diff --git a/data_juicer/core/ray_data.py b/data_juicer/core/ray_data.py
index 621e68cd9..2966e75e8 100644
--- a/data_juicer/core/ray_data.py
+++ b/data_juicer/core/ray_data.py
@@ -58,18 +58,8 @@ def set_dataset_to_absolute_path(dataset, dataset_path, cfg):
 
 
 def preprocess_dataset(dataset: rd.Dataset, dataset_path, cfg) -> rd.Dataset:
-    columns = dataset.columns()
     if dataset_path:
         dataset = set_dataset_to_absolute_path(dataset, dataset_path, cfg)
-    if Fields.stats not in columns:
-
-        def process_batch_arrow(table: pa.Table) -> pa.Table:
-            new_column_data = [{} for _ in range(len(table))]
-            new_talbe = table.append_column(Fields.stats, [new_column_data])
-            return new_talbe
-
-        dataset = dataset.map_batches(process_batch_arrow,
-                                      batch_format='pyarrow')
     return dataset
 
 
@@ -123,6 +113,16 @@ def _run_single_op(self, op):
                                                   batch_format='pyarrow',
                                                   num_gpus=num_gpus)
             elif isinstance(op, Filter):
+                columns = self.data.columns()
+                if Fields.stats not in columns:
+
+                    def process_batch_arrow(table: pa.Table) -> pa.Table:
+                        new_column_data = [{} for _ in range(len(table))]
+                        new_talbe = table.append_column(Fields.stats, [new_column_data])
+                        return new_talbe
+
+                    self.data = self.data.map_batches(process_batch_arrow,
+                                                      batch_format='pyarrow')
                 self.data = self.data.map_batches(op.compute_stats,
                                                   batch_size=batch_size,
                                                   batch_format='pyarrow',
diff --git a/data_juicer/core/ray_executor.py b/data_juicer/core/ray_executor.py
index 6b93fd3dd..f146ffc02 100644
--- a/data_juicer/core/ray_executor.py
+++ b/data_juicer/core/ray_executor.py
@@ -68,10 +68,10 @@ def run(self, load_data_np=None):
         logger.info('Processing data...')
         tstart = time.time()
         dataset.process(ops)
-        tend = time.time()
-        logger.info(f'All Ops are done in {tend - tstart:.3f}s.')
 
         # 4. data export
         logger.info('Exporting dataset to disk...')
         dataset.data.write_json(self.cfg.export_path, force_ascii=False)
+        tend = time.time()
+        logger.info(f'All Ops are done in {tend - tstart:.3f}s.')
         return dataset
diff --git a/data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py b/data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py
index ba87edda9..676aa0185 100644
--- a/data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py
+++ b/data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py
@@ -585,24 +585,16 @@ def minhash_with_uid(table: pa.Table) -> pa.Table:
                 HashKeys.uid,
                 pa.array(list(uid_list))
             )
-            if not new_table[Fields.stats][0].as_py():
-                columns_to_keep = [
-                    name
-                    for name in new_table.column_names
-                    if name != Fields.stats
-                ]
-                new_table = new_table.select(columns_to_keep)
-            pq.write_table(
-                new_table,
-                os.path.join(self.tmp_file_name, f'{min_id}.parquet')
-            )
-            return pa.Table.from_arrays([])
+            return new_table
 
         dataset.map_batches(
             minhash_with_uid,
             batch_format='pyarrow',
             zero_copy_batch=True,
-        ).materialize()
+        ).write_parquet(
+            self.tmp_file_name,
+            force_ascii=False
+        )
         dataset = ray.data.read_parquet(self.tmp_file_name)
         end_time = time.time()
         print(f'MinHash time = {end_time - start_time}')