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123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
# Copyright (C) 2019-2020 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software distributed under the License
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# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
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# or implied. See the License for the specific language governing permissions and limitations under the License.
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import random
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import numpy as np
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import time
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import argparse
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from pymilvus import (
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connections, list_collections,
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FieldSchema, CollectionSchema, DataType,
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Collection, utility
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)
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TIMEOUT = 120
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def hello_milvus(collection_name):
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import time
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# create collection
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dim = 128
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default_fields = [
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FieldSchema(name="int64", dtype=DataType.INT64, is_primary=True),
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FieldSchema(name="float", dtype=DataType.FLOAT),
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FieldSchema(name="float_vector", dtype=DataType.FLOAT_VECTOR, dim=dim)
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]
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default_schema = CollectionSchema(fields=default_fields, description="test collection")
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if utility.has_collection(collection_name):
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print("collection is exist")
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collection = Collection(name=collection_name)
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default_schema = collection.schema
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dim = [field.params['dim'] for field in default_schema.fields if field.dtype in [101, 102]][0]
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print(f"\nCreate collection...")
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collection = Collection(name=collection_name, schema=default_schema)
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# insert data
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nb = 3000
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vectors = [[random.random() for _ in range(dim)] for _ in range(nb)]
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t0 = time.time()
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collection.insert(
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[
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[i for i in range(nb)],
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[np.float32(i) for i in range(nb)],
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vectors
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]
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)
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t1 = time.time()
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print(f"\nInsert {nb} vectors cost {t1 - t0:.4f} seconds")
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t0 = time.time()
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print(f"\nGet collection entities...")
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print(collection.num_entities)
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t1 = time.time()
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print(f"\nGet collection entities cost {t1 - t0:.4f} seconds")
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# create index and load table
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default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
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print(f"\nCreate index...")
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t0 = time.time()
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collection.create_index(field_name="float_vector", index_params=default_index)
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t1 = time.time()
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print(f"\nCreate index cost {t1 - t0:.4f} seconds")
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print(f"\nload collection...")
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t0 = time.time()
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collection.load()
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t1 = time.time()
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print(f"\nload collection cost {t1 - t0:.4f} seconds")
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# load and search
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topK = 5
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search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
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t0 = time.time()
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print(f"\nSearch...")
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# define output_fields of search result
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res = collection.search(
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vectors[-2:], "float_vector", search_params, topK,
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"int64 > 100", output_fields=["int64", "float"], timeout=TIMEOUT
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)
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t1 = time.time()
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print(f"search cost {t1 - t0:.4f} seconds")
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# show result
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for hits in res:
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for hit in hits:
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# Get value of the random value field for search result
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print(hit, hit.entity.get("float"))
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# query
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expr = "int64 in [2,4,6,8]"
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output_fields = ["int64", "float"]
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res = collection.query(expr, output_fields, timeout=TIMEOUT)
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sorted_res = sorted(res, key=lambda k: k['int64'])
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for r in sorted_res:
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print(r)
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collection.release()
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parser = argparse.ArgumentParser(description='host ip')
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parser.add_argument('--host', type=str, default='10.96.77.209', help='host ip')
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args = parser.parse_args()
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# add time stamp
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print(f"\nStart time: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))}")
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# create connection
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connections.connect(host=args.host, port="19530")
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print(f"\nList collections...")
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collection_list = list_collections()
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print(collection_list)
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# keep 10 collections with prefix "CreateChecker_", others will be skiped
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cnt = 0
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for collection_name in collection_list:
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if collection_name.startswith("CreateChecker_"):
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cnt += 1
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if collection_name.startswith("CreateChecker_") and cnt > 10:
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continue
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print(f"check collection {collection_name}")
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hello_milvus(collection_name)
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