def run(self, definition, run_type=None):
if run_type == "performance":
for op_type, op_value in definition.items():
# run docker mode
run_count = op_value["run_count"]
run_params = op_value["params"]
container = None
if op_type == "insert":
if not run_params:
logger.debug("No run params")
continue
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["table_name"]
volume_name = param["db_path_prefix"]
print(table_name)
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
for k, v in param.items():
if k.startswith("server."):
# Update server config
utils.modify_config(k,
v,
type="server",
db_slave=None)
container = utils.run_server(self.image,
test_type="remote",
volume_name=volume_name,
db_slave=None)
time.sleep(2)
milvus = MilvusClient(table_name)
# Check has table or not
if milvus.exists_table():
milvus.delete()
time.sleep(10)
milvus.create_table(table_name, dimension,
index_file_size, metric_type)
# debug
# milvus.create_index("ivf_sq8", 16384)
res = self.do_insert(milvus, table_name, data_type,
dimension, table_size,
param["ni_per"])
logger.info(res)
# wait for file merge
time.sleep(table_size * dimension / 5000000)
# Clear up
utils.remove_container(container)
elif op_type == "query":
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
volume_name = param["db_path_prefix"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
for k, v in param.items():
if k.startswith("server."):
utils.modify_config(k, v, type="server")
container = utils.run_server(self.image,
test_type="remote",
volume_name=volume_name,
db_slave=None)
time.sleep(2)
milvus = MilvusClient(table_name)
logger.debug(milvus.show_tables())
# Check has table or not
if not milvus.exists_table():
logger.warning(
"Table %s not existed, continue exec next params ..."
% table_name)
continue
# parse index info
index_types = param["index.index_types"]
nlists = param["index.nlists"]
# parse top-k, nq, nprobe
top_ks, nqs, nprobes = parser.search_params_parser(
param)
for index_type in index_types:
for nlist in nlists:
result = milvus.describe_index()
logger.info(result)
# milvus.drop_index()
# milvus.create_index(index_type, nlist)
result = milvus.describe_index()
logger.info(result)
logger.info(milvus.count())
# preload index
milvus.preload_table()
logger.info("Start warm up query")
res = self.do_query(milvus, table_name, [1],
[1], 1, 1)
logger.info("End warm up query")
# Run query test
for nprobe in nprobes:
logger.info(
"index_type: %s, nlist: %s, metric_type: %s, nprobe: %s"
% (index_type, nlist, metric_type,
nprobe))
res = self.do_query(
milvus, table_name, top_ks, nqs,
nprobe, run_count)
headers = ["Nq/Top-k"]
headers.extend(
[str(top_k) for top_k in top_ks])
utils.print_table(headers, nqs, res)
utils.remove_container(container)
elif run_type == "insert_performance":
for op_type, op_value in definition.items():
# run docker mode
run_count = op_value["run_count"]
run_params = op_value["params"]
container = None
if not run_params:
logger.debug("No run params")
continue
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["table_name"]
volume_name = param["db_path_prefix"]
print(table_name)
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
for k, v in param.items():
if k.startswith("server."):
# Update server config
utils.modify_config(k,
v,
type="server",
db_slave=None)
container = utils.run_server(self.image,
test_type="remote",
volume_name=volume_name,
db_slave=None)
time.sleep(2)
milvus = MilvusClient(table_name)
# Check has table or not
if milvus.exists_table():
milvus.delete()
time.sleep(10)
milvus.create_table(table_name, dimension, index_file_size,
metric_type)
# debug
# milvus.create_index("ivf_sq8", 16384)
res = self.do_insert(milvus, table_name, data_type,
dimension, table_size,
param["ni_per"])
logger.info(res)
# wait for file merge
time.sleep(table_size * dimension / 5000000)
# Clear up
utils.remove_container(container)
elif run_type == "search_performance":
for op_type, op_value in definition.items():
# run docker mode
run_count = op_value["run_count"]
run_params = op_value["params"]
container = None
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
volume_name = param["db_path_prefix"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
for k, v in param.items():
if k.startswith("server."):
utils.modify_config(k, v, type="server")
container = utils.run_server(self.image,
test_type="remote",
volume_name=volume_name,
db_slave=None)
time.sleep(2)
milvus = MilvusClient(table_name)
logger.debug(milvus.show_tables())
# Check has table or not
if not milvus.exists_table():
logger.warning(
"Table %s not existed, continue exec next params ..."
% table_name)
continue
# parse index info
index_types = param["index.index_types"]
nlists = param["index.nlists"]
# parse top-k, nq, nprobe
top_ks, nqs, nprobes = parser.search_params_parser(param)
for index_type in index_types:
for nlist in nlists:
result = milvus.describe_index()
logger.info(result)
# milvus.drop_index()
# milvus.create_index(index_type, nlist)
result = milvus.describe_index()
logger.info(result)
logger.info(milvus.count())
# preload index
milvus.preload_table()
logger.info("Start warm up query")
res = self.do_query(milvus, table_name, [1], [1],
1, 1)
logger.info("End warm up query")
# Run query test
for nprobe in nprobes:
logger.info(
"index_type: %s, nlist: %s, metric_type: %s, nprobe: %s"
% (index_type, nlist, metric_type, nprobe))
res = self.do_query(milvus, table_name, top_ks,
nqs, nprobe, run_count)
headers = ["Nq/Top-k"]
headers.extend(
[str(top_k) for top_k in top_ks])
utils.print_table(headers, nqs, res)
utils.remove_container(container)
elif run_type == "accuracy":
"""
{
"dataset": "random_50m_1024_512",
"index.index_types": ["flat", ivf_flat", "ivf_sq8"],
"index.nlists": [16384],
"nprobes": [1, 32, 128],
"nqs": [100],
"top_ks": [1, 64],
"server.use_blas_threshold": 1100,
"server.cpu_cache_capacity": 256
}
"""
for op_type, op_value in definition.items():
if op_type != "query":
logger.warning(
"invalid operation: %s in accuracy test, only support query operation"
% op_type)
break
run_count = op_value["run_count"]
run_params = op_value["params"]
container = None
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
sift_acc = False
if "sift_acc" in param:
sift_acc = param["sift_acc"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
for k, v in param.items():
if k.startswith("server."):
utils.modify_config(k, v, type="server")
volume_name = param["db_path_prefix"]
container = utils.run_server(self.image,
test_type="remote",
volume_name=volume_name,
db_slave=None)
time.sleep(2)
milvus = MilvusClient(table_name)
# Check has table or not
if not milvus.exists_table():
logger.warning(
"Table %s not existed, continue exec next params ..."
% table_name)
continue
# parse index info
index_types = param["index.index_types"]
nlists = param["index.nlists"]
# parse top-k, nq, nprobe
top_ks, nqs, nprobes = parser.search_params_parser(param)
if sift_acc is True:
# preload groundtruth data
true_ids_all = self.get_groundtruth_ids(table_size)
acc_dict = {}
for index_type in index_types:
for nlist in nlists:
result = milvus.describe_index()
logger.info(result)
milvus.create_index(index_type, nlist)
# preload index
milvus.preload_table()
# Run query test
for nprobe in nprobes:
logger.info(
"index_type: %s, nlist: %s, metric_type: %s, nprobe: %s"
% (index_type, nlist, metric_type, nprobe))
for top_k in top_ks:
for nq in nqs:
result_ids = []
id_prefix = "%s_index_%s_nlist_%s_metric_type_%s_nprobe_%s_top_k_%s_nq_%s" % \
(table_name, index_type, nlist, metric_type, nprobe, top_k, nq)
if sift_acc is False:
self.do_query_acc(
milvus, table_name, top_k, nq,
nprobe, id_prefix)
if index_type != "flat":
# Compute accuracy
base_name = "%s_index_flat_nlist_%s_metric_type_%s_nprobe_%s_top_k_%s_nq_%s" % \
(table_name, nlist, metric_type, nprobe, top_k, nq)
avg_acc = self.compute_accuracy(
base_name, id_prefix)
logger.info(
"Query: <%s> accuracy: %s"
% (id_prefix, avg_acc))
else:
result_ids, result_distances = self.do_query_ids(
milvus, table_name, top_k, nq,
nprobe)
debug_file_ids = "0.5.3_result_ids"
debug_file_distances = "0.5.3_result_distances"
with open(debug_file_ids,
"w+") as fd:
total = 0
for index, item in enumerate(
result_ids):
true_item = true_ids_all[:
nq, :
top_k].tolist(
)[index]
tmp = set(
item).intersection(
set(true_item))
total = total + len(tmp)
fd.write(
"query: N-%d, intersection: %d, total: %d\n"
% (index, len(tmp),
total))
fd.write("%s\n" %
str(item))
fd.write("%s\n" %
str(true_item))
acc_value = self.get_recall_value(
true_ids_all[:nq, :top_k].
tolist(), result_ids)
logger.info(
"Query: <%s> accuracy: %s" %
(id_prefix, acc_value))
# # print accuracy table
# headers = [table_name]
# headers.extend([str(top_k) for top_k in top_ks])
# utils.print_table(headers, nqs, res)
# remove container, and run next definition
logger.info("remove container, and run next definition")
utils.remove_container(container)
elif run_type == "stability":
for op_type, op_value in definition.items():
if op_type != "query":
logger.warning(
"invalid operation: %s in accuracy test, only support query operation"
% op_type)
break
run_count = op_value["run_count"]
run_params = op_value["params"]
container = None
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
index_type = param["index_type"]
volume_name = param["db_path_prefix"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
# set default test time
if "during_time" not in param:
during_time = 100 # seconds
else:
during_time = int(param["during_time"]) * 60
# set default query process num
if "query_process_num" not in param:
query_process_num = 10
else:
query_process_num = int(param["query_process_num"])
for k, v in param.items():
if k.startswith("server."):
utils.modify_config(k, v, type="server")
container = utils.run_server(self.image,
test_type="remote",
volume_name=volume_name,
db_slave=None)
time.sleep(2)
milvus = MilvusClient(table_name)
# Check has table or not
if not milvus.exists_table():
logger.warning(
"Table %s not existed, continue exec next params ..."
% table_name)
continue
start_time = time.time()
insert_vectors = [[
random.random() for _ in range(dimension)
] for _ in range(10000)]
i = 0
while time.time() < start_time + during_time:
i = i + 1
processes = []
# do query
# for i in range(query_process_num):
# milvus_instance = MilvusClient(table_name)
# top_k = random.choice([x for x in range(1, 100)])
# nq = random.choice([x for x in range(1, 100)])
# nprobe = random.choice([x for x in range(1, 1000)])
# # logger.info("index_type: %s, nlist: %s, metric_type: %s, nprobe: %s" % (index_type, nlist, metric_type, nprobe))
# p = Process(target=self.do_query, args=(milvus_instance, table_name, [top_k], [nq], [nprobe], run_count, ))
# processes.append(p)
# p.start()
# time.sleep(0.1)
# for p in processes:
# p.join()
milvus_instance = MilvusClient(table_name)
top_ks = random.sample([x for x in range(1, 100)], 3)
nqs = random.sample([x for x in range(1, 1000)], 3)
nprobe = random.choice([x for x in range(1, 500)])
res = self.do_query(milvus, table_name, top_ks, nqs,
nprobe, run_count)
if i % 10 == 0:
status, res = milvus_instance.insert(
insert_vectors,
ids=[x for x in range(len(insert_vectors))])
if not status.OK():
logger.error(status)
# status = milvus_instance.drop_index()
# if not status.OK():
# logger.error(status)
# index_type = random.choice(["flat", "ivf_flat", "ivf_sq8"])
milvus_instance.create_index(index_type, 16384)
result = milvus.describe_index()
logger.info(result)
# milvus_instance.create_index("ivf_sq8", 16384)
utils.remove_container(container)
else:
logger.warning("Run type: %s not supported" % run_type)
def run(self, definition, run_type=None):
if run_type == "performance":
for op_type, op_value in definition.items():
run_count = op_value["run_count"]
run_params = op_value["params"]
if op_type == "insert":
for index, param in enumerate(run_params):
table_name = param["table_name"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
milvus = MilvusClient(table_name,
ip=self.ip,
port=self.port)
# Check has table or not
if milvus.exists_table():
milvus.delete()
time.sleep(10)
milvus.create_table(table_name, dimension,
index_file_size, metric_type)
res = self.do_insert(milvus, table_name, data_type,
dimension, table_size,
param["ni_per"])
logger.info(res)
elif op_type == "query":
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
milvus = MilvusClient(table_name,
ip=self.ip,
port=self.port)
logger.info(milvus.describe())
logger.info(milvus.describe_index())
logger.info(milvus.count())
logger.info(milvus.show_tables())
# parse index info
index_types = param["index.index_types"]
nlists = param["index.nlists"]
# parse top-k, nq, nprobe
top_ks, nqs, nprobes = parser.search_params_parser(
param)
# milvus.drop_index()
for index_type in index_types:
for nlist in nlists:
# milvus.create_index(index_type, nlist)
# preload index
logger.info("Start preloading table")
milvus.preload_table()
logger.info("End preloading table")
# Run query test
logger.info("Start warm up query")
res = self.do_query(milvus, table_name, [1],
[1], 1, 2)
logger.info("End warm up query")
for nprobe in nprobes:
logger.info(
"index_type: %s, nlist: %s, metric_type: %s, nprobe: %s"
% (index_type, nlist, metric_type,
nprobe))
res = self.do_query(
milvus, table_name, top_ks, nqs,
nprobe, run_count)
headers = ["nq/topk"]
headers.extend(
[str(top_k) for top_k in top_ks])
utils.print_table(headers, nqs, res)
elif run_type == "accuracy":
for op_type, op_value in definition.items():
if op_type != "query":
logger.warning(
"invalid operation: %s in accuracy test, only support query operation"
% op_type)
break
run_count = op_value["run_count"]
run_params = op_value["params"]
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
sift_acc = False
if "sift_acc" in param:
sift_acc = param["sift_acc"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
milvus = MilvusClient(table_name,
ip=self.ip,
port=self.port)
logger.debug(milvus.show_tables())
# Check has table or not
if not milvus.exists_table():
logger.warning(
"Table %s not existed, continue exec next params ..."
% table_name)
continue
# parse index info
index_types = param["index.index_types"]
nlists = param["index.nlists"]
# parse top-k, nq, nprobe
top_ks, nqs, nprobes = parser.search_params_parser(param)
if sift_acc is True:
# preload groundtruth data
true_ids_all = self.get_groundtruth_ids(table_size)
acc_dict = {}
for index_type in index_types:
for nlist in nlists:
result = milvus.describe_index()
logger.info(result)
# milvus.drop_index()
milvus.create_index(index_type, nlist)
# preload index
milvus.preload_table()
# Run query test
for nprobe in nprobes:
logger.info(
"index_type: %s, nlist: %s, metric_type: %s, nprobe: %s"
% (index_type, nlist, metric_type, nprobe))
for top_k in top_ks:
for nq in nqs:
result_ids = []
id_prefix = "%s_index_%s_nlist_%s_metric_type_%s_nprobe_%s_top_k_%s_nq_%s" % \
(table_name, index_type, nlist, metric_type, nprobe, top_k, nq)
if sift_acc is False:
self.do_query_acc(
milvus, table_name, top_k, nq,
nprobe, id_prefix)
if index_type != "flat":
# Compute accuracy
base_name = "%s_index_flat_nlist_%s_metric_type_%s_nprobe_%s_top_k_%s_nq_%s" % \
(table_name, nlist, metric_type, nprobe, top_k, nq)
avg_acc = self.compute_accuracy(
base_name, id_prefix)
logger.info(
"Query: <%s> accuracy: %s"
% (id_prefix, avg_acc))
else:
result_ids, result_distances = self.do_query_ids(
milvus, table_name, top_k, nq,
nprobe)
debug_file_ids = "0.5.3_result_ids"
debug_file_distances = "0.5.3_result_distances"
with open(debug_file_ids,
"w+") as fd:
total = 0
for index, item in enumerate(
result_ids):
true_item = true_ids_all[:
nq, :
top_k].tolist(
)[index]
tmp = set(
item).intersection(
set(true_item))
total = total + len(tmp)
fd.write(
"query: N-%d, intersection: %d, total: %d\n"
% (index, len(tmp),
total))
fd.write("%s\n" %
str(item))
fd.write("%s\n" %
str(true_item))
acc_value = self.get_recall_value(
true_ids_all[:nq, :top_k].
tolist(), result_ids)
logger.info(
"Query: <%s> accuracy: %s" %
(id_prefix, acc_value))
# # print accuracy table
# headers = [table_name]
# headers.extend([str(top_k) for top_k in top_ks])
# utils.print_table(headers, nqs, res)
elif run_type == "stability":
for op_type, op_value in definition.items():
if op_type != "query":
logger.warning(
"invalid operation: %s in accuracy test, only support query operation"
% op_type)
break
run_count = op_value["run_count"]
run_params = op_value["params"]
nq = 100000
for index, param in enumerate(run_params):
logger.info("Definition param: %s" % str(param))
table_name = param["dataset"]
index_type = param["index_type"]
(data_type, table_size, index_file_size, dimension,
metric_type) = parser.table_parser(table_name)
# set default test time
if "during_time" not in param:
during_time = 100 # seconds
else:
during_time = int(param["during_time"]) * 60
# set default query process num
if "query_process_num" not in param:
query_process_num = 10
else:
query_process_num = int(param["query_process_num"])
milvus = MilvusClient(table_name,
ip=self.ip,
port=self.port)
logger.debug(milvus.show_tables())
logger.debug(milvus.describe_index())
logger.debug(milvus.count())
# Check has table or not
if not milvus.exists_table():
logger.warning(
"Table %s not existed, continue exec next params ..."
% table_name)
continue
start_time = time.time()
insert_vectors = [[
random.random() for _ in range(dimension)
] for _ in range(nq)]
i = 0
while time.time() < start_time + during_time:
# processes = []
# # do query
# for i in range(query_process_num):
# milvus_instance = MilvusClient(table_name)
# top_k = random.choice([x for x in range(1, 100)])
# nq = random.choice([x for x in range(1, 1000)])
# nprobe = random.choice([x for x in range(1, 500)])
# logger.info(nprobe)
# p = Process(target=self.do_query, args=(milvus_instance, table_name, [top_k], [nq], 64, run_count, ))
# processes.append(p)
# p.start()
# time.sleep(0.1)
# for p in processes:
# p.join()
i = i + 1
milvus_instance = MilvusClient(table_name,
ip=self.ip,
port=self.port)
top_ks = random.sample([x for x in range(1, 100)], 1)
nqs = random.sample([x for x in range(1, 200)], 2)
nprobe = random.choice([x for x in range(1, 100)])
res = self.do_query(milvus_instance, table_name,
top_ks, nqs, nprobe, run_count)
# milvus_instance = MilvusClient(table_name)
status, res = milvus_instance.insert(
insert_vectors,
ids=[x for x in range(len(insert_vectors))])
if not status.OK():
logger.error(status.message)
logger.debug(milvus.count())
res = self.do_query(milvus_instance, table_name,
top_ks, nqs, nprobe, run_count)