[PERF] Set min_partitions for post aggregation shuffles

daft/context.py

@@ -215,6 +215,7 @@ def set_execution_config(
     parquet_inflation_factor: float | None = None,
     csv_target_filesize: int | None = None,
     csv_inflation_factor: float | None = None,
+    aggregation_min_partitions: int | None = None,
 ) -> DaftContext:
     """Globally sets various configuration parameters which control various aspects of Daft execution. These configuration values
     are used when a Dataframe is executed (e.g. calls to `.write_*`, `.collect()` or `.show()`)
@@ -243,6 +244,7 @@ def set_execution_config(
         parquet_inflation_factor: Inflation Factor of parquet files (In-Memory-Size / File-Size) ratio. Defaults to 3.0
         csv_target_filesize: Target File Size when writing out CSV Files. Defaults to 512MB
         csv_inflation_factor: Inflation Factor of CSV files (In-Memory-Size / File-Size) ratio. Defaults to 0.5
+        aggregation_min_partitions: Minimum number of partitions to create when performing aggregations. Defaults to 10, unless the number of input partitions is less than 10.
     """
     # Replace values in the DaftExecutionConfig with user-specified overrides
     ctx = get_context()
@@ -260,6 +262,7 @@ def set_execution_config(
         parquet_inflation_factor=parquet_inflation_factor,
         csv_target_filesize=csv_target_filesize,
         csv_inflation_factor=csv_inflation_factor,
+        aggregation_min_partitions=aggregation_min_partitions,
     )
 
     ctx.daft_execution_config = new_daft_execution_config

daft/daft.pyi

@@ -1197,6 +1197,7 @@ class PyDaftExecutionConfig:
         parquet_inflation_factor: float | None = None,
         csv_target_filesize: int | None = None,
         csv_inflation_factor: float | None = None,
+        aggregation_min_partitions: int | None = None,
     ) -> PyDaftExecutionConfig: ...
     @property
     def scan_tasks_min_size_bytes(self) -> int: ...
@@ -1220,6 +1221,8 @@ class PyDaftExecutionConfig:
     def csv_target_filesize(self) -> int: ...
     @property
     def csv_inflation_factor(self) -> float: ...
+    @property
+    def aggregation_min_partitions(self) -> int: ...
 
 class PyDaftPlanningConfig:
     def with_config_values(

src/common/daft-config/src/lib.rs

@@ -34,6 +34,7 @@ pub struct DaftExecutionConfig {
     pub parquet_inflation_factor: f64,
     pub csv_target_filesize: usize,
     pub csv_inflation_factor: f64,
+    pub aggregation_min_partitions: usize,
 }
 
 impl Default for DaftExecutionConfig {
@@ -51,6 +52,7 @@ impl Default for DaftExecutionConfig {
             parquet_inflation_factor: 3.0,
             csv_target_filesize: 512 * 1024 * 1024, // 512MB
             csv_inflation_factor: 0.5,
+            aggregation_min_partitions: 10,
         }
     }
 }

src/common/daft-config/src/python.rs

@@ -89,6 +89,7 @@ impl PyDaftExecutionConfig {
         parquet_inflation_factor: Option<f64>,
         csv_target_filesize: Option<usize>,
         csv_inflation_factor: Option<f64>,
+        aggregation_min_partitions: Option<usize>,
     ) -> PyResult<PyDaftExecutionConfig> {
         let mut config = self.config.as_ref().clone();
 
@@ -131,6 +132,9 @@ impl PyDaftExecutionConfig {
         if let Some(csv_inflation_factor) = csv_inflation_factor {
             config.csv_inflation_factor = csv_inflation_factor;
         }
+        if let Some(aggregation_min_partitions) = aggregation_min_partitions {
+            config.aggregation_min_partitions = aggregation_min_partitions;
+        }
 
         Ok(PyDaftExecutionConfig {
             config: Arc::new(config),
@@ -192,6 +196,11 @@ impl PyDaftExecutionConfig {
         Ok(self.config.csv_inflation_factor)
     }
 
+    #[getter]
+    fn get_aggregation_min_partitions(&self) -> PyResult<usize> {
+        Ok(self.config.aggregation_min_partitions)
+    }
+
     fn __reduce__(&self, py: Python) -> PyResult<(PyObject, (Vec<u8>,))> {
         let bin_data = bincode::serialize(self.config.as_ref())
             .expect("DaftExecutionConfig should be serializable to bytes");

src/daft-plan/src/planner.rs

@@ -1,6 +1,9 @@
 use std::cmp::Ordering;
 use std::sync::Arc;
-use std::{cmp::max, collections::HashMap};
+use std::{
+    cmp::{max, min},
+    collections::HashMap,
+};
 
 use common_daft_config::DaftExecutionConfig;
 use common_error::DaftResult;
@@ -317,7 +320,7 @@ pub fn plan(logical_plan: &LogicalPlan, cfg: Arc<DaftExecutionConfig>) -> DaftRe
         }) => {
             use daft_dsl::AggExpr::{self, *};
             use daft_dsl::Expr::Column;
-            let input_plan = plan(input, cfg)?;
+            let input_plan = plan(input, cfg.clone())?;
 
             let num_input_partitions = input_plan.partition_spec().num_partitions;
 
@@ -497,7 +500,7 @@ pub fn plan(logical_plan: &LogicalPlan, cfg: Arc<DaftExecutionConfig>) -> DaftRe
                     } else {
                         let split_op = PhysicalPlan::FanoutByHash(FanoutByHash::new(
                             first_stage_agg.into(),
-                            num_input_partitions,
+                            min(num_input_partitions, cfg.aggregation_min_partitions),
                             groupby.clone(),
                         ));
                         PhysicalPlan::ReduceMerge(ReduceMerge::new(split_op.into()))

tests/dataframe/test_aggregations.py

@@ -8,6 +8,7 @@
 
 import daft
 from daft import col
+from daft.context import get_context
 from daft.datatype import DataType
 from daft.errors import ExpressionTypeError
 from daft.utils import freeze
@@ -365,3 +366,30 @@ def test_groupby_agg_pyobjects():
     assert res["groups"] == [1, 2]
     assert res["count"] == [2, 1]
     assert res["list"] == [[objects[0], objects[2], objects[4]], [objects[1], objects[3]]]
+
+
+@pytest.mark.parametrize("aggregation_min_partitions", [None, 20])
+def test_groupby_result_partitions_smaller_than_input(aggregation_min_partitions):
+    if aggregation_min_partitions is None:
+        min_partitions = get_context().daft_execution_config.aggregation_min_partitions
+    else:
+        daft.set_execution_config(aggregation_min_partitions=aggregation_min_partitions)
+        min_partitions = aggregation_min_partitions
+
+    for partition_size in [1, min_partitions, min_partitions + 1]:
+        df = daft.from_pydict(
+            {"group": [i for i in range(min_partitions + 1)], "value": [i for i in range(min_partitions + 1)]}
+        )
+        df = df.into_partitions(partition_size)
+
+        df = df.groupby(col("group")).agg(
+            [
+                (col("value").alias("sum"), "sum"),
+                (col("value").alias("mean"), "mean"),
+                (col("value").alias("min"), "min"),
+            ]
+        )
+
+        df = df.collect()
+
+        assert df.num_partitions() == min(min_partitions, partition_size)