true);
+ let expr = logical2physical(&expr, &schema);
+ let p = PruningPredicate::try_new(expr, schema.clone()).unwrap();
+ let result = p.prune(&statistics).unwrap();
+ assert_eq!(result, vec![true, true, true, true, true]);
+
+ // false
+ // constant literals that do NOT refer to any columns are currently not evaluated at all, hence the result is
+ // "all true"
+ let expr = lit(false);
Review Comment:
this is unfortunate (an all false predicate should skip the entire file). However, as long as this PR doesn't make the pruning worse in this situation I think it is fine (and we can improve things in a follow on PR)
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[GitHub] [arrow-datafusion] crepererum commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1118884746
##########
datafusion/core/src/datasource/listing/table.rs:
##########
@@ -661,6 +663,20 @@ impl TableProvider for ListingTable {
})
.collect::<Result<Vec<_>>>()?;
+ let filters = if let Some(expr) = conjunction(filters.to_vec()) {
+ // NOTE: Use the table schema (NOT file schema) here because `expr` may contain references to partition columns.
+ let table_df_schema = self.table_schema.as_ref().clone().to_dfschema()?;
+ let filters = create_physical_expr(
+ &expr,
+ &table_df_schema,
+ &self.table_schema,
+ state.execution_props(),
Review Comment:
Uses the real `ExeuctionProps` now :)
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[GitHub] [arrow-datafusion] crepererum commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1120486601
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -187,7 +170,7 @@ impl PruningPredicate {
// in the predicate means we don't know for sure if the row
// group can be filtered out or not. To maintain correctness
// the row group must be kept and thus `true` is returned.
- match self.predicate_expr.evaluate(&statistics_batch)? {
+ match self.predicate_expr.evaluate(&statistics_batch).expect("bug") {
Review Comment:
ah, this is debug code, I HAVE TO remove this before merging.
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[GitHub] [arrow-datafusion] crepererum commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1118885925
##########
datafusion/core/src/physical_plan/file_format/parquet/row_filter.rs:
##########
@@ -391,36 +391,14 @@ pub fn build_row_filter(
mod test {
use super::*;
use arrow::datatypes::Field;
- use datafusion_expr::{cast, col, lit};
+ use datafusion_common::ToDFSchema;
+ use datafusion_expr::{cast, col, lit, Expr};
+ use datafusion_physical_expr::create_physical_expr;
+ use datafusion_physical_expr::execution_props::ExecutionProps;
use parquet::arrow::parquet_to_arrow_schema;
use parquet::file::reader::{FileReader, SerializedFileReader};
use rand::prelude::*;
- // Assume a column expression for a column not in the table schema is a projected column and ignore it
Review Comment:
This test is no longer relevant because you cannot lower such expression from logical to physical.
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[GitHub] [arrow-datafusion] jiangzhx commented on pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "jiangzhx (via GitHub)" <gi...@apache.org>.
jiangzhx commented on PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#issuecomment-1489851152
This feature is not implemented: Physical plan does not support logical expression EXISTS ()
https://github.com/apache/arrow-datafusion/issues/5789
version: 19.0.0 worked.
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[GitHub] [arrow-datafusion] ozankabak commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "ozankabak (via GitHub)" <gi...@apache.org>.
ozankabak commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1121812665
##########
datafusion/physical-expr/src/utils.rs:
##########
@@ -235,6 +239,80 @@ pub fn ordering_satisfy_concrete<F: FnOnce() -> EquivalenceProperties>(
}
}
+/// Extract referenced [`Column`]s within a [`PhysicalExpr`].
+///
+/// This works recursively.
+pub fn get_phys_expr_columns(pred: &Arc<dyn PhysicalExpr>) -> HashSet<Column> {
+ let mut rewriter = ColumnCollector::default();
Review Comment:
Yes, happy to help!
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[GitHub] [arrow-datafusion] crepererum commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1121397443
##########
datafusion/physical-expr/src/utils.rs:
##########
@@ -235,6 +239,80 @@ pub fn ordering_satisfy_concrete<F: FnOnce() -> EquivalenceProperties>(
}
}
+/// Extract referenced [`Column`]s within a [`PhysicalExpr`].
+///
+/// This works recursively.
+pub fn get_phys_expr_columns(pred: &Arc<dyn PhysicalExpr>) -> HashSet<Column> {
+ let mut rewriter = ColumnCollector::default();
Review Comment:
@ozankabak the issue w/ `Vec` is that you have a `O(n^2)` complexity in the number of used columns. In InfluxDB IOx we sometimes have schemas w/ over 200 columns and I'm somewhat worried that such a simple oversight quickly turns into a performance bug.
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[GitHub] [arrow-datafusion] crepererum commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1121398269
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -187,7 +170,7 @@ impl PruningPredicate {
// in the predicate means we don't know for sure if the row
// group can be filtered out or not. To maintain correctness
// the row group must be kept and thus `true` is returned.
- match self.predicate_expr.evaluate(&statistics_batch)? {
+ match self.predicate_expr.evaluate(&statistics_batch).expect("bug") {
Review Comment:
done
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[GitHub] [arrow-datafusion] crepererum commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "crepererum (via GitHub)" <gi...@apache.org>.
crepererum commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1120489517
##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1840,12 +2034,34 @@ mod tests {
(schema, statistics, expected_true, expected_false)
}
+ #[test]
+ fn prune_bool_const_expr() {
+ let (schema, statistics, _, _) = bool_setup();
+
+ // true
+ let expr = lit(true);
+ let expr = logical2physical(&expr, &schema);
+ let p = PruningPredicate::try_new(expr, schema.clone()).unwrap();
+ let result = p.prune(&statistics).unwrap();
+ assert_eq!(result, vec![true, true, true, true, true]);
+
+ // false
+ // constant literals that do NOT refer to any columns are currently not evaluated at all, hence the result is
+ // "all true"
+ let expr = lit(false);
Review Comment:
It's not making things worse. This was the state of the art even before #5386, but I was asked here https://github.com/apache/arrow-datafusion/pull/5386#discussion_r1117641476 for the concrete semantics and I couldn't find a test that already covered that, so I've added one.
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[GitHub] [arrow-datafusion] ozankabak commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "ozankabak (via GitHub)" <gi...@apache.org>.
ozankabak commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1120950514
##########
datafusion/physical-expr/src/utils.rs:
##########
@@ -235,6 +239,80 @@ pub fn ordering_satisfy_concrete<F: FnOnce() -> EquivalenceProperties>(
}
}
+/// Extract referenced [`Column`]s within a [`PhysicalExpr`].
+///
+/// This works recursively.
+pub fn get_phys_expr_columns(pred: &Arc<dyn PhysicalExpr>) -> HashSet<Column> {
+ let mut rewriter = ColumnCollector::default();
Review Comment:
Interesting! We had the same need (collecting columns) emerge in SHJ implementation, so we used this more lightweight recursion:
```rust
fn collect_columns_recursive(expr: &Arc<dyn PhysicalExpr>, columns: &mut Vec<Column>) {
if let Some(column) = expr.as_any().downcast_ref::<Column>() {
if !columns.iter().any(|c| c.eq(column)) {
columns.push(column.clone())
}
}
expr.children()
.iter()
.for_each(|e| collect_columns_recursive(e, columns))
}
fn collect_columns(expr: &Arc<dyn PhysicalExpr>) -> Vec<Column> {
let mut columns = vec![];
collect_columns_recursive(expr, &mut columns);
columns
}
```
We used a `Vec` instead of a `HashSet` due to anticipated small sizes, but the code is essentially the same 🙂
This makes me think that doing a comprehensive code review and collecting/coalescing/documenting utilities such as this may simplify the codebase, and could be a worthy pursuit.
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[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#discussion_r1121562818
##########
datafusion/physical-expr/src/utils.rs:
##########
@@ -235,6 +239,80 @@ pub fn ordering_satisfy_concrete<F: FnOnce() -> EquivalenceProperties>(
}
}
+/// Extract referenced [`Column`]s within a [`PhysicalExpr`].
+///
+/// This works recursively.
+pub fn get_phys_expr_columns(pred: &Arc<dyn PhysicalExpr>) -> HashSet<Column> {
+ let mut rewriter = ColumnCollector::default();
Review Comment:
> This makes me think that doing a comprehensive code review and collecting/coalescing/documenting utilities such as this may simplify the codebase, and could be a worthy pursuit.
This absolutely would be a worthy pursuit -- is that something you could help lead @ozankabak ?
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[GitHub] [arrow-datafusion] ursabot commented on pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "ursabot (via GitHub)" <gi...@apache.org>.
ursabot commented on PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#issuecomment-1450449643
Benchmark runs are scheduled for baseline = c4b895822a02d28d9adcd42112cc2604dde5d0e1 and contender = 03fbf9fecad00f8d6eb3e72e72ba16252b28b1d6. 03fbf9fecad00f8d6eb3e72e72ba16252b28b1d6 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/bfa8fdd2d8424bc09ccaf799adea02e6...ddab7ca7b40a4ea28d10bdf4c4b4221b/)
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on test-mac-arm] [test-mac-arm](https://conbench.ursa.dev/compare/runs/31dbe55bb88a430e9fef8d99eebb1424...7a7fd81653a04f5a8c6b328022315aa1/)
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on ursa-i9-9960x] [ursa-i9-9960x](https://conbench.ursa.dev/compare/runs/8c0736b67b344f5c9b61d50067c3d5c2...45c2f3426a3b4a7e8ceb19ed88f6c207/)
[Skipped :warning: Benchmarking of arrow-datafusion-commits is not supported on ursa-thinkcentre-m75q] [ursa-thinkcentre-m75q](https://conbench.ursa.dev/compare/runs/d8d611bb731845f0b68b821c442d81b9...94ce6c80d79b48f6b1bf11615ab5ee7b/)
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] alamb commented on pull request #5419: refactor: ParquetExec logical expr. => phys. expr.
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on PR #5419:
URL: https://github.com/apache/arrow-datafusion/pull/5419#issuecomment-1481807475
This PR appears to have introduced a regression -- see https://github.com/apache/arrow-datafusion/issues/5708
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