diff --git a/datafusion_ray/context.py b/datafusion_ray/context.py
index 5cbaf86..954d665 100644
--- a/datafusion_ray/context.py
+++ b/datafusion_ray/context.py
@@ -92,80 +92,6 @@ def _get_worker_inputs(
     return futures
 
 
-@ray.remote(num_cpus=0)
-def execute_query_stage(
-    query_stages: list[QueryStage],
-    stage_id: int,
-) -> tuple[int, list[ray.ObjectRef]]:
-    """
-    Execute a query stage on the workers.
-
-    Returns the stage ID, and a list of futures for the output partitions of the query stage.
-    """
-    stage = QueryStage(stage_id, query_stages[stage_id])
-
-    # execute child stages first
-    child_futures = []
-    for child_id in stage.get_child_stage_ids():
-        child_futures.append(execute_query_stage.remote(query_stages, child_id))
-
-    # if the query stage has a single output partition then we need to execute for the output
-    # partition, otherwise we need to execute in parallel for each input partition
-    concurrency = stage.get_input_partition_count()
-    output_partitions_count = stage.get_output_partition_count()
-    if output_partitions_count == 1:
-        # reduce stage
-        print("Forcing reduce stage concurrency from {} to 1".format(concurrency))
-        concurrency = 1
-
-    print(
-        "Scheduling query stage #{} with {} input partitions and {} output partitions".format(
-            stage.id(), concurrency, output_partitions_count
-        )
-    )
-
-    # A list of (stage ID, list of futures) for each child stage
-    # Each list is a 2-D array of (input partitions, output partitions).
-    child_outputs = ray.get(child_futures)
-
-    def _get_worker_inputs(
-        part: int,
-    ) -> tuple[list[tuple[int, int, int]], list[ray.ObjectRef]]:
-        ids = []
-        futures = []
-        for child_stage_id, child_futures in child_outputs:
-            for i, lst in enumerate(child_futures):
-                if isinstance(lst, list):
-                    for j, f in enumerate(lst):
-                        if concurrency == 1 or j == part:
-                            # If concurrency is 1, pass in all shuffle partitions. Otherwise,
-                            # only pass in the partitions that match the current worker partition.
-                            ids.append((child_stage_id, i, j))
-                            futures.append(f)
-                elif concurrency == 1 or part == 0:
-                    ids.append((child_stage_id, i, 0))
-                    futures.append(lst)
-        return ids, futures
-
-    # schedule the actual execution workers
-    plan_bytes = stage.get_execution_plan_bytes()
-    futures = []
-    opt = {}
-    # TODO not sure why we had this but my Ray cluster could not find suitable resource
-    # until I commented this out
-    # opt["resources"] = {"worker": 1e-3}
-    opt["num_returns"] = output_partitions_count
-    for part in range(concurrency):
-        ids, inputs = _get_worker_inputs(part)
-        futures.append(
-            execute_query_partition.options(**opt).remote(
-                stage_id, plan_bytes, part, ids, *inputs
-            )
-        )
-
-    return stage_id, futures
-
-
 @ray.remote
 def execute_query_partition(
     stage_id: int,