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198 lines
8.2 KiB
Python
198 lines
8.2 KiB
Python
import pdb
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import copy
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import pytest
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import threading
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import datetime
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import logging
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from time import sleep
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from multiprocessing import Process
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import sklearn.preprocessing
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from utils import *
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index_file_size = 10
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vectors = gen_vectors(10000, default_dim)
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vectors = sklearn.preprocessing.normalize(vectors, axis=1, norm='l2')
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vectors = vectors.tolist()
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top_k = 1
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nprobe = 1
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epsilon = 0.001
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nlist = 128
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# index_params = {'index_type': IndexType.IVFLAT, 'nlist': 16384}
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default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 16384}, "metric_type": "L2"}
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class TestMixBase:
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# TODO
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def _test_mix_base(self, connect, collection):
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nb = 200000
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nq = 5
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entities = gen_entities(nb=nb)
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ids = connect.insert(collection, entities)
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assert len(ids) == nb
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connect.flush([collection])
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connect.create_index(collection, default_float_vec_field_name, default_index)
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index = connect.describe_index(collection, "")
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create_target_index(default_index, default_float_vec_field_name)
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assert index == default_index
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query, vecs = gen_query_vectors(default_float_vec_field_name, entities, default_top_k, nq)
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connect.load_collection(collection)
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res = connect.search(collection, query)
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assert len(res) == nq
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assert len(res[0]) == default_top_k
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assert res[0]._distances[0] <= epsilon
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assert check_id_result(res[0], ids[0])
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# disable
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def _test_search_during_createIndex(self, args):
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loops = 10000
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collection = gen_unique_str()
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query_vecs = [vectors[0], vectors[1]]
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uri = "tcp://%s:%s" % (args["ip"], args["port"])
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id_0 = 0;
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id_1 = 0
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milvus_instance = get_milvus(args["handler"])
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# milvus_instance.connect(uri=uri)
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milvus_instance.create_collection({'collection_name': collection,
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'dimension': default_dim,
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'index_file_size': index_file_size,
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'metric_type': "L2"})
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for i in range(10):
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status, ids = milvus_instance.bulk_insert(collection, vectors)
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# logging.getLogger().info(ids)
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if i == 0:
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id_0 = ids[0];
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id_1 = ids[1]
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# def create_index(milvus_instance):
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# logging.getLogger().info("In create index")
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# status = milvus_instance.create_index(collection, index_params)
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# logging.getLogger().info(status)
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# status, result = milvus_instance.get_index_info(collection)
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# logging.getLogger().info(result)
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def insert(milvus_instance):
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logging.getLogger().info("In add vectors")
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status, ids = milvus_instance.bulk_insert(collection, vectors)
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logging.getLogger().info(status)
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def search(milvus_instance):
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logging.getLogger().info("In search vectors")
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for i in range(loops):
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status, result = milvus_instance.search(collection, top_k, nprobe, query_vecs)
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logging.getLogger().info(status)
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assert result[0][0].id == id_0
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assert result[1][0].id == id_1
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milvus_instance = get_milvus(args["handler"])
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# milvus_instance.connect(uri=uri)
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p_search = Process(target=search, args=(milvus_instance,))
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p_search.start()
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milvus_instance = get_milvus(args["handler"])
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# milvus_instance.connect(uri=uri)
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p_create = Process(target=insert, args=(milvus_instance,))
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p_create.start()
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p_create.join()
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@pytest.mark.level(2)
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def _test_mix_multi_collections(self, connect):
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'''
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target: test functions with multiple collections of different metric_types and index_types
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method: create 60 collections which 30 are L2 and the other are IP, add vectors into them
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and test describe index and search
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expected: status ok
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'''
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nq = 10000
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collection_list = []
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idx = []
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index_param = {'nlist': nlist}
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# create collection and add vectors
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for i in range(30):
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collection_name = gen_unique_str('test_mix_multi_collections')
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collection_list.append(collection_name)
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param = {'collection_name': collection_name,
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'dimension': default_dim,
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'index_file_size': index_file_size,
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'metric_type': MetricType.L2}
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connect.create_collection(param)
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status, ids = connect.bulk_insert(collection_name=collection_name, records=vectors)
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idx.append(ids[0])
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idx.append(ids[10])
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idx.append(ids[20])
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assert status.OK()
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for i in range(30):
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collection_name = gen_unique_str('test_mix_multi_collections')
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collection_list.append(collection_name)
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param = {'collection_name': collection_name,
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'dimension': default_dim,
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'index_file_size': index_file_size,
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'metric_type': MetricType.IP}
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connect.create_collection(param)
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status, ids = connect.bulk_insert(collection_name=collection_name, records=vectors)
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assert status.OK()
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status = connect.flush([collection_name])
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assert status.OK()
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idx.append(ids[0])
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idx.append(ids[10])
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idx.append(ids[20])
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assert status.OK()
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for i in range(10):
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status = connect.create_index(collection_list[i], IndexType.FLAT, index_param)
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assert status.OK()
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status = connect.create_index(collection_list[30 + i], IndexType.FLAT, index_param)
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assert status.OK()
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status = connect.create_index(collection_list[10 + i], IndexType.IVFLAT, index_param)
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assert status.OK()
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status = connect.create_index(collection_list[40 + i], IndexType.IVFLAT, index_param)
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assert status.OK()
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status = connect.create_index(collection_list[20 + i], IndexType.IVF_SQ8, index_param)
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assert status.OK()
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status = connect.create_index(collection_list[50 + i], IndexType.IVF_SQ8, index_param)
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assert status.OK()
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# describe index
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for i in range(10):
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status, result = connect.get_index_info(collection_list[i])
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assert result._index_type == IndexType.FLAT
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status, result = connect.get_index_info(collection_list[10 + i])
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assert result._index_type == IndexType.IVFLAT
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status, result = connect.get_index_info(collection_list[20 + i])
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assert result._index_type == IndexType.IVF_SQ8
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status, result = connect.get_index_info(collection_list[30 + i])
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assert result._index_type == IndexType.FLAT
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status, result = connect.get_index_info(collection_list[40 + i])
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assert result._index_type == IndexType.IVFLAT
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status, result = connect.get_index_info(collection_list[50 + i])
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assert result._index_type == IndexType.IVF_SQ8
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# search
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query_vecs = [vectors[0], vectors[10], vectors[20]]
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for i in range(60):
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collection = collection_list[i]
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status, result = connect.search(collection, top_k, query_records=query_vecs, params={"nprobe": 1})
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assert status.OK()
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assert len(result) == len(query_vecs)
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logging.getLogger().info(i)
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for j in range(len(query_vecs)):
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assert len(result[j]) == top_k
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for j in range(len(query_vecs)):
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if not check_result(result[j], idx[3 * i + j]):
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logging.getLogger().info(result[j]._id_list)
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logging.getLogger().info(idx[3 * i + j])
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assert check_result(result[j], idx[3 * i + j])
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def check_result(result, id):
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if len(result) >= 5:
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return id in [result[0].id, result[1].id, result[2].id, result[3].id, result[4].id]
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else:
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return id in (i.id for i in result)
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def check_id_result(result, id):
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limit_in = 5
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ids = [entity.id for entity in result]
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if len(result) >= limit_in:
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return id in ids[:limit_in]
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else:
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return id in ids
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