datafusion-optimizer 12.0.0

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//   http://www.apache.org/licenses/LICENSE-2.0
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use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
use datafusion_common::{DataFusionError, Result};
use datafusion_expr::{AggregateUDF, LogicalPlan, ScalarUDF, TableSource};
use datafusion_optimizer::common_subexpr_eliminate::CommonSubexprEliminate;
use datafusion_optimizer::decorrelate_where_exists::DecorrelateWhereExists;
use datafusion_optimizer::decorrelate_where_in::DecorrelateWhereIn;
use datafusion_optimizer::eliminate_filter::EliminateFilter;
use datafusion_optimizer::eliminate_limit::EliminateLimit;
use datafusion_optimizer::filter_null_join_keys::FilterNullJoinKeys;
use datafusion_optimizer::filter_push_down::FilterPushDown;
use datafusion_optimizer::limit_push_down::LimitPushDown;
use datafusion_optimizer::optimizer::Optimizer;
use datafusion_optimizer::pre_cast_lit_in_comparison::PreCastLitInComparisonExpressions;
use datafusion_optimizer::projection_push_down::ProjectionPushDown;
use datafusion_optimizer::reduce_outer_join::ReduceOuterJoin;
use datafusion_optimizer::rewrite_disjunctive_predicate::RewriteDisjunctivePredicate;
use datafusion_optimizer::scalar_subquery_to_join::ScalarSubqueryToJoin;
use datafusion_optimizer::simplify_expressions::SimplifyExpressions;
use datafusion_optimizer::single_distinct_to_groupby::SingleDistinctToGroupBy;
use datafusion_optimizer::subquery_filter_to_join::SubqueryFilterToJoin;
use datafusion_optimizer::type_coercion::TypeCoercion;
use datafusion_optimizer::{OptimizerConfig, OptimizerRule};
use datafusion_sql::planner::{ContextProvider, SqlToRel};
use datafusion_sql::sqlparser::ast::Statement;
use datafusion_sql::sqlparser::dialect::GenericDialect;
use datafusion_sql::sqlparser::parser::Parser;
use datafusion_sql::TableReference;
use std::any::Any;
use std::collections::HashMap;
use std::sync::Arc;

#[test]
fn distribute_by() -> Result<()> {
    // regression test for https://github.com/apache/arrow-datafusion/issues/3234
    let sql = "SELECT col_int32, col_utf8 FROM test DISTRIBUTE BY (col_utf8)";
    let plan = test_sql(sql)?;
    let expected = "Repartition: DistributeBy(#col_utf8)\
    \n  Projection: #test.col_int32, #test.col_utf8\
    \n    TableScan: test projection=[col_int32, col_utf8]";
    assert_eq!(expected, format!("{:?}", plan));
    Ok(())
}

#[test]
fn intersect() -> Result<()> {
    let sql = "SELECT col_int32, col_utf8 FROM test \
    INTERSECT SELECT col_int32, col_utf8 FROM test \
    INTERSECT SELECT col_int32, col_utf8 FROM test";
    let plan = test_sql(sql)?;
    let expected =
        "Semi Join: #test.col_int32 = #test.col_int32, #test.col_utf8 = #test.col_utf8\
    \n  Distinct:\
    \n    Semi Join: #test.col_int32 = #test.col_int32, #test.col_utf8 = #test.col_utf8\
    \n      Distinct:\
    \n        TableScan: test projection=[col_int32, col_utf8]\
    \n      TableScan: test projection=[col_int32, col_utf8]\
    \n  TableScan: test projection=[col_int32, col_utf8]";
    assert_eq!(expected, format!("{:?}", plan));
    Ok(())
}

#[test]
fn between_date32_plus_interval() -> Result<()> {
    let sql = "SELECT count(1) FROM test \
    WHERE col_date32 between '1998-03-18' AND cast('1998-03-18' as date) + INTERVAL '90 days'";
    let plan = test_sql(sql)?;
    let expected =
        "Projection: #COUNT(UInt8(1))\n  Aggregate: groupBy=[[]], aggr=[[COUNT(UInt8(1))]]\
        \n    Filter: #test.col_date32 >= CAST(Utf8(\"1998-03-18\") AS Date32) AND #test.col_date32 <= Date32(\"10393\")\
        \n      TableScan: test projection=[col_date32]";
    assert_eq!(expected, format!("{:?}", plan));
    Ok(())
}

#[test]
fn between_date64_plus_interval() -> Result<()> {
    let sql = "SELECT count(1) FROM test \
    WHERE col_date64 between '1998-03-18' AND cast('1998-03-18' as date) + INTERVAL '90 days'";
    let plan = test_sql(sql)?;
    let expected =
        "Projection: #COUNT(UInt8(1))\n  Aggregate: groupBy=[[]], aggr=[[COUNT(UInt8(1))]]\
        \n    Filter: #test.col_date64 >= CAST(Utf8(\"1998-03-18\") AS Date64) AND #test.col_date64 <= CAST(Date32(\"10393\") AS Date64)\
        \n      TableScan: test projection=[col_date64]";
    assert_eq!(expected, format!("{:?}", plan));
    Ok(())
}

fn test_sql(sql: &str) -> Result<LogicalPlan> {
    let rules: Vec<Arc<dyn OptimizerRule + Sync + Send>> = vec![
        // Simplify expressions first to maximize the chance
        // of applying other optimizations
        Arc::new(SimplifyExpressions::new()),
        Arc::new(PreCastLitInComparisonExpressions::new()),
        Arc::new(DecorrelateWhereExists::new()),
        Arc::new(DecorrelateWhereIn::new()),
        Arc::new(ScalarSubqueryToJoin::new()),
        Arc::new(SubqueryFilterToJoin::new()),
        Arc::new(EliminateFilter::new()),
        Arc::new(CommonSubexprEliminate::new()),
        Arc::new(EliminateLimit::new()),
        Arc::new(ProjectionPushDown::new()),
        Arc::new(RewriteDisjunctivePredicate::new()),
        Arc::new(FilterNullJoinKeys::default()),
        Arc::new(ReduceOuterJoin::new()),
        Arc::new(FilterPushDown::new()),
        Arc::new(TypeCoercion::new()),
        Arc::new(LimitPushDown::new()),
        Arc::new(SingleDistinctToGroupBy::new()),
    ];

    let optimizer = Optimizer::new(rules);

    // parse the SQL
    let dialect = GenericDialect {}; // or AnsiDialect, or your own dialect ...
    let ast: Vec<Statement> = Parser::parse_sql(&dialect, sql).unwrap();
    let statement = &ast[0];

    // create a logical query plan
    let schema_provider = MySchemaProvider {};
    let sql_to_rel = SqlToRel::new(&schema_provider);
    let plan = sql_to_rel.sql_statement_to_plan(statement.clone()).unwrap();

    // optimize the logical plan
    let mut config = OptimizerConfig::new().with_skip_failing_rules(false);
    optimizer.optimize(&plan, &mut config, &observe)
}

struct MySchemaProvider {}

impl ContextProvider for MySchemaProvider {
    fn get_table_provider(
        &self,
        name: TableReference,
    ) -> datafusion_common::Result<Arc<dyn TableSource>> {
        let table_name = name.table();
        if table_name.starts_with("test") {
            let schema = Schema::new_with_metadata(
                vec![
                    Field::new("col_int32", DataType::Int32, true),
                    Field::new("col_utf8", DataType::Utf8, true),
                    Field::new("col_date32", DataType::Date32, true),
                    Field::new("col_date64", DataType::Date64, true),
                ],
                HashMap::new(),
            );

            Ok(Arc::new(MyTableSource {
                schema: Arc::new(schema),
            }))
        } else {
            Err(DataFusionError::Plan("table does not exist".to_string()))
        }
    }

    fn get_function_meta(&self, _name: &str) -> Option<Arc<ScalarUDF>> {
        None
    }

    fn get_aggregate_meta(&self, _name: &str) -> Option<Arc<AggregateUDF>> {
        None
    }

    fn get_variable_type(&self, _variable_names: &[String]) -> Option<DataType> {
        None
    }
}

fn observe(_plan: &LogicalPlan, _rule: &dyn OptimizerRule) {}

struct MyTableSource {
    schema: SchemaRef,
}

impl TableSource for MyTableSource {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn schema(&self) -> SchemaRef {
        self.schema.clone()
    }
}