def create_index(self, field_name, index_type, metric_type, _async=False, index_param=None):
index_type = INDEX_MAP[index_type]
metric_type = utils.metric_type_trans(metric_type)
logger.info("Building index start, collection_name: %s, index_type: %s, metric_type: %s" % (
self._collection_name, index_type, metric_type))
if index_param:
logger.info(index_param)
index_params = {
"index_type": index_type,
"metric_type": metric_type,
"params": index_param
}
self._milvus.create_index(self._collection_name, field_name, index_params, _async=_async)
def load_query_rand(self, nq_max=100, timeout=None):
# for ivf search
dimension = 128
top_k = random.randint(1, 100)
nq = random.randint(1, nq_max)
nprobe = random.randint(1, 100)
search_param = {"nprobe": nprobe}
query_vectors = [[random.random() for _ in range(dimension)] for _ in range(nq)]
metric_type = random.choice(["l2", "ip"])
logger.info("%s, Search nq: %d, top_k: %d, nprobe: %d" % (self._collection_name, nq, top_k, nprobe))
vec_field_name = utils.get_default_field_name()
vector_query = {"vector": {vec_field_name: {
"topk": top_k,
"query": query_vectors,
"metric_type": utils.metric_type_trans(metric_type),
"params": search_param}
}}
self.load_and_query(vector_query, timeout=timeout)
def extract_cases(self, collection):
collection_name = collection[
"collection_name"] if "collection_name" in collection else None
(data_type, collection_size, dimension,
metric_type) = parser.collection_parser(collection_name)
run_count = collection["run_count"]
top_ks = collection["top_ks"]
nqs = collection["nqs"]
filters = collection["filters"] if "filters" in collection else []
search_params = collection["search_params"]
# TODO: get fields by describe_index
# fields = self.get_fields(self.milvus, collection_name)
fields = None
collection_info = {
"dimension": dimension,
"metric_type": metric_type,
"dataset_name": collection_name,
"collection_size": collection_size,
"fields": fields
}
# TODO: need to get index_info
index_info = None
vector_type = utils.get_vector_type(data_type)
index_field_name = utils.get_default_field_name(vector_type)
base_query_vectors = utils.get_vectors_from_binary(
utils.MAX_NQ, dimension, data_type)
cases = list()
case_metrics = list()
self.init_metric(self.name, collection_info, index_info, None)
for search_param in search_params:
logger.info("Search param: %s" % json.dumps(search_param))
for filter in filters:
filter_query = []
filter_param = []
if filter and isinstance(filter, dict):
if "range" in filter:
filter_query.append(eval(filter["range"]))
filter_param.append(filter["range"])
elif "term" in filter:
filter_query.append(eval(filter["term"]))
filter_param.append(filter["term"])
else:
raise Exception("%s not supported" % filter)
logger.info("filter param: %s" % json.dumps(filter_param))
for nq in nqs:
query_vectors = base_query_vectors[0:nq]
for top_k in top_ks:
search_info = {
"topk": top_k,
"query": query_vectors,
"metric_type":
utils.metric_type_trans(metric_type),
"params": search_param
}
# TODO: only update search_info
case_metric = copy.deepcopy(self.metric)
case_metric.set_case_metric_type()
case_metric.search = {
"nq": nq,
"topk": top_k,
"search_param": search_param,
"filter": filter_param
}
vector_query = {
"vector": {
index_field_name: search_info
}
}
case = {
"collection_name": collection_name,
"index_field_name": index_field_name,
"run_count": run_count,
"filter_query": filter_query,
"vector_query": vector_query,
}
cases.append(case)
case_metrics.append(case_metric)
return cases, case_metrics
def extract_cases(self, collection):
collection_name = collection[
"collection_name"] if "collection_name" in collection else None
(data_type, collection_size, dimension,
metric_type) = parser.collection_parser(collection_name)
build_index = collection[
"build_index"] if "build_index" in collection else False
index_type = collection[
"index_type"] if "index_type" in collection else None
index_param = collection[
"index_param"] if "index_param" in collection else None
run_count = collection["run_count"]
top_ks = collection["top_ks"]
nqs = collection["nqs"]
other_fields = collection[
"other_fields"] if "other_fields" in collection else None
filters = collection["filters"] if "filters" in collection else []
filter_query = []
search_params = collection["search_params"]
ni_per = collection["ni_per"]
# TODO: get fields by describe_index
# fields = self.get_fields(self.milvus, collection_name)
fields = None
collection_info = {
"dimension": dimension,
"metric_type": metric_type,
"dataset_name": collection_name,
"fields": fields
}
index_info = {"index_type": index_type, "index_param": index_param}
vector_type = utils.get_vector_type(data_type)
index_field_name = utils.get_default_field_name(vector_type)
# Get the path of the query.npy file stored on the NAS and get its data
base_query_vectors = utils.get_vectors_from_binary(
utils.MAX_NQ, dimension, data_type)
cases = list()
case_metrics = list()
self.init_metric(self.name, collection_info, index_info, None)
for search_param in search_params:
if not filters:
filters.append(None)
for filter in filters:
# filter_param = []
filter_query = []
if isinstance(filter, dict) and "range" in filter:
filter_query.append(eval(filter["range"]))
# filter_param.append(filter["range"])
if isinstance(filter, dict) and "term" in filter:
filter_query.append(eval(filter["term"]))
# filter_param.append(filter["term"])
# logger.info("filter param: %s" % json.dumps(filter_param))
for nq in nqs:
# Take nq groups of data for query
query_vectors = base_query_vectors[0:nq]
for top_k in top_ks:
search_info = {
"topk": top_k,
"query": query_vectors,
"metric_type":
utils.metric_type_trans(metric_type),
"params": search_param
}
# TODO: only update search_info
case_metric = copy.deepcopy(self.metric)
# set metric type as case
case_metric.set_case_metric_type()
case_metric.search = {
"nq": nq,
"topk": top_k,
"search_param": search_param,
"filter": filter_query
}
vector_query = {
"vector": {
index_field_name: search_info
}
}
case = {
"collection_name": collection_name,
"index_field_name": index_field_name,
"other_fields": other_fields,
"dimension": dimension,
"data_type": data_type,
"vector_type": vector_type,
"collection_size": collection_size,
"ni_per": ni_per,
"build_index": build_index,
"index_type": index_type,
"index_param": index_param,
"metric_type": metric_type,
"run_count": run_count,
"filter_query": filter_query,
"vector_query": vector_query,
}
cases.append(case)
case_metrics.append(case_metric)
return cases, case_metrics
def extract_cases(self, collection):
collection_name = collection[
"collection_name"] if "collection_name" in collection else None
(data_type, collection_size, dimension,
metric_type) = parser.collection_parser(collection_name)
vector_type = utils.get_vector_type(data_type)
index_field_name = utils.get_default_field_name(vector_type)
base_query_vectors = utils.get_vectors_from_binary(
utils.MAX_NQ, dimension, data_type)
collection_info = {
"dimension": dimension,
"metric_type": metric_type,
"dataset_name": collection_name,
"collection_size": collection_size
}
index_info = self.milvus.describe_index(index_field_name,
collection_name)
filters = collection["filters"] if "filters" in collection else []
filter_query = []
top_ks = collection["top_ks"]
nqs = collection["nqs"]
guarantee_timestamp = collection[
"guarantee_timestamp"] if "guarantee_timestamp" in collection else None
search_params = collection["search_params"]
search_params = utils.generate_combinations(search_params)
cases = list()
case_metrics = list()
self.init_metric(self.name,
collection_info,
index_info,
search_info=None)
for search_param in search_params:
if not filters:
filters.append(None)
for filter in filters:
filter_param = []
if isinstance(filter, dict) and "range" in filter:
filter_query.append(eval(filter["range"]))
filter_param.append(filter["range"])
if isinstance(filter, dict) and "term" in filter:
filter_query.append(eval(filter["term"]))
filter_param.append(filter["term"])
for nq in nqs:
query_vectors = base_query_vectors[0:nq]
for top_k in top_ks:
search_info = {
"topk": top_k,
"query": query_vectors,
"metric_type":
utils.metric_type_trans(metric_type),
"params": search_param
}
# TODO: only update search_info
case_metric = copy.deepcopy(self.metric)
# set metric type as case
case_metric.set_case_metric_type()
case_metric.search = {
"nq": nq,
"topk": top_k,
"search_param": search_param,
"filter": filter_param,
"guarantee_timestamp": guarantee_timestamp
}
vector_query = {
"vector": {
index_field_name: search_info
}
}
case = {
"collection_name": collection_name,
"index_field_name": index_field_name,
"dimension": dimension,
"data_type": data_type,
"metric_type": metric_type,
"vector_type": vector_type,
"collection_size": collection_size,
"filter_query": filter_query,
"vector_query": vector_query,
"guarantee_timestamp": guarantee_timestamp
}
cases.append(case)
case_metrics.append(case_metric)
return cases, case_metrics
def extract_cases(self, collection):
collection_name = collection[
"collection_name"] if "collection_name" in collection else None
(data_type, dimension,
metric_type) = parser.parse_ann_collection_name(collection_name)
# hdf5_source_file: The path of the source data file saved on the NAS
hdf5_source_file = collection["source_file"]
index_types = collection["index_types"]
index_params = collection["index_params"]
top_ks = collection["top_ks"]
nqs = collection["nqs"]
guarantee_timestamp = collection[
"guarantee_timestamp"] if "guarantee_timestamp" in collection else None
search_params = collection["search_params"]
vector_type = utils.get_vector_type(data_type)
index_field_name = utils.get_default_field_name(vector_type)
dataset = utils.get_dataset(hdf5_source_file)
collection_info = {
"dimension": dimension,
"metric_type": metric_type,
"dataset_name": collection_name
}
filters = collection["filters"] if "filters" in collection else []
filter_query = []
# Convert list data into a set of dictionary data
search_params = utils.generate_combinations(search_params)
index_params = utils.generate_combinations(index_params)
cases = list()
case_metrics = list()
self.init_metric(self.name, collection_info, {}, search_info=None)
# true_ids: The data set used to verify the results returned by query
true_ids = np.array(dataset["neighbors"])
for index_type in index_types:
for index_param in index_params:
index_info = {
"index_type": index_type,
"index_param": index_param
}
for search_param in search_params:
if not filters:
filters.append(None)
for filter in filters:
filter_param = []
if isinstance(filter, dict) and "range" in filter:
filter_query.append(eval(filter["range"]))
filter_param.append(filter["range"])
if isinstance(filter, dict) and "term" in filter:
filter_query.append(eval(filter["term"]))
filter_param.append(filter["term"])
for nq in nqs:
query_vectors = utils.normalize(
metric_type, np.array(dataset["test"][:nq]))
for top_k in top_ks:
search_info = {
"topk":
top_k,
"query":
query_vectors,
"metric_type":
utils.metric_type_trans(metric_type),
"params":
search_param
}
# TODO: only update search_info
case_metric = copy.deepcopy(self.metric)
# set metric type as case
case_metric.set_case_metric_type()
case_metric.index = index_info
case_metric.search = {
"nq": nq,
"topk": top_k,
"search_param": search_param,
"filter": filter_param,
"guarantee_timestamp": guarantee_timestamp
}
vector_query = {
"vector": {
index_field_name: search_info
}
}
case = {
"collection_name": collection_name,
"dataset": dataset,
"index_field_name": index_field_name,
"dimension": dimension,
"data_type": data_type,
"metric_type": metric_type,
"vector_type": vector_type,
"index_type": index_type,
"index_param": index_param,
"filter_query": filter_query,
"vector_query": vector_query,
"true_ids": true_ids,
"guarantee_timestamp": guarantee_timestamp
}
# Obtain the parameters of the use case to be tested
cases.append(case)
case_metrics.append(case_metric)
return cases, case_metrics
def extract_cases(self, collection):
collection_name = collection[
"collection_name"] if "collection_name" in collection else None
(data_type, dimension,
metric_type) = parser.parse_ann_collection_name(collection_name)
hdf5_source_file = collection["source_file"]
index_types = collection["index_types"]
index_params = collection["index_params"]
top_ks = collection["top_ks"]
nqs = collection["nqs"]
search_params = collection["search_params"]
vector_type = utils.get_vector_type(data_type)
index_field_name = utils.get_default_field_name(vector_type)
dataset = utils.get_dataset(hdf5_source_file)
collection_info = {
"dimension": dimension,
"metric_type": metric_type,
"dataset_name": collection_name
}
filters = collection["filters"] if "filters" in collection else []
filter_query = []
search_params = utils.generate_combinations(search_params)
index_params = utils.generate_combinations(index_params)
cases = list()
case_metrics = list()
self.init_metric(self.name, collection_info, {}, search_info=None)
true_ids = np.array(dataset["neighbors"])
for index_type in index_types:
for index_param in index_params:
index_info = {
"index_type": index_type,
"index_param": index_param
}
for search_param in search_params:
if not filters:
filters.append(None)
for filter in filters:
filter_param = []
if isinstance(filter, dict) and "range" in filter:
filter_query.append(eval(filter["range"]))
filter_param.append(filter["range"])
if isinstance(filter, dict) and "term" in filter:
filter_query.append(eval(filter["term"]))
filter_param.append(filter["term"])
for nq in nqs:
query_vectors = utils.normalize(
metric_type, np.array(dataset["test"][:nq]))
for top_k in top_ks:
search_info = {
"topk":
top_k,
"query":
query_vectors,
"metric_type":
utils.metric_type_trans(metric_type),
"params":
search_param
}
# TODO: only update search_info
case_metric = copy.deepcopy(self.metric)
case_metric.index = index_info
case_metric.search = {
"nq": nq,
"topk": top_k,
"search_param": search_param,
"filter": filter_param
}
vector_query = {
"vector": {
index_field_name: search_info
}
}
case = {
"collection_name": collection_name,
"dataset": dataset,
"index_field_name": index_field_name,
"dimension": dimension,
"data_type": data_type,
"metric_type": metric_type,
"vector_type": vector_type,
"index_type": index_type,
"index_param": index_param,
"filter_query": filter_query,
"vector_query": vector_query,
"true_ids": true_ids
}
cases.append(case)
case_metrics.append(case_metric)
return cases, case_metrics
