diff --git a/.gitignore b/.gitignore
index aab1f8d41..4de9443ab 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,3 +32,6 @@ htmlcov/
 debug/
 .codecov.yml
 coverage.xml
+
+# Example data
+/examples/bulk_writer
diff --git a/examples/data/train_embeddings.csv b/examples/data/train_embeddings.csv
new file mode 100644
index 000000000..de39e4d1c
--- /dev/null
+++ b/examples/data/train_embeddings.csv
@@ -0,0 +1,101 @@
+path,label,vector
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diff --git a/examples/example_bulkwriter.py b/examples/example_bulkwriter.py
index 96f1d26b9..1b41223e7 100644
--- a/examples/example_bulkwriter.py
+++ b/examples/example_bulkwriter.py
@@ -14,10 +14,12 @@
 import json
 import random
 import threading
+import time
+import pandas as pd
+import numpy as np
 
 import logging
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("example_bulkwriter")
 
 from pymilvus import (
     connections,
@@ -30,6 +32,7 @@
     bulk_import,
     get_import_progress,
     list_import_jobs,
+    BulkInsertState,
 )
 
 # minio
@@ -41,8 +44,17 @@
 HOST = '127.0.0.1'
 PORT = '19530'
 
-COLLECTION_NAME = "test_abc"
-DIM = 256
+CSV_COLLECTION_NAME = "test_csv"
+ALL_TYPES_COLLECTION_NAME = "test_all_types"
+DIM = 512
+
+def gen_binary_vector():
+    raw_vector = [random.randint(0, 1) for i in range(DIM)]
+    binary_vectors = np.packbits(raw_vector, axis=-1).tolist()
+    return binary_vectors
+
+def gen_float_vector():
+    return [random.random() for _ in range(DIM)]
 
 def create_connection():
     print(f"\nCreate connection...")
@@ -50,56 +62,101 @@ def create_connection():
     print(f"\nConnected")
 
 
-def build_collection():
-    if utility.has_collection(COLLECTION_NAME):
-        utility.drop_collection(COLLECTION_NAME)
-
-    field1 = FieldSchema(name="id", dtype=DataType.INT64, auto_id=True, is_primary=True)
-    field2 = FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=DIM)
-    field3 = FieldSchema(name="desc", dtype=DataType.VARCHAR, max_length=100)
-    schema = CollectionSchema(fields=[field1, field2, field3])
-    collection = Collection(name=COLLECTION_NAME, schema=schema)
-    print("Collection created")
+def build_csv_collection():
+    print(f"\n===================== create collection ====================")
+    if utility.has_collection(CSV_COLLECTION_NAME):
+        utility.drop_collection(CSV_COLLECTION_NAME)
+
+    fields = [
+        FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
+        FieldSchema(name="path", dtype=DataType.VARCHAR, max_length=512),
+        FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=DIM),
+        FieldSchema(name="label", dtype=DataType.VARCHAR, max_length=512),
+    ]
+    schema = CollectionSchema(fields=fields)
+    collection = Collection(name=CSV_COLLECTION_NAME, schema=schema)
+    print(f"Collection '{collection.name}' created")
     return collection.schema
 
+def build_all_type_schema(bin_vec: bool):
+    print(f"\n===================== build all types schema ====================")
+    fields = [
+        FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=False),
+        FieldSchema(name="bool", dtype=DataType.BOOL),
+        FieldSchema(name="int8", dtype=DataType.INT8),
+        FieldSchema(name="int16", dtype=DataType.INT16),
+        FieldSchema(name="int32", dtype=DataType.INT32),
+        FieldSchema(name="int64", dtype=DataType.INT64),
+        FieldSchema(name="float", dtype=DataType.FLOAT),
+        FieldSchema(name="double", dtype=DataType.DOUBLE),
+        FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=512),
+        FieldSchema(name="json", dtype=DataType.JSON),
+        FieldSchema(name="vector", dtype=DataType.BINARY_VECTOR, dim=DIM) if bin_vec else FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=DIM),
+    ]
+    schema = CollectionSchema(fields=fields, enable_dynamic_field=True)
+    return schema
+
+def read_sample_data(file_path: str, writer: [LocalBulkWriter, RemoteBulkWriter]):
+    csv_data = pd.read_csv(file_path)
+    print(f"The csv file has {csv_data.shape[0]} rows")
+    for i in range(csv_data.shape[0]):
+        row = {}
+        for col in csv_data.columns.values:
+            if col == "vector":
+                vec = json.loads(csv_data[col][i])
+                row[col] = vec
+            else:
+                row[col] = csv_data[col][i]
+
+        writer.append_row(row)
+
 def test_local_writer_json(schema: CollectionSchema):
-    local_writer = LocalBulkWriter(schema=schema,
-                                   local_path="/tmp/bulk_data",
-                                   segment_size=4*1024*1024,
-                                   file_type=BulkFileType.JSON_RB,
-                                   )
-    for i in range(10):
-        local_writer.append_row({"id": i, "vector": [random.random() for _ in range(DIM)], "desc": f"description_{i}"})
+    print(f"\n===================== test local JSON writer ====================")
+    with LocalBulkWriter(
+            schema=schema,
+            local_path="/tmp/bulk_writer",
+            segment_size=4*1024*1024,
+            file_type=BulkFileType.JSON_RB,
+    ) as local_writer:
+        read_sample_data("./data/train_embeddings.csv", local_writer)
+        local_writer.commit()
+        batch_files = local_writer.batch_files
 
-    local_writer.commit()
-    print("test local writer done!")
-    print(local_writer.data_path)
-    return local_writer.data_path
+    print(f"Test local writer done! output local files: {batch_files}")
 
-def test_local_writer_npy(schema: CollectionSchema):
-    local_writer = LocalBulkWriter(schema=schema, local_path="/tmp/bulk_data", segment_size=4*1024*1024)
-    for i in range(10000):
-        local_writer.append_row({"id": i, "vector": [random.random() for _ in range(DIM)], "desc": f"description_{i}"})
 
-    local_writer.commit()
-    print("test local writer done!")
-    print(local_writer.data_path)
-    return local_writer.data_path
+def test_local_writer_npy(schema: CollectionSchema):
+    print(f"\n===================== test local npy writer ====================")
+    with LocalBulkWriter(
+            schema=schema,
+            local_path="/tmp/bulk_writer",
+            segment_size=4*1024*1024,
+    ) as local_writer:
+        read_sample_data("./data/train_embeddings.csv", local_writer)
+        local_writer.commit()
+        batch_files = local_writer.batch_files
 
+    print(f"Test local writer done! output local files: {batch_files}")
 
-def _append_row(writer: LocalBulkWriter, begin: int, end: int):
-    for i in range(begin, end):
-        writer.append_row({"id": i, "vector": [random.random() for _ in range(DIM)], "desc": f"description_{i}"})
 
 def test_parallel_append(schema: CollectionSchema):
-    local_writer = LocalBulkWriter(schema=schema,
-                                   local_path="/tmp/bulk_data",
-                                   segment_size=1000 * 1024 * 1024,
-                                   file_type=BulkFileType.JSON_RB,
-                                   )
+    print(f"\n===================== test parallel append ====================")
+    def _append_row(writer: LocalBulkWriter, begin: int, end: int):
+        try:
+            for i in range(begin, end):
+                writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
+        except Exception as e:
+            print("failed to append row!")
+
+    local_writer = LocalBulkWriter(
+        schema=schema,
+        local_path="/tmp/bulk_writer",
+        segment_size=1000 * 1024 * 1024,
+        file_type=BulkFileType.JSON_RB,
+    )
     threads = []
     thread_count = 100
-    rows_per_thread = 1000
+    rows_per_thread = 100
     for k in range(thread_count):
         x = threading.Thread(target=_append_row, args=(local_writer, k*rows_per_thread, (k+1)*rows_per_thread,))
         threads.append(x)
@@ -108,6 +165,7 @@ def test_parallel_append(schema: CollectionSchema):
 
     for th in threads:
         th.join()
+        print(f"Thread '{th.name}' finished")
 
     local_writer.commit()
     print(f"Append finished, {thread_count*rows_per_thread} rows")
@@ -118,40 +176,128 @@ def test_parallel_append(schema: CollectionSchema):
     print("Verify the output content...")
     rows = data['rows']
     assert len(rows) == thread_count*rows_per_thread
-    for i in range(len(rows)):
-        row = rows[i]
-        assert row['desc'] == f"description_{row['id']}"
+    for row in rows:
+        pa = row['path']
+        label = row['label']
+        assert pa.replace("path_", "") == label.replace("label_", "")
+    print("Data is correct")
 
 
 def test_remote_writer(schema: CollectionSchema):
-    remote_writer = RemoteBulkWriter(schema=schema,
-                                     remote_path="bulk_data",
-                                     local_path="/tmp/bulk_data",
-                                     connect_param=RemoteBulkWriter.ConnectParam(
-                                         endpoint=MINIO_ADDRESS,
-                                         access_key=MINIO_ACCESS_KEY,
-                                         secret_key=MINIO_SECRET_KEY,
-                                         bucket_name="a-bucket",
-                                     ),
-                                     segment_size=50 * 1024 * 1024,
-                                     )
+    print(f"\n===================== test remote writer ====================")
+    with RemoteBulkWriter(
+            schema=schema,
+            remote_path="bulk_data",
+            connect_param=RemoteBulkWriter.ConnectParam(
+                endpoint=MINIO_ADDRESS,
+                access_key=MINIO_ACCESS_KEY,
+                secret_key=MINIO_SECRET_KEY,
+                bucket_name="a-bucket",
+            ),
+            segment_size=50 * 1024 * 1024,
+    ) as remote_writer:
+        read_sample_data("./data/train_embeddings.csv", remote_writer)
+        remote_writer.commit()
+        batch_files = remote_writer.batch_files
+
+    print(f"Test remote writer done! output remote files: {batch_files}")
+
+
+def test_all_types_writer(bin_vec: bool, schema: CollectionSchema)->list:
+    print(f"\n===================== all types test ====================")
+    remote_writer = RemoteBulkWriter(
+        schema=schema,
+        remote_path="bulk_data",
+        connect_param=RemoteBulkWriter.ConnectParam(
+            endpoint=MINIO_ADDRESS,
+            access_key=MINIO_ACCESS_KEY,
+            secret_key=MINIO_SECRET_KEY,
+            bucket_name="a-bucket",
+        ),
+    )
 
+    print("Append rows")
     for i in range(10000):
-        if i % 1000 == 0:
-            logger.info(f"{i} rows has been append to remote writer")
-        remote_writer.append_row({"id": i, "vector": [random.random() for _ in range(DIM)], "desc": f"description_{i}"})
-
+        row = {
+            "id": i,
+            "bool": True if i%5 == 0 else False,
+            "int8": i%128,
+            "int16": i%1000,
+            "int32": i%100000,
+            "int64": i,
+            "float": i/3,
+            "double": i/7,
+            "varchar": f"varchar_{i}",
+            "json": {"dummy": i, "ok": f"name_{i}"},
+            "vector": gen_binary_vector() if bin_vec else gen_float_vector(),
+            f"dynamic_{i}": i,
+        }
+        remote_writer.append_row(row)
+
+    print("Generate data files...")
     remote_writer.commit()
-    print("test remote writer done!")
-    print(remote_writer.data_path)
-    return remote_writer.data_path
-
+    print(f"Data files have been uploaded: {remote_writer.batch_files}")
+    return remote_writer.batch_files
+
+
+def test_call_bulkinsert(schema: CollectionSchema, batch_files: list):
+    print(f"\n===================== test call bulkinsert ====================")
+    if utility.has_collection(ALL_TYPES_COLLECTION_NAME):
+        utility.drop_collection(ALL_TYPES_COLLECTION_NAME)
+
+    collection = Collection(name=ALL_TYPES_COLLECTION_NAME, schema=schema)
+    print(f"Collection '{collection.name}' created")
+
+    task_ids = []
+    for files in batch_files:
+        task_id = utility.do_bulk_insert(collection_name=ALL_TYPES_COLLECTION_NAME, files=files)
+        task_ids.append(task_id)
+        print(f"Create a bulkinert task, task id: {task_id}")
+
+    while len(task_ids) > 0:
+        print("Wait 1 second to check bulkinsert tasks state...")
+        time.sleep(1)
+        for id in task_ids:
+            state = utility.get_bulk_insert_state(task_id=id)
+            if state.state == BulkInsertState.ImportFailed or state.state == BulkInsertState.ImportFailedAndCleaned:
+                print(f"The task {state.task_id} failed, reason: {state.failed_reason}")
+                task_ids.remove(id)
+            elif state.state == BulkInsertState.ImportCompleted:
+                print(f"The task {state.task_id} completed")
+                task_ids.remove(id)
+
+    print(f"Collection row number: {collection.num_entities}")
+
+
+def test_retrieve_imported_data(bin_vec: bool):
+    collection = Collection(name=ALL_TYPES_COLLECTION_NAME)
+    print("Create index...")
+    index_param = {
+        "index_type": "BIN_FLAT",
+        "params": {},
+        "metric_type": "HAMMING"
+    } if bin_vec else {
+        "index_type": "FLAT",
+        "params": {},
+        "metric_type": "L2"
+    }
+    collection.create_index(field_name="vector", index_params=index_param)
+
+    ids = [100, 5000]
+    print(f"Load collection and query items {ids}")
+    collection.load()
+    expr = f"id in {ids}"
+    print(expr)
+    results = collection.query(expr=expr, output_fields=["*", "vector"])
+    print("Query results:")
+    for item in results:
+        print(item)
 
 def test_cloud_bulkinsert():
     url = "https://_your_cloud_server_url_"
     cluster_id = "_your_cloud_instance_id_"
 
-    print(f"===================== import files to cloud vectordb ====================")
+    print(f"\n===================== import files to cloud vectordb ====================")
     object_url = "_your_object_storage_service_url_"
     object_url_access_key = "_your_object_storage_service_access_key_"
     object_url_secret_key = "_your_object_storage_service_secret_key_"
@@ -161,11 +307,11 @@ def test_cloud_bulkinsert():
         access_key=object_url_access_key,
         secret_key=object_url_secret_key,
         cluster_id=cluster_id,
-        collection_name=COLLECTION_NAME,
+        collection_name=CSV_COLLECTION_NAME,
     )
     print(resp)
 
-    print(f"===================== get import job progress ====================")
+    print(f"\n===================== get import job progress ====================")
     job_id = resp['data']['jobId']
     resp = get_import_progress(
         url=url,
@@ -174,7 +320,7 @@ def test_cloud_bulkinsert():
     )
     print(resp)
 
-    print(f"===================== list import jobs ====================")
+    print(f"\n===================== list import jobs ====================")
     resp = list_import_jobs(
         url=url,
         cluster_id=cluster_id,
@@ -186,12 +332,24 @@ def test_cloud_bulkinsert():
 
 if __name__ == '__main__':
     create_connection()
-    schema = build_collection()
 
+    schema = build_csv_collection()
     test_local_writer_json(schema)
     test_local_writer_npy(schema)
     test_remote_writer(schema)
     test_parallel_append(schema)
 
+    # float vectors + all scalar types
+    schema = build_all_type_schema(bin_vec=False)
+    batch_files = test_all_types_writer(bin_vec=False, schema=schema)
+    test_call_bulkinsert(schema, batch_files)
+    test_retrieve_imported_data(bin_vec=False)
+
+    # binary vectors + all scalar types
+    schema = build_all_type_schema(bin_vec=True)
+    batch_files = test_all_types_writer(bin_vec=True, schema=schema)
+    test_call_bulkinsert(schema, batch_files)
+    test_retrieve_imported_data(bin_vec=True)
+
     # test_cloud_bulkinsert()
 
diff --git a/pymilvus/bulk_writer/buffer.py b/pymilvus/bulk_writer/buffer.py
index 5db288fec..09f70d061 100644
--- a/pymilvus/bulk_writer/buffer.py
+++ b/pymilvus/bulk_writer/buffer.py
@@ -16,11 +16,18 @@
 
 import numpy as np
 
+from pymilvus.client.types import (
+    DataType,
+)
 from pymilvus.exceptions import MilvusException
-from pymilvus.orm.schema import CollectionSchema
+from pymilvus.orm.schema import (
+    CollectionSchema,
+    FieldSchema,
+)
 
 from .constants import (
     DYNAMIC_FIELD_NAME,
+    NUMPY_TYPE_CREATOR,
     BulkFileType,
 )
 
@@ -35,9 +42,13 @@ def __init__(
         file_type: BulkFileType = BulkFileType.NPY,
     ):
         self._buffer = {}
+        self._fields = {}
         self._file_type = file_type
         for field in schema.fields:
+            if field.is_primary and field.auto_id:
+                continue
             self._buffer[field.name] = []
+            self._fields[field.name] = field
 
         if len(self._buffer) == 0:
             self._throw("Illegal collection schema: fields list is empty")
@@ -45,6 +56,9 @@ def __init__(
         # dynamic field, internal name is '$meta'
         if schema.enable_dynamic_field:
             self._buffer[DYNAMIC_FIELD_NAME] = []
+            self._fields[DYNAMIC_FIELD_NAME] = FieldSchema(
+                name=DYNAMIC_FIELD_NAME, dtype=DataType.JSON
+            )
 
     @property
     def row_count(self) -> int:
@@ -75,7 +89,7 @@ def append_row(self, row: dict):
                 self._buffer[k].append(row[k])
 
         if DYNAMIC_FIELD_NAME in self._buffer:
-            self._buffer[DYNAMIC_FIELD_NAME].append(json.dumps(dynamic_values))
+            self._buffer[DYNAMIC_FIELD_NAME].append(dynamic_values)
 
     def persist(self, local_path: str) -> list:
         # verify row count of fields are equal
@@ -100,14 +114,28 @@ def persist(self, local_path: str) -> list:
         return []
 
     def _persist_npy(self, local_path: str):
-        Path(local_path).mkdir(exist_ok=True)
-
         file_list = []
         for k in self._buffer:
             full_file_name = Path(local_path).joinpath(k + ".npy")
-            file_list.append(full_file_name)
+            file_list.append(str(full_file_name))
             try:
-                np.save(full_file_name, self._buffer[k])
+                Path(local_path).mkdir(exist_ok=True)
+
+                # numpy data type specify
+                dt = None
+                field_schema = self._fields[k]
+                if field_schema.dtype.name in NUMPY_TYPE_CREATOR:
+                    dt = NUMPY_TYPE_CREATOR[field_schema.dtype.name]
+
+                # for JSON field, convert to string array
+                if field_schema.dtype == DataType.JSON:
+                    str_arr = []
+                    for val in self._buffer[k]:
+                        str_arr.append(json.dumps(val))
+                    self._buffer[k] = str_arr
+
+                arr = np.array(self._buffer[k], dtype=dt)
+                np.save(str(full_file_name), arr)
             except Exception as e:
                 self._throw(f"Failed to persist column-based file {full_file_name}, error: {e}")
 
@@ -145,4 +173,4 @@ def _persist_json_rows(self, local_path: str):
             self._throw(f"Failed to persist row-based file {file_path}, error: {e}")
 
         logger.info(f"Successfully persist row-based file {file_path}")
-        return [file_path]
+        return [str(file_path)]
diff --git a/pymilvus/bulk_writer/bulk_writer.py b/pymilvus/bulk_writer/bulk_writer.py
index 4548a96e1..2d4cc3095 100644
--- a/pymilvus/bulk_writer/bulk_writer.py
+++ b/pymilvus/bulk_writer/bulk_writer.py
@@ -94,18 +94,26 @@ def _verify_row(self, row: dict):
 
         row_size = 0
         for field in self._schema.fields:
+            if field.is_primary and field.auto_id:
+                if field.name in row:
+                    self._throw(
+                        f"The primary key field '{field.name}' is auto-id, no need to provide"
+                    )
+                else:
+                    continue
+
             if field.name not in row:
                 self._throw(f"The field '{field.name}' is missed in the row")
 
-            if field.is_parimary and field.auto_id:
-                self._throw(f"The primary key field '{field.name}' is auto-id, no need to provide")
-
             dtype = DataType(field.dtype)
             validator = TYPE_VALIDATOR[dtype.name]
             if dtype in {DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR}:
                 dim = field.params["dim"]
                 if not validator(row[field.name], dim):
-                    self._throw(f"Illegal vector data for vector field '{dtype.name}'")
+                    self._throw(
+                        f"Illegal vector data for vector field: '{dtype.name}',"
+                        f" dim is not {dim} or type mismatch"
+                    )
 
                 vec_size = (
                     len(row[field.name]) * 4
@@ -116,12 +124,22 @@ def _verify_row(self, row: dict):
             elif dtype == DataType.VARCHAR:
                 max_len = field.params["max_length"]
                 if not validator(row[field.name], max_len):
-                    self._throw(f"Illegal varchar value for field '{dtype.name}'")
+                    self._throw(
+                        f"Illegal varchar value for field '{dtype.name}',"
+                        f" length exceeds {max_len} or type mismatch"
+                    )
+
+                row_size = row_size + len(row[field.name])
+            elif dtype == DataType.JSON:
+                if not validator(row[field.name]):
+                    self._throw(f"Illegal varchar value for field '{dtype.name}', type mismatch")
 
                 row_size = row_size + len(row[field.name])
             else:
                 if not validator(row[field.name]):
-                    self._throw(f"Illegal scalar value for field '{dtype.name}'")
+                    self._throw(
+                        f"Illegal scalar value for field '{dtype.name}', value overflow or type mismatch"
+                    )
 
                 row_size = row_size + TYPE_SIZE[dtype.name]
 
diff --git a/pymilvus/bulk_writer/constants.py b/pymilvus/bulk_writer/constants.py
index beda9da69..ba233b979 100644
--- a/pymilvus/bulk_writer/constants.py
+++ b/pymilvus/bulk_writer/constants.py
@@ -12,6 +12,8 @@
 
 from enum import IntEnum
 
+import numpy as np
+
 from pymilvus.client.types import (
     DataType,
 )
@@ -41,9 +43,23 @@
     DataType.FLOAT.name: lambda x: isinstance(x, float),
     DataType.DOUBLE.name: lambda x: isinstance(x, float),
     DataType.VARCHAR.name: lambda x, max_len: isinstance(x, str) and len(x) <= max_len,
-    DataType.JSON.name: lambda x: isinstance(x, dict),
+    DataType.JSON.name: lambda x: isinstance(x, dict) and len(x) <= 65535,
     DataType.FLOAT_VECTOR.name: lambda x, dim: isinstance(x, list) and len(x) == dim,
-    DataType.BINARY_VECTOR.name: lambda x, dim: isinstance(x, bytes) and len(x) * 8 == dim,
+    DataType.BINARY_VECTOR.name: lambda x, dim: isinstance(x, list) and len(x) * 8 == dim,
+}
+
+NUMPY_TYPE_CREATOR = {
+    DataType.BOOL.name: np.dtype("bool"),
+    DataType.INT8.name: np.dtype("int8"),
+    DataType.INT16.name: np.dtype("int16"),
+    DataType.INT32.name: np.dtype("int32"),
+    DataType.INT64.name: np.dtype("int64"),
+    DataType.FLOAT.name: np.dtype("float32"),
+    DataType.DOUBLE.name: np.dtype("float64"),
+    DataType.VARCHAR.name: None,
+    DataType.JSON.name: None,
+    DataType.FLOAT_VECTOR.name: np.dtype("float32"),
+    DataType.BINARY_VECTOR.name: np.dtype("uint8"),
 }
 
 
diff --git a/pymilvus/bulk_writer/local_bulk_writer.py b/pymilvus/bulk_writer/local_bulk_writer.py
index ad1b7998d..7a29144a8 100644
--- a/pymilvus/bulk_writer/local_bulk_writer.py
+++ b/pymilvus/bulk_writer/local_bulk_writer.py
@@ -40,22 +40,41 @@ def __init__(
     ):
         super().__init__(schema, segment_size, file_type)
         self._local_path = local_path
-        self._make_dir()
+        self._uuid = str(uuid.uuid4())
         self._flush_count = 0
         self._working_thread = {}
+        self._local_files = []
+
+    @property
+    def uuid(self):
+        return self._uuid
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type: object, exc_val: object, exc_tb: object):
+        self._exit()
 
     def __del__(self):
+        self._exit()
+
+    def _exit(self):
+        if Path(self._local_path).exists() and not any(Path(self._local_path).iterdir()):
+            Path(self._local_path).rmdir()
+            logger.info(f"Delete empty directory '{self._local_path}'")
+
         if len(self._working_thread) > 0:
             for k, th in self._working_thread.items():
-                logger.info(f"Wait thread '{k}' to finish")
+                logger.info(f"Wait flush thread '{k}' to finish")
                 th.join()
 
     def _make_dir(self):
         Path(self._local_path).mkdir(exist_ok=True)
-        uidir = Path(self._local_path).joinpath(str(uuid.uuid4()))
-        Path(uidir).mkdir(exist_ok=True)
+        logger.info(f"Data path created: {self._local_path}")
+        uidir = Path(self._local_path).joinpath(self._uuid)
         self._local_path = uidir
-        logger.info(f"Local buffer writer initialized, target path: {uidir}")
+        Path(uidir).mkdir(exist_ok=True)
+        logger.info(f"Data path created: {uidir}")
 
     def append_row(self, row: dict, **kwargs):
         super().append_row(row, **kwargs)
@@ -76,16 +95,19 @@ def commit(self, **kwargs):
         x = Thread(target=self._flush, args=(call_back,))
         x.start()
         if not _async:
+            logger.info("Wait flush to finish")
             x.join()
         super().commit()  # reset the buffer size
 
     def _flush(self, call_back: Optional[Callable] = None):
+        self._make_dir()
         self._working_thread[threading.current_thread().name] = threading.current_thread()
         self._flush_count = self._flush_count + 1
         target_path = Path.joinpath(self._local_path, str(self._flush_count))
 
         old_buffer = super()._new_buffer()
         file_list = old_buffer.persist(str(target_path))
+        self._local_files.append(file_list)
         if call_back:
             call_back(file_list)
         del self._working_thread[threading.current_thread().name]
@@ -93,3 +115,7 @@ def _flush(self, call_back: Optional[Callable] = None):
     @property
     def data_path(self):
         return self._local_path
+
+    @property
+    def batch_files(self):
+        return self._local_files
diff --git a/pymilvus/bulk_writer/remote_bulk_writer.py b/pymilvus/bulk_writer/remote_bulk_writer.py
index dfa6e94a8..9770d3f09 100644
--- a/pymilvus/bulk_writer/remote_bulk_writer.py
+++ b/pymilvus/bulk_writer/remote_bulk_writer.py
@@ -11,6 +11,7 @@
 # the License.
 
 import logging
+import sys
 from pathlib import Path
 from typing import Any, Optional
 
@@ -22,6 +23,7 @@
 from .constants import (
     DEFAULT_BUCKET_NAME,
     MB,
+    BulkFileType,
 )
 from .local_bulk_writer import LocalBulkWriter
 
@@ -57,18 +59,25 @@ def __init__(
         self,
         schema: CollectionSchema,
         remote_path: str,
-        local_path: str,
         connect_param: ConnectParam,
         segment_size: int = 512 * MB,
+        file_type: BulkFileType = BulkFileType.NPY,
     ):
-        super().__init__(schema, local_path, segment_size)
-        uid = Path(super().data_path).name
-        self._remote_path = Path("/").joinpath(remote_path).joinpath(uid)
+        local_path = Path(sys.argv[0]).resolve().parent.joinpath("bulk_writer")
+        super().__init__(schema, str(local_path), segment_size, file_type)
+        self._remote_path = Path("/").joinpath(remote_path).joinpath(super().uuid)
         self._connect_param = connect_param
         self._client = None
         self._get_client()
+        self._remote_files = []
         logger.info(f"Remote buffer writer initialized, target path: {self._remote_path}")
 
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type: object, exc_val: object, exc_tb: object):
+        super().__exit__(exc_type, exc_val, exc_tb)
+
     def _get_client(self):
         try:
             if self._client is None:
@@ -110,7 +119,11 @@ def _remote_exists(self, file: str) -> bool:
 
     def _local_rm(self, file: str):
         try:
-            Path.unlink(file)
+            Path(file).unlink()
+            parent_dir = Path(file).parent
+            if not any(Path(parent_dir).iterdir()):
+                Path(parent_dir).rmdir()
+                logger.info(f"Delete empty directory '{parent_dir!s}'")
         except Exception:
             logger.warning(f"Failed to delete local file: {file}")
 
@@ -131,25 +144,33 @@ def _upload(self, file_list: list):
                 relative_file_path = str(file_path).replace(str(super().data_path), "")
                 minio_file_path = str(
                     Path.joinpath(self._remote_path, relative_file_path.lstrip("/"))
-                )
+                ).lstrip("/")
 
-                if not self._remote_exists(minio_file_path):
-                    minio_client.fput_object(
-                        bucket_name=self._connect_param._bucket_name,
-                        object_name=minio_file_path,
-                        file_path=file_path,
+                if self._remote_exists(minio_file_path):
+                    logger.info(
+                        f"Remote file '{minio_file_path}' already exists, will overwrite it"
                     )
-                    logger.info(f"Upload file '{file_path}' to '{minio_file_path}'")
-                else:
-                    logger.info(f"Remote file '{minio_file_path}' already exists")
-                remote_files.append(minio_file_path)
+
+                minio_client.fput_object(
+                    bucket_name=self._connect_param._bucket_name,
+                    object_name=minio_file_path,
+                    file_path=file_path,
+                )
+                logger.info(f"Upload file '{file_path}' to '{minio_file_path}'")
+
+                remote_files.append(str(minio_file_path))
                 self._local_rm(file_path)
         except Exception as e:
             self._throw(f"Failed to call MinIO/S3 api, error: {e}")
 
         logger.info(f"Successfully upload files: {file_list}")
+        self._remote_files.append(remote_files)
         return remote_files
 
     @property
     def data_path(self):
         return self._remote_path
+
+    @property
+    def batch_files(self):
+        return self._remote_files