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consolidate physical_optimizer tests into core/tests/physical_optimizer #14244

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322 changes: 322 additions & 0 deletions datafusion/core/tests/physical_optimizer/aggregate_statistics.rs
Original file line number Diff line number Diff line change
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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.

use datafusion_common::config::ConfigOptions;
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Moved into its own file for consistency. If people like this pattern I can do it for the other tests too

use datafusion_execution::TaskContext;
use datafusion_physical_optimizer::aggregate_statistics::AggregateStatistics;
use datafusion_physical_optimizer::PhysicalOptimizerRule;
use datafusion_physical_plan::aggregates::AggregateExec;
use datafusion_physical_plan::projection::ProjectionExec;
use datafusion_physical_plan::ExecutionPlan;
use std::sync::Arc;

use datafusion_common::Result;

use datafusion_physical_plan::aggregates::PhysicalGroupBy;
use datafusion_physical_plan::coalesce_partitions::CoalescePartitionsExec;
use datafusion_physical_plan::common;
use datafusion_physical_plan::filter::FilterExec;
use datafusion_physical_plan::memory::MemoryExec;

use arrow::array::Int32Array;
use arrow::datatypes::{DataType, Field, Schema};
use arrow::record_batch::RecordBatch;
use datafusion_common::cast::as_int64_array;
use datafusion_expr::Operator;
use datafusion_physical_expr::expressions::{self, cast};
use datafusion_physical_optimizer::test_utils::TestAggregate;
use datafusion_physical_plan::aggregates::AggregateMode;

/// Mock data using a MemoryExec which has an exact count statistic
fn mock_data() -> Result<Arc<MemoryExec>> {
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, true),
Field::new("b", DataType::Int32, true),
]));

let batch = RecordBatch::try_new(
Arc::clone(&schema),
vec![
Arc::new(Int32Array::from(vec![Some(1), Some(2), None])),
Arc::new(Int32Array::from(vec![Some(4), None, Some(6)])),
],
)?;

Ok(Arc::new(MemoryExec::try_new(
&[vec![batch]],
Arc::clone(&schema),
None,
)?))
}

/// Checks that the count optimization was applied and we still get the right result
async fn assert_count_optim_success(
plan: AggregateExec,
agg: TestAggregate,
) -> Result<()> {
let task_ctx = Arc::new(TaskContext::default());
let plan: Arc<dyn ExecutionPlan> = Arc::new(plan);

let config = ConfigOptions::new();
let optimized = AggregateStatistics::new().optimize(Arc::clone(&plan), &config)?;

// A ProjectionExec is a sign that the count optimization was applied
assert!(optimized.as_any().is::<ProjectionExec>());

// run both the optimized and nonoptimized plan
let optimized_result =
common::collect(optimized.execute(0, Arc::clone(&task_ctx))?).await?;
let nonoptimized_result = common::collect(plan.execute(0, task_ctx)?).await?;
assert_eq!(optimized_result.len(), nonoptimized_result.len());

// and validate the results are the same and expected
assert_eq!(optimized_result.len(), 1);
check_batch(optimized_result.into_iter().next().unwrap(), &agg);
// check the non optimized one too to ensure types and names remain the same
assert_eq!(nonoptimized_result.len(), 1);
check_batch(nonoptimized_result.into_iter().next().unwrap(), &agg);

Ok(())
}

fn check_batch(batch: RecordBatch, agg: &TestAggregate) {
let schema = batch.schema();
let fields = schema.fields();
assert_eq!(fields.len(), 1);

let field = &fields[0];
assert_eq!(field.name(), agg.column_name());
assert_eq!(field.data_type(), &DataType::Int64);
// note that nullability differs

assert_eq!(
as_int64_array(batch.column(0)).unwrap().values(),
&[agg.expected_count()]
);
}

#[tokio::test]
async fn test_count_partial_direct_child() -> Result<()> {
// basic test case with the aggregation applied on a source with exact statistics
let source = mock_data()?;
let schema = source.schema();
let agg = TestAggregate::new_count_star();

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
source,
Arc::clone(&schema),
)?;

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
Arc::new(partial_agg),
Arc::clone(&schema),
)?;

assert_count_optim_success(final_agg, agg).await?;

Ok(())
}

#[tokio::test]
async fn test_count_partial_with_nulls_direct_child() -> Result<()> {
// basic test case with the aggregation applied on a source with exact statistics
let source = mock_data()?;
let schema = source.schema();
let agg = TestAggregate::new_count_column(&schema);

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
source,
Arc::clone(&schema),
)?;

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
Arc::new(partial_agg),
Arc::clone(&schema),
)?;

assert_count_optim_success(final_agg, agg).await?;

Ok(())
}

#[tokio::test]
async fn test_count_partial_indirect_child() -> Result<()> {
let source = mock_data()?;
let schema = source.schema();
let agg = TestAggregate::new_count_star();

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
source,
Arc::clone(&schema),
)?;

// We introduce an intermediate optimization step between the partial and final aggregator
let coalesce = CoalescePartitionsExec::new(Arc::new(partial_agg));

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
Arc::new(coalesce),
Arc::clone(&schema),
)?;

assert_count_optim_success(final_agg, agg).await?;

Ok(())
}

#[tokio::test]
async fn test_count_partial_with_nulls_indirect_child() -> Result<()> {
let source = mock_data()?;
let schema = source.schema();
let agg = TestAggregate::new_count_column(&schema);

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
source,
Arc::clone(&schema),
)?;

// We introduce an intermediate optimization step between the partial and final aggregator
let coalesce = CoalescePartitionsExec::new(Arc::new(partial_agg));

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
Arc::new(coalesce),
Arc::clone(&schema),
)?;

assert_count_optim_success(final_agg, agg).await?;

Ok(())
}

#[tokio::test]
async fn test_count_inexact_stat() -> Result<()> {
let source = mock_data()?;
let schema = source.schema();
let agg = TestAggregate::new_count_star();

// adding a filter makes the statistics inexact
let filter = Arc::new(FilterExec::try_new(
expressions::binary(
expressions::col("a", &schema)?,
Operator::Gt,
cast(expressions::lit(1u32), &schema, DataType::Int32)?,
&schema,
)?,
source,
)?);

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
filter,
Arc::clone(&schema),
)?;

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
Arc::new(partial_agg),
Arc::clone(&schema),
)?;

let conf = ConfigOptions::new();
let optimized = AggregateStatistics::new().optimize(Arc::new(final_agg), &conf)?;

// check that the original ExecutionPlan was not replaced
assert!(optimized.as_any().is::<AggregateExec>());

Ok(())
}

#[tokio::test]
async fn test_count_with_nulls_inexact_stat() -> Result<()> {
let source = mock_data()?;
let schema = source.schema();
let agg = TestAggregate::new_count_column(&schema);

// adding a filter makes the statistics inexact
let filter = Arc::new(FilterExec::try_new(
expressions::binary(
expressions::col("a", &schema)?,
Operator::Gt,
cast(expressions::lit(1u32), &schema, DataType::Int32)?,
&schema,
)?,
source,
)?);

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
filter,
Arc::clone(&schema),
)?;

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(agg.count_expr(&schema))],
vec![None],
Arc::new(partial_agg),
Arc::clone(&schema),
)?;

let conf = ConfigOptions::new();
let optimized = AggregateStatistics::new().optimize(Arc::new(final_agg), &conf)?;

// check that the original ExecutionPlan was not replaced
assert!(optimized.as_any().is::<AggregateExec>());

Ok(())
}
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