Introduce expr builder for aggregate function

datafusion-examples/examples/expr_api.rs

@@ -24,6 +24,7 @@ use arrow::record_batch::RecordBatch;
 use datafusion::arrow::datatypes::{DataType, Field, Schema, TimeUnit};
 use datafusion::common::DFSchema;
 use datafusion::error::Result;
+use datafusion::functions_aggregate::first_last::first_value_udaf;
 use datafusion::optimizer::simplify_expressions::ExprSimplifier;
 use datafusion::physical_expr::{analyze, AnalysisContext, ExprBoundaries};
 use datafusion::prelude::*;
@@ -32,7 +33,7 @@ use datafusion_expr::execution_props::ExecutionProps;
 use datafusion_expr::expr::BinaryExpr;
 use datafusion_expr::interval_arithmetic::Interval;
 use datafusion_expr::simplify::SimplifyContext;
-use datafusion_expr::{ColumnarValue, ExprSchemable, Operator};
+use datafusion_expr::{AggregateExt, ColumnarValue, ExprSchemable, Operator};
 
 /// This example demonstrates the DataFusion [`Expr`] API.
 ///
@@ -44,11 +45,12 @@ use datafusion_expr::{ColumnarValue, ExprSchemable, Operator};
 /// also comes with APIs for evaluation, simplification, and analysis.
 ///
 /// The code in this example shows how to:
-/// 1. Create [`Exprs`] using different APIs: [`main`]`
-/// 2. Evaluate [`Exprs`] against data: [`evaluate_demo`]
-/// 3. Simplify expressions: [`simplify_demo`]
-/// 4. Analyze predicates for boundary ranges: [`range_analysis_demo`]
-/// 5. Get the types of the expressions: [`expression_type_demo`]
+/// 1. Create [`Expr`]s using different APIs: [`main`]`
+/// 2. Use the fluent API to easly create complex [`Expr`]s:  [`expr_fn_demo`]
+/// 3. Evaluate [`Expr`]s against data: [`evaluate_demo`]
+/// 4. Simplify expressions: [`simplify_demo`]
+/// 5. Analyze predicates for boundary ranges: [`range_analysis_demo`]
+/// 6. Get the types of the expressions: [`expression_type_demo`]
 #[tokio::main]
 async fn main() -> Result<()> {
     // The easiest way to do create expressions is to use the
@@ -63,6 +65,9 @@ async fn main() -> Result<()> {
     ));
     assert_eq!(expr, expr2);
 
+    // See how to build aggregate functions with the expr_fn API
+    expr_fn_demo()?;
+
     // See how to evaluate expressions
     evaluate_demo()?;
 
@@ -78,6 +83,33 @@ async fn main() -> Result<()> {
     Ok(())
 }
 
+/// Datafusion's `expr_fn` API makes it easy to create [`Expr`]s for the
+/// full range of expression types such as aggregates and window functions.
+fn expr_fn_demo() -> Result<()> {
+    // Let's say you want to call the "first_value" aggregate function
+    let first_value = first_value_udaf();
+
+    // For example, to create the expression `FIRST_VALUE(price)`
+    // These expressions can be passed to `DataFrame::aggregate` and other
+    // APIs that take aggregate expressions.
+    let agg = first_value.call(vec![col("price")]);
+    assert_eq!(agg.to_string(), "first_value(price)");
+
+    // You can use the AggregateExt trait to create more complex aggregates
+    // such as `FIRST_VALUE(price FILTER quantity > 100 ORDER BY ts )
+    let agg = first_value
+        .call(vec![col("price")])
+        .order_by(vec![col("ts").sort(false, false)])
+        .filter(col("quantity").gt(lit(100)))
+        .build()?; // build the aggregate
+    assert_eq!(
+        agg.to_string(),
+        "first_value(price) FILTER (WHERE quantity > Int32(100)) ORDER BY [ts DESC NULLS LAST]"
+    );
+
+    Ok(())
+}
+
 /// DataFusion can also evaluate arbitrary expressions on Arrow arrays.
 fn evaluate_demo() -> Result<()> {
     // For example, let's say you have some integers in an array

datafusion/core/tests/expr_api/mod.rs

@@ -15,14 +15,18 @@
 // specific language governing permissions and limitations
 // under the License.
 
-use arrow::util::pretty::pretty_format_columns;
+use arrow::util::pretty::{pretty_format_batches, pretty_format_columns};
 use arrow_array::builder::{ListBuilder, StringBuilder};
-use arrow_array::{ArrayRef, RecordBatch, StringArray, StructArray};
+use arrow_array::{ArrayRef, Int64Array, RecordBatch, StringArray, StructArray};
 use arrow_schema::{DataType, Field};
 use datafusion::prelude::*;
-use datafusion_common::{DFSchema, ScalarValue};
+use datafusion_common::{assert_contains, DFSchema, ScalarValue};
+use datafusion_expr::AggregateExt;
 use datafusion_functions::core::expr_ext::FieldAccessor;
+use datafusion_functions_aggregate::first_last::first_value_udaf;
+use datafusion_functions_aggregate::sum::sum_udaf;
 use datafusion_functions_array::expr_ext::{IndexAccessor, SliceAccessor};
+use sqlparser::ast::NullTreatment;
 /// Tests of using and evaluating `Expr`s outside the context of a LogicalPlan
 use std::sync::{Arc, OnceLock};
 
@@ -162,6 +166,183 @@ fn test_list_range() {
     );
 }
 
+#[tokio::test]
+async fn test_aggregate_error() {
+    let err = first_value_udaf()
+        .call(vec![col("props")])
+        // not a sort column
+        .order_by(vec![col("id")])
+        .build()
+        .unwrap_err()
+        .to_string();
+    assert_contains!(
+        err,
+        "Error during planning: ORDER BY expressions must be Expr::Sort"
+    );
+}
+
+#[tokio::test]
+async fn test_aggregate_ext_order_by() {
+    let agg = first_value_udaf().call(vec![col("props")]);
+
+    // ORDER BY id ASC
+    let agg_asc = agg
+        .clone()
+        .order_by(vec![col("id").sort(true, true)])
+        .build()
+        .unwrap()
+        .alias("asc");
+
+    // ORDER BY id DESC
+    let agg_desc = agg
+        .order_by(vec![col("id").sort(false, true)])
+        .build()
+        .unwrap()
+        .alias("desc");
+
+    evaluate_agg_test(
+        agg_asc,
+        vec![
+            "+-----------------+",
+            "| asc             |",
+            "+-----------------+",
+            "| {a: 2021-02-01} |",
+            "+-----------------+",
+        ],
+    )
+    .await;
+
+    evaluate_agg_test(
+        agg_desc,
+        vec![
+            "+-----------------+",
+            "| desc            |",
+            "+-----------------+",
+            "| {a: 2021-02-03} |",
+            "+-----------------+",
+        ],
+    )
+    .await;
+}
+
+#[tokio::test]
+async fn test_aggregate_ext_filter() {
+    let agg = first_value_udaf()
+        .call(vec![col("i")])
+        .order_by(vec![col("i").sort(true, true)])
+        .filter(col("i").is_not_null())
+        .build()
+        .unwrap()
+        .alias("val");
+
+    #[rustfmt::skip]
+    evaluate_agg_test(
+        agg,
+        vec![
+            "+-----+",
+            "| val |",
+            "+-----+",
+            "| 5   |",
+            "+-----+",
+        ],
+    )
+        .await;
+}
+
+#[tokio::test]
+async fn test_aggregate_ext_distinct() {
+    let agg = sum_udaf()
+        .call(vec![lit(5)])
+        // distinct sum should be 5, not 15
+        .distinct()
+        .build()
+        .unwrap()
+        .alias("distinct");
+
+    evaluate_agg_test(
+        agg,
+        vec![
+            "+----------+",
+            "| distinct |",
+            "+----------+",
+            "| 5        |",
+            "+----------+",
+        ],
+    )
+    .await;
+}
+
+#[tokio::test]
+async fn test_aggregate_ext_null_treatment() {
+    let agg = first_value_udaf()
+        .call(vec![col("i")])
+        .order_by(vec![col("i").sort(true, true)]);
+
+    let agg_respect = agg
+        .clone()
+        .null_treatment(NullTreatment::RespectNulls)
+        .build()
+        .unwrap()
+        .alias("respect");
+
+    let agg_ignore = agg
+        .null_treatment(NullTreatment::IgnoreNulls)
+        .build()
+        .unwrap()
+        .alias("ignore");
+
+    evaluate_agg_test(
+        agg_respect,
+        vec![
+            "+---------+",
+            "| respect |",
+            "+---------+",
+            "|         |",
+            "+---------+",
+        ],
+    )
+    .await;
+
+    evaluate_agg_test(
+        agg_ignore,
+        vec![
+            "+--------+",
+            "| ignore |",
+            "+--------+",
+            "| 5      |",
+            "+--------+",
+        ],
+    )
+    .await;
+}
+
+/// Evaluates the specified expr as an aggregate and compares the result to the
+/// expected result.
+async fn evaluate_agg_test(expr: Expr, expected_lines: Vec<&str>) {
+    let batch = test_batch();
+
+    let ctx = SessionContext::new();
+    let group_expr = vec![];
+    let agg_expr = vec![expr];
+    let result = ctx
+        .read_batch(batch)
+        .unwrap()
+        .aggregate(group_expr, agg_expr)
+        .unwrap()
+        .collect()
+        .await
+        .unwrap();
+
+    let result = pretty_format_batches(&result).unwrap().to_string();
+    let actual_lines = result.lines().collect::<Vec<_>>();
+
+    assert_eq!(
+        expected_lines, actual_lines,
+        "\n\nexpected:\n\n{:#?}\nactual:\n\n{:#?}\n\n",
+        expected_lines, actual_lines
+    );
+}
+
 /// Converts the `Expr` to a `PhysicalExpr`, evaluates it against the provided
 /// `RecordBatch` and compares the result to the expected result.
 fn evaluate_expr_test(expr: Expr, expected_lines: Vec<&str>) {
@@ -189,6 +370,8 @@ fn test_batch() -> RecordBatch {
     TEST_BATCH
         .get_or_init(|| {
             let string_array: ArrayRef = Arc::new(StringArray::from(vec!["1", "2", "3"]));
+            let int_array: ArrayRef =
+                Arc::new(Int64Array::from_iter(vec![Some(10), None, Some(5)]));
 
             // { a: "2021-02-01" } { a: "2021-02-02" } { a: "2021-02-03" }
             let struct_array: ArrayRef = Arc::from(StructArray::from(vec![(
@@ -209,6 +392,7 @@ fn test_batch() -> RecordBatch {
 
             RecordBatch::try_from_iter(vec![
                 ("id", string_array),
+                ("i", int_array),
                 ("props", struct_array),
                 ("list", list_array),
             ])

datafusion/expr/src/expr.rs

@@ -255,19 +255,23 @@ pub enum Expr {
     /// can be used. The first form consists of a series of boolean "when" expressions with
     /// corresponding "then" expressions, and an optional "else" expression.
     ///
+    /// ```text
     /// CASE WHEN condition THEN result
     ///      [WHEN ...]
     ///      [ELSE result]
     /// END
+    /// ```
     ///
     /// The second form uses a base expression and then a series of "when" clauses that match on a
     /// literal value.
     ///
+    /// ```text
     /// CASE expression
     ///     WHEN value THEN result
     ///     [WHEN ...]
     ///     [ELSE result]
     /// END
+    /// ```
     Case(Case),
     /// Casts the expression to a given type and will return a runtime error if the expression cannot be cast.
     /// This expression is guaranteed to have a fixed type.
@@ -279,7 +283,12 @@ pub enum Expr {
     Sort(Sort),
     /// Represents the call of a scalar function with a set of arguments.
     ScalarFunction(ScalarFunction),
-    /// Represents the call of an aggregate built-in function with arguments.
+    /// Calls an aggregate function with arguments, and optional
+    /// `ORDER BY`, `FILTER`, `DISTINCT` and `NULL TREATMENT`.
+    ///
+    /// See also [`AggregateExt`] to set these fields.
+    ///
+    /// [`AggregateExt`]: crate::udaf::AggregateExt
     AggregateFunction(AggregateFunction),
     /// Represents the call of a window function with arguments.
     WindowFunction(WindowFunction),
@@ -623,6 +632,10 @@ impl AggregateFunctionDefinition {
 }
 
 /// Aggregate function
+///
+/// See also  [`AggregateExt`] to set these fields on `Expr`
+///
+/// [`AggregateExt`]: crate::udaf::AggregateExt
 #[derive(Clone, PartialEq, Eq, Hash, Debug)]
 pub struct AggregateFunction {
     /// Name of the function

datafusion/expr/src/lib.rs

@@ -81,7 +81,7 @@ pub use signature::{
     ArrayFunctionSignature, Signature, TypeSignature, Volatility, TIMEZONE_WILDCARD,
 };
 pub use table_source::{TableProviderFilterPushDown, TableSource, TableType};
-pub use udaf::{AggregateUDF, AggregateUDFImpl, ReversedUDAF};
+pub use udaf::{AggregateExt, AggregateUDF, AggregateUDFImpl, ReversedUDAF};
 pub use udf::{ScalarUDF, ScalarUDFImpl};
 pub use udwf::{WindowUDF, WindowUDFImpl};
 pub use window_frame::{WindowFrame, WindowFrameBound, WindowFrameUnits};