Bulkwriter supports parquet

examples/example_bulkwriter.py

@@ -44,8 +44,8 @@
 HOST = '127.0.0.1'
 PORT = '19530'
 
-CSV_COLLECTION_NAME = "test_csv"
-ALL_TYPES_COLLECTION_NAME = "test_all_types"
+SIMPLE_COLLECTION_NAME = "for_bulkwriter"
+ALL_TYPES_COLLECTION_NAME = "all_types_for_bulkwriter"
 DIM = 512
 
 def gen_binary_vector():
@@ -62,10 +62,10 @@ def create_connection():
     print(f"\nConnected")
 
 
-def build_csv_collection():
+def build_simple_collection():
     print(f"\n===================== create collection ====================")
-    if utility.has_collection(CSV_COLLECTION_NAME):
-        utility.drop_collection(CSV_COLLECTION_NAME)
+    if utility.has_collection(SIMPLE_COLLECTION_NAME):
+        utility.drop_collection(SIMPLE_COLLECTION_NAME)
 
     fields = [
         FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
@@ -74,7 +74,7 @@ def build_csv_collection():
         FieldSchema(name="label", dtype=DataType.VARCHAR, max_length=512),
     ]
     schema = CollectionSchema(fields=fields)
-    collection = Collection(name=CSV_COLLECTION_NAME, schema=schema)
+    collection = Collection(name=SIMPLE_COLLECTION_NAME, schema=schema)
     print(f"Collection '{collection.name}' created")
     return collection.schema
 
@@ -110,37 +110,59 @@ def read_sample_data(file_path: str, writer: [LocalBulkWriter, RemoteBulkWriter]
 
         writer.append_row(row)
 
-def test_local_writer_json(schema: CollectionSchema):
-    print(f"\n===================== test local JSON writer ====================")
+def local_writer(schema: CollectionSchema, file_type: BulkFileType):
+    print(f"\n===================== local writer ({file_type.name}) ====================")
     with LocalBulkWriter(
             schema=schema,
             local_path="/tmp/bulk_writer",
-            segment_size=4*1024*1024,
-            file_type=BulkFileType.JSON_RB,
+            segment_size=128*1024*1024,
+            file_type=file_type,
     ) as local_writer:
+        # read data from csv
         read_sample_data("./data/train_embeddings.csv", local_writer)
+
+        # append rows
+        for i in range(100000):
+            local_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
+
+        print(f"{local_writer.total_row_count} rows appends")
+        print(f"{local_writer.buffer_row_count} rows in buffer not flushed")
         local_writer.commit()
         batch_files = local_writer.batch_files
 
-    print(f"Test local writer done! output local files: {batch_files}")
+    print(f"Local writer done! output local files: {batch_files}")
 
 
-def test_local_writer_npy(schema: CollectionSchema):
-    print(f"\n===================== test local npy writer ====================")
-    with LocalBulkWriter(
+def remote_writer(schema: CollectionSchema, file_type: BulkFileType):
+    print(f"\n===================== remote writer ({file_type.name}) ====================")
+    with RemoteBulkWriter(
             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
+            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=512 * 1024 * 1024,
+            file_type=file_type,
+    ) as remote_writer:
+        # read data from csv
+        read_sample_data("./data/train_embeddings.csv", remote_writer)
+
+        # append rows
+        for i in range(10000):
+            remote_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
 
-    print(f"Test local writer done! output local files: {batch_files}")
+        print(f"{remote_writer.total_row_count} rows appends")
+        print(f"{remote_writer.buffer_row_count} rows in buffer not flushed")
+        remote_writer.commit()
+        batch_files = remote_writer.batch_files
 
+    print(f"Remote writer done! output remote files: {batch_files}")
 
-def test_parallel_append(schema: CollectionSchema):
-    print(f"\n===================== test parallel append ====================")
+def parallel_append(schema: CollectionSchema):
+    print(f"\n===================== parallel append ====================")
     def _append_row(writer: LocalBulkWriter, begin: int, end: int):
         try:
             for i in range(begin, end):
@@ -169,6 +191,8 @@ def _append_row(writer: LocalBulkWriter, begin: int, end: int):
         th.join()
         print(f"Thread '{th.name}' finished")
 
+    print(f"{local_writer.total_row_count} rows appends")
+    print(f"{local_writer.buffer_row_count} rows in buffer not flushed")
     local_writer.commit()
     print(f"Append finished, {thread_count*rows_per_thread} rows")
 
@@ -192,8 +216,8 @@ def _append_row(writer: LocalBulkWriter, begin: int, end: int):
     print("Data is correct")
 
 
-def test_remote_writer(schema: CollectionSchema):
-    print(f"\n===================== test remote writer ====================")
+def all_types_writer(bin_vec: bool, schema: CollectionSchema, file_type: BulkFileType)->list:
+    print(f"\n===================== all field types ({file_type.name}) binary_vector={bin_vec} ====================")
     with RemoteBulkWriter(
             schema=schema,
             remote_path="bulk_data",
@@ -203,33 +227,7 @@ def test_remote_writer(schema: CollectionSchema):
                 secret_key=MINIO_SECRET_KEY,
                 bucket_name="a-bucket",
             ),
-            segment_size=50 * 1024 * 1024,
-    ) as remote_writer:
-        # read data from csv
-        read_sample_data("./data/train_embeddings.csv", remote_writer)
-        remote_writer.commit()
-
-        # append rows
-        for i in range(10000):
-            remote_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
-        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 ====================")
-    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",
-        ),
+            file_type=file_type,
     ) as remote_writer:
         print("Append rows")
         batch_count = 10000
@@ -267,14 +265,16 @@ def test_all_types_writer(bin_vec: bool, schema: CollectionSchema)->list:
                 f"dynamic_{i}": i,
             })
 
+        print(f"{remote_writer.total_row_count} rows appends")
+        print(f"{remote_writer.buffer_row_count} rows in buffer not flushed")
         print("Generate data files...")
         remote_writer.commit()
         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 ====================")
+def call_bulkinsert(schema: CollectionSchema, batch_files: list):
+    print(f"\n===================== call bulkinsert ====================")
     if utility.has_collection(ALL_TYPES_COLLECTION_NAME):
         utility.drop_collection(ALL_TYPES_COLLECTION_NAME)
 
@@ -302,7 +302,7 @@ def test_call_bulkinsert(schema: CollectionSchema, batch_files: list):
     print(f"Collection row number: {collection.num_entities}")
 
 
-def test_retrieve_imported_data(bin_vec: bool):
+def retrieve_imported_data(bin_vec: bool):
     collection = Collection(name=ALL_TYPES_COLLECTION_NAME)
     print("Create index...")
     index_param = {
@@ -326,7 +326,7 @@ def test_retrieve_imported_data(bin_vec: bool):
     for item in results:
         print(item)
 
-def test_cloud_bulkinsert():
+def cloud_bulkinsert():
     url = "https://_your_cloud_server_url_"
     api_key = "_api_key_for_the_url_"
     cluster_id = "_your_cloud_instance_id_"
@@ -371,24 +371,31 @@ def test_cloud_bulkinsert():
 if __name__ == '__main__':
     create_connection()
 
-    schema = build_csv_collection()
-    test_local_writer_json(schema)
-    test_local_writer_npy(schema)
-    test_remote_writer(schema)
-    test_parallel_append(schema)
+    file_types = [BulkFileType.JSON_RB, BulkFileType.NPY, BulkFileType.PARQUET]
+
+    schema = build_simple_collection()
+    for file_type in file_types:
+        local_writer(schema=schema, file_type=file_type)
+
+    for file_type in file_types:
+        remote_writer(schema=schema, file_type=file_type)
+
+    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)
+    for file_type in file_types:
+        batch_files = all_types_writer(bin_vec=False, schema=schema, file_type=file_type)
+        call_bulkinsert(schema, batch_files)
+        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)
+    for file_type in file_types:
+        batch_files = all_types_writer(bin_vec=True, schema=schema, file_type=file_type)
+        call_bulkinsert(schema, batch_files)
+        retrieve_imported_data(bin_vec=True)
 
-    # # to test cloud bulkinsert api, you need to apply a cloud service from Zilliz Cloud(https://zilliz.com/cloud)
-    # test_cloud_bulkinsert()
+    # # to call cloud bulkinsert api, you need to apply a cloud service from Zilliz Cloud(https://zilliz.com/cloud)
+    # cloud_bulkinsert()
 

pymilvus/bulk_writer/buffer.py

@@ -15,6 +15,7 @@
 from pathlib import Path
 
 import numpy as np
+import pandas as pd
 
 from pymilvus.client.types import (
     DataType,
@@ -109,6 +110,8 @@ def persist(self, local_path: str) -> list:
             return self._persist_npy(local_path)
         if self._file_type == BulkFileType.JSON_RB:
             return self._persist_json_rows(local_path)
+        if self._file_type == BulkFileType.PARQUET:
+            return self._persist_parquet(local_path)
 
         self._throw(f"Unsupported file tpye: {self._file_type}")
         return []
@@ -174,3 +177,36 @@ def _persist_json_rows(self, local_path: str):
 
         logger.info(f"Successfully persist row-based file {file_path}")
         return [str(file_path)]
+
+    def _persist_parquet(self, local_path: str):
+        file_path = Path(local_path + ".parquet")
+
+        data = {}
+        for k in self._buffer:
+            field_schema = self._fields[k]
+            if field_schema.dtype == DataType.JSON:
+                # for JSON field, store as string array
+                str_arr = []
+                for val in self._buffer[k]:
+                    str_arr.append(json.dumps(val))
+                data[k] = pd.Series(str_arr, dtype=None)
+            elif field_schema.dtype == DataType.FLOAT_VECTOR:
+                arr = []
+                for val in self._buffer[k]:
+                    arr.append(np.array(val, dtype=np.dtype("float32")))
+                data[k] = pd.Series(arr)
+            elif field_schema.dtype == DataType.BINARY_VECTOR:
+                arr = []
+                for val in self._buffer[k]:
+                    arr.append(np.array(val, dtype=np.dtype("uint8")))
+                data[k] = pd.Series(arr)
+            elif field_schema.dtype.name in NUMPY_TYPE_CREATOR:
+                dt = NUMPY_TYPE_CREATOR[field_schema.dtype.name]
+                data[k] = pd.Series(self._buffer[k], dtype=dt)
+
+        # write to Parquet file
+        data_frame = pd.DataFrame(data=data)
+        data_frame.to_parquet(file_path, engine="pyarrow")  # don't use fastparquet
+
+        logger.info(f"Successfully persist parquet file {file_path}")
+        return [str(file_path)]

pymilvus/bulk_writer/bulk_writer.py

@@ -43,6 +43,7 @@ def __init__(
         self._schema = schema
         self._buffer_size = 0
         self._buffer_row_count = 0
+        self._total_row_count = 0
         self._segment_size = segment_size
         self._file_type = file_type
         self._buffer_lock = Lock()
@@ -64,6 +65,10 @@ def buffer_size(self):
     def buffer_row_count(self):
         return self._buffer_row_count
 
+    @property
+    def total_row_count(self):
+        return self._total_row_count
+
     @property
     def segment_size(self):
         return self._segment_size
@@ -165,3 +170,4 @@ def _verify_row(self, row: dict):
         with self._buffer_lock:
             self._buffer_size = self._buffer_size + row_size
             self._buffer_row_count = self._buffer_row_count + 1
+            self._total_row_count = self._total_row_count + 1

pymilvus/bulk_writer/constants.py

@@ -46,6 +46,7 @@
     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, list) and len(x) * 8 == dim,
+    DataType.ARRAY.name: lambda x: isinstance(x, list),
 }
 
 NUMPY_TYPE_CREATOR = {
@@ -60,9 +61,11 @@
     DataType.JSON.name: None,
     DataType.FLOAT_VECTOR.name: np.dtype("float32"),
     DataType.BINARY_VECTOR.name: np.dtype("uint8"),
+    DataType.ARRAY.name: None,
 }
 
 
 class BulkFileType(IntEnum):
     NPY = 1
     JSON_RB = 2
+    PARQUET = 3

pymilvus/bulk_writer/local_bulk_writer.py

@@ -45,6 +45,7 @@ def __init__(
         self._working_thread = {}
         self._working_thread_lock = Lock()
         self._local_files = []
+
         self._make_dir()
 
     @property
@@ -61,25 +62,29 @@ def __del__(self):
         self._exit()
 
     def _exit(self):
-        # remove the uuid folder
-        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}'")
-
         # wait flush thread
         if len(self._working_thread) > 0:
             for k, th in self._working_thread.items():
                 logger.info(f"Wait flush thread '{k}' to finish")
                 th.join()
 
+        self._rm_dir()
+
     def _make_dir(self):
         Path(self._local_path).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
         Path(uidir).mkdir(exist_ok=True)
         logger.info(f"Data path created: {uidir}")
 
+    def _rm_dir(self):
+        # remove the uuid folder if it is empty
+        if Path(self._local_path).exists() and not any(Path(self._local_path).iterdir()):
+            Path(self._local_path).rmdir()
+            logger.info(f"Delete local directory '{self._local_path}'")
+
     def append_row(self, row: dict, **kwargs):
         super().append_row(row, **kwargs)