diff --git a/datafusion/physical-optimizer/src/enforce_distribution.rs b/datafusion/physical-optimizer/src/enforce_distribution.rs
index f3ec083efb240..acb1c588097ee 100644
--- a/datafusion/physical-optimizer/src/enforce_distribution.rs
+++ b/datafusion/physical-optimizer/src/enforce_distribution.rs
@@ -36,7 +36,7 @@ use datafusion_common::config::ConfigOptions;
 use datafusion_common::error::Result;
 use datafusion_common::stats::Precision;
 use datafusion_common::tree_node::{Transformed, TransformedResult, TreeNode};
-use datafusion_expr::logical_plan::JoinType;
+use datafusion_expr::logical_plan::{Aggregate, JoinType};
 use datafusion_physical_expr::expressions::{Column, NoOp};
 use datafusion_physical_expr::utils::map_columns_before_projection;
 use datafusion_physical_expr::{
@@ -1301,10 +1301,25 @@ pub fn ensure_distribution(
             // Allow subset satisfaction when:
             // 1. Current partition count >= threshold
             // 2. Not a partitioned join since must use exact hash matching for joins
+            // 3. Not a grouping set aggregate (requires exact hash including __grouping_id)
             let current_partitions = child.plan.output_partitioning().partition_count();
+
+            // Check if the hash partitioning requirement includes __grouping_id column.
+            // Grouping set aggregates (ROLLUP, CUBE, GROUPING SETS) require exact hash
+            // partitioning on all group columns including __grouping_id to ensure partial
+            // aggregates from different partitions are correctly combined.
+            let requires_grouping_id = matches!(&requirement, Distribution::HashPartitioned(exprs)
+                if exprs.iter().any(|expr| {
+                    expr.as_any()
+                        .downcast_ref::<Column>()
+                        .is_some_and(|col| col.name() == Aggregate::INTERNAL_GROUPING_ID)
+                })
+            );
+
             let allow_subset_satisfy_partitioning = current_partitions
                 >= subset_satisfaction_threshold
-                && !is_partitioned_join;
+                && !is_partitioned_join
+                && !requires_grouping_id;
 
             // When `repartition_file_scans` is set, attempt to increase
             // parallelism at the source.
diff --git a/datafusion/sqllogictest/test_files/grouping_set_repartition.slt b/datafusion/sqllogictest/test_files/grouping_set_repartition.slt
new file mode 100644
index 0000000000000..16ab90651c8b3
--- /dev/null
+++ b/datafusion/sqllogictest/test_files/grouping_set_repartition.slt
@@ -0,0 +1,246 @@
+# 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.
+
+##########
+# Tests for ROLLUP/CUBE/GROUPING SETS with multiple partitions
+#
+# This tests the fix for https://github.com/apache/datafusion/issues/19849
+# where ROLLUP queries produced incorrect results with multiple partitions
+# because subset partitioning satisfaction was incorrectly applied.
+#
+# The bug manifests when:
+# 1. UNION ALL of subqueries each with hash-partitioned aggregates
+# 2. Outer ROLLUP groups by more columns than inner hash partitioning
+# 3. InterleaveExec preserves the inner hash partitioning
+# 4. Optimizer incorrectly uses subset satisfaction, skipping necessary repartition
+#
+# The fix ensures that when hash partitioning includes __grouping_id,
+# subset satisfaction is disabled and proper RepartitionExec is inserted.
+##########
+
+##########
+# SETUP: Create partitioned parquet files to simulate distributed data
+##########
+
+statement ok
+set datafusion.execution.target_partitions = 4;
+
+statement ok
+set datafusion.optimizer.repartition_aggregations = true;
+
+# Create partition 1
+statement ok
+COPY (SELECT column1 as channel, column2 as brand, column3 as amount FROM (VALUES
+    ('store', 'nike', 100),
+    ('store', 'nike', 200),
+    ('store', 'adidas', 150)
+))
+TO 'test_files/scratch/grouping_set_repartition/part=1/data.parquet'
+STORED AS PARQUET;
+
+# Create partition 2
+statement ok
+COPY (SELECT column1 as channel, column2 as brand, column3 as amount FROM (VALUES
+    ('store', 'adidas', 250),
+    ('web', 'nike', 300),
+    ('web', 'nike', 400)
+))
+TO 'test_files/scratch/grouping_set_repartition/part=2/data.parquet'
+STORED AS PARQUET;
+
+# Create partition 3
+statement ok
+COPY (SELECT column1 as channel, column2 as brand, column3 as amount FROM (VALUES
+    ('web', 'adidas', 350),
+    ('web', 'adidas', 450),
+    ('catalog', 'nike', 500)
+))
+TO 'test_files/scratch/grouping_set_repartition/part=3/data.parquet'
+STORED AS PARQUET;
+
+# Create partition 4
+statement ok
+COPY (SELECT column1 as channel, column2 as brand, column3 as amount FROM (VALUES
+    ('catalog', 'nike', 600),
+    ('catalog', 'adidas', 550),
+    ('catalog', 'adidas', 650)
+))
+TO 'test_files/scratch/grouping_set_repartition/part=4/data.parquet'
+STORED AS PARQUET;
+
+# Create external table pointing to the partitioned data
+statement ok
+CREATE EXTERNAL TABLE sales (channel VARCHAR, brand VARCHAR, amount INT)
+STORED AS PARQUET
+PARTITIONED BY (part INT)
+LOCATION 'test_files/scratch/grouping_set_repartition/';
+
+##########
+# TEST 1: UNION ALL + ROLLUP pattern (similar to TPC-DS q14)
+# This query pattern triggers the subset satisfaction bug because:
+# - Each UNION ALL branch has hash partitioning on (brand)
+# - The outer ROLLUP requires hash partitioning on (channel, brand, __grouping_id)
+# - Without the fix, subset satisfaction incorrectly skips repartition
+#
+# Verify the physical plan includes RepartitionExec with __grouping_id
+##########
+
+query TT
+EXPLAIN SELECT channel, brand, SUM(total) as grand_total
+FROM (
+    SELECT 'store' as channel, brand, SUM(amount) as total
+    FROM sales WHERE channel = 'store'
+    GROUP BY brand
+    UNION ALL
+    SELECT 'web' as channel, brand, SUM(amount) as total
+    FROM sales WHERE channel = 'web'
+    GROUP BY brand
+    UNION ALL
+    SELECT 'catalog' as channel, brand, SUM(amount) as total
+    FROM sales WHERE channel = 'catalog'
+    GROUP BY brand
+) sub
+GROUP BY ROLLUP(channel, brand)
+ORDER BY channel NULLS FIRST, brand NULLS FIRST;
+----
+logical_plan
+01)Sort: sub.channel ASC NULLS FIRST, sub.brand ASC NULLS FIRST
+02)--Projection: sub.channel, sub.brand, sum(sub.total) AS grand_total
+03)----Aggregate: groupBy=[[ROLLUP (sub.channel, sub.brand)]], aggr=[[sum(sub.total)]]
+04)------SubqueryAlias: sub
+05)--------Union
+06)----------Projection: Utf8("store") AS channel, sales.brand, sum(sales.amount) AS total
+07)------------Aggregate: groupBy=[[sales.brand]], aggr=[[sum(CAST(sales.amount AS Int64))]]
+08)--------------Projection: sales.brand, sales.amount
+09)----------------Filter: sales.channel = Utf8View("store")
+10)------------------TableScan: sales projection=[channel, brand, amount], partial_filters=[sales.channel = Utf8View("store")]
+11)----------Projection: Utf8("web") AS channel, sales.brand, sum(sales.amount) AS total
+12)------------Aggregate: groupBy=[[sales.brand]], aggr=[[sum(CAST(sales.amount AS Int64))]]
+13)--------------Projection: sales.brand, sales.amount
+14)----------------Filter: sales.channel = Utf8View("web")
+15)------------------TableScan: sales projection=[channel, brand, amount], partial_filters=[sales.channel = Utf8View("web")]
+16)----------Projection: Utf8("catalog") AS channel, sales.brand, sum(sales.amount) AS total
+17)------------Aggregate: groupBy=[[sales.brand]], aggr=[[sum(CAST(sales.amount AS Int64))]]
+18)--------------Projection: sales.brand, sales.amount
+19)----------------Filter: sales.channel = Utf8View("catalog")
+20)------------------TableScan: sales projection=[channel, brand, amount], partial_filters=[sales.channel = Utf8View("catalog")]
+physical_plan
+01)SortPreservingMergeExec: [channel@0 ASC, brand@1 ASC]
+02)--SortExec: expr=[channel@0 ASC, brand@1 ASC], preserve_partitioning=[true]
+03)----ProjectionExec: expr=[channel@0 as channel, brand@1 as brand, sum(sub.total)@3 as grand_total]
+04)------AggregateExec: mode=FinalPartitioned, gby=[channel@0 as channel, brand@1 as brand, __grouping_id@2 as __grouping_id], aggr=[sum(sub.total)]
+05)--------RepartitionExec: partitioning=Hash([channel@0, brand@1, __grouping_id@2], 4), input_partitions=4
+06)----------AggregateExec: mode=Partial, gby=[(NULL as channel, NULL as brand), (channel@0 as channel, NULL as brand), (channel@0 as channel, brand@1 as brand)], aggr=[sum(sub.total)]
+07)------------InterleaveExec
+08)--------------ProjectionExec: expr=[store as channel, brand@0 as brand, sum(sales.amount)@1 as total]
+09)----------------AggregateExec: mode=FinalPartitioned, gby=[brand@0 as brand], aggr=[sum(sales.amount)]
+10)------------------RepartitionExec: partitioning=Hash([brand@0], 4), input_partitions=4
+11)--------------------AggregateExec: mode=Partial, gby=[brand@0 as brand], aggr=[sum(sales.amount)]
+12)----------------------FilterExec: channel@0 = store, projection=[brand@1, amount@2]
+13)------------------------DataSourceExec: file_groups={4 groups: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=1/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=2/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=3/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=4/data.parquet]]}, projection=[channel, brand, amount], file_type=parquet, predicate=channel@0 = store, pruning_predicate=channel_null_count@2 != row_count@3 AND channel_min@0 <= store AND store <= channel_max@1, required_guarantees=[channel in (store)]
+14)--------------ProjectionExec: expr=[web as channel, brand@0 as brand, sum(sales.amount)@1 as total]
+15)----------------AggregateExec: mode=FinalPartitioned, gby=[brand@0 as brand], aggr=[sum(sales.amount)]
+16)------------------RepartitionExec: partitioning=Hash([brand@0], 4), input_partitions=4
+17)--------------------AggregateExec: mode=Partial, gby=[brand@0 as brand], aggr=[sum(sales.amount)]
+18)----------------------FilterExec: channel@0 = web, projection=[brand@1, amount@2]
+19)------------------------DataSourceExec: file_groups={4 groups: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=1/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=2/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=3/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=4/data.parquet]]}, projection=[channel, brand, amount], file_type=parquet, predicate=channel@0 = web, pruning_predicate=channel_null_count@2 != row_count@3 AND channel_min@0 <= web AND web <= channel_max@1, required_guarantees=[channel in (web)]
+20)--------------ProjectionExec: expr=[catalog as channel, brand@0 as brand, sum(sales.amount)@1 as total]
+21)----------------AggregateExec: mode=FinalPartitioned, gby=[brand@0 as brand], aggr=[sum(sales.amount)]
+22)------------------RepartitionExec: partitioning=Hash([brand@0], 4), input_partitions=4
+23)--------------------AggregateExec: mode=Partial, gby=[brand@0 as brand], aggr=[sum(sales.amount)]
+24)----------------------FilterExec: channel@0 = catalog, projection=[brand@1, amount@2]
+25)------------------------DataSourceExec: file_groups={4 groups: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=1/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=2/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=3/data.parquet], [WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/grouping_set_repartition/part=4/data.parquet]]}, projection=[channel, brand, amount], file_type=parquet, predicate=channel@0 = catalog, pruning_predicate=channel_null_count@2 != row_count@3 AND channel_min@0 <= catalog AND catalog <= channel_max@1, required_guarantees=[channel in (catalog)]
+
+query TTI rowsort
+SELECT channel, brand, SUM(total) as grand_total
+FROM (
+    SELECT 'store' as channel, brand, SUM(amount) as total
+    FROM sales WHERE channel = 'store'
+    GROUP BY brand
+    UNION ALL
+    SELECT 'web' as channel, brand, SUM(amount) as total
+    FROM sales WHERE channel = 'web'
+    GROUP BY brand
+    UNION ALL
+    SELECT 'catalog' as channel, brand, SUM(amount) as total
+    FROM sales WHERE channel = 'catalog'
+    GROUP BY brand
+) sub
+GROUP BY ROLLUP(channel, brand)
+ORDER BY channel NULLS FIRST, brand NULLS FIRST;
+----
+NULL NULL 4500
+catalog NULL 2300
+catalog adidas 1200
+catalog nike 1100
+store NULL 700
+store adidas 400
+store nike 300
+web NULL 1500
+web adidas 800
+web nike 700
+
+##########
+# TEST 2: Simple ROLLUP (baseline test)
+##########
+
+query TTI rowsort
+SELECT channel, brand, SUM(amount) as total
+FROM sales
+GROUP BY ROLLUP(channel, brand)
+ORDER BY channel NULLS FIRST, brand NULLS FIRST;
+----
+NULL NULL 4500
+catalog NULL 2300
+catalog adidas 1200
+catalog nike 1100
+store NULL 700
+store adidas 400
+store nike 300
+web NULL 1500
+web adidas 800
+web nike 700
+
+##########
+# TEST 3: Verify CUBE also works correctly
+##########
+
+query TTI rowsort
+SELECT channel, brand, SUM(amount) as total
+FROM sales
+GROUP BY CUBE(channel, brand)
+ORDER BY channel NULLS FIRST, brand NULLS FIRST;
+----
+NULL NULL 4500
+NULL adidas 2400
+NULL nike 2100
+catalog NULL 2300
+catalog adidas 1200
+catalog nike 1100
+store NULL 700
+store adidas 400
+store nike 300
+web NULL 1500
+web adidas 800
+web nike 700
+
+##########
+# CLEANUP
+##########
+
+statement ok
+DROP TABLE sales;