From b5b4db32e7c79240aa91a13ea1990234f203180a Mon Sep 17 00:00:00 2001 From: groot Date: Tue, 12 Sep 2023 10:41:14 +0800 Subject: [PATCH] bulkwriter sample read csv (#1673) Signed-off-by: yhmo --- .gitignore | 3 + examples/data/train_embeddings.csv | 101 +++++++ examples/example_bulkwriter.py | 294 ++++++++++++++++----- pymilvus/bulk_writer/buffer.py | 42 ++- pymilvus/bulk_writer/bulk_writer.py | 30 ++- pymilvus/bulk_writer/constants.py | 20 +- pymilvus/bulk_writer/local_bulk_writer.py | 36 ++- pymilvus/bulk_writer/remote_bulk_writer.py | 51 ++-- 8 files changed, 474 insertions(+), 103 deletions(-) create mode 100644 examples/data/train_embeddings.csv 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 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0.05172787606716156, 0.02102244272828102, -0.03884565085172653, -0.006186930928379297, -0.049769602715969086, 0.09874702990055084, -0.006112928502261639, 0.009424751624464989]" 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