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milvus/tests-deprecating/python_test/utils.py
binbin eff75c7701
Replace sdk source and merge tests and tests20 (#7182) 
Signed-off-by: Binbin Lv <binbin.lv@zilliz.com>
2021-08-20 11:00:56 +08:00

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import os
import sys
import random
import pdb
import string
import struct
import logging
import threading
import traceback
import time
import copy
import numpy as np
from sklearn import preprocessing
from pymilvus import Milvus, DataType
port = 19530
epsilon = 0.000001
namespace = "milvus"
default_flush_interval = 1
big_flush_interval = 1000
default_drop_interval = 3
default_dim = 128
default_nb = 3000
default_top_k = 10
max_top_k = 16384
# max_partition_num = 256
max_partition_num = 4096
default_segment_row_limit = 1000
default_server_segment_row_limit = 1024 * 512
default_float_vec_field_name = "float_vector"
default_binary_vec_field_name = "binary_vector"
default_partition_name = "_default"
default_tag = "1970_01_01"
row_count = "row_count"
# TODO:
# TODO: disable RHNSW_SQ/PQ in 0.11.0
all_index_types = [
"FLAT",
"IVF_FLAT",
"IVF_SQ8",
# "IVF_SQ8_HYBRID",
"IVF_PQ",
"HNSW",
# "NSG",
"ANNOY",
"RHNSW_FLAT",
"RHNSW_PQ",
"RHNSW_SQ",
"BIN_FLAT",
"BIN_IVF_FLAT"
]
default_index_params = [
{"nlist": 128},
{"nlist": 128},
{"nlist": 128},
# {"nlist": 128},
{"nlist": 128, "m": 16, "nbits": 8},
{"M": 48, "efConstruction": 500},
# {"search_length": 50, "out_degree": 40, "candidate_pool_size": 100, "knng": 50},
{"n_trees": 50},
{"M": 48, "efConstruction": 500},
{"M": 48, "efConstruction": 500, "PQM": 64},
{"M": 48, "efConstruction": 500},
{"nlist": 128},
{"nlist": 128}
]
def create_target_index(index,field_name):
index["field_name"]=field_name
def index_cpu_not_support():
return ["IVF_SQ8_HYBRID"]
def binary_support():
return ["BIN_FLAT", "BIN_IVF_FLAT"]
def delete_support():
return ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_SQ8_HYBRID", "IVF_PQ"]
def ivf():
return ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_SQ8_HYBRID", "IVF_PQ"]
def skip_pq():
return ["IVF_PQ", "RHNSW_PQ", "RHNSW_SQ"]
def binary_metrics():
return ["JACCARD", "HAMMING", "TANIMOTO", "SUBSTRUCTURE", "SUPERSTRUCTURE"]
def structure_metrics():
return ["SUBSTRUCTURE", "SUPERSTRUCTURE"]
def l2(x, y):
return np.linalg.norm(np.array(x) - np.array(y))
def ip(x, y):
return np.inner(np.array(x), np.array(y))
def jaccard(x, y):
x = np.asarray(x, np.bool)
y = np.asarray(y, np.bool)
return 1 - np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum())
def hamming(x, y):
x = np.asarray(x, np.bool)
y = np.asarray(y, np.bool)
return np.bitwise_xor(x, y).sum()
def tanimoto(x, y):
x = np.asarray(x, np.bool)
y = np.asarray(y, np.bool)
return -np.log2(np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum()))
def substructure(x, y):
x = np.asarray(x, np.bool)
y = np.asarray(y, np.bool)
return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(y)
def superstructure(x, y):
x = np.asarray(x, np.bool)
y = np.asarray(y, np.bool)
return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(x)
def get_milvus(host, port, uri=None, handler=None, **kwargs):
if handler is None:
handler = "GRPC"
try_connect = kwargs.get("try_connect", True)
if uri is not None:
milvus = Milvus(uri=uri, handler=handler, try_connect=try_connect)
else:
milvus = Milvus(host=host, port=port, handler=handler, try_connect=try_connect)
return milvus
def reset_build_index_threshold(connect):
connect.set_config("engine", "build_index_threshold", 1024)
def disable_flush(connect):
connect.set_config("storage", "auto_flush_interval", big_flush_interval)
def enable_flush(connect):
# reset auto_flush_interval=1
connect.set_config("storage", "auto_flush_interval", default_flush_interval)
config_value = connect.get_config("storage", "auto_flush_interval")
assert config_value == str(default_flush_interval)
def gen_inaccuracy(num):
return num / 255.0
def gen_vectors(num, dim, is_normal=True):
vectors = [[random.random() for _ in range(dim)] for _ in range(num)]
vectors = preprocessing.normalize(vectors, axis=1, norm='l2')
return vectors.tolist()
# def gen_vectors(num, dim, seed=np.random.RandomState(1234), is_normal=False):
# xb = seed.rand(num, dim).astype("float32")
# xb = preprocessing.normalize(xb, axis=1, norm='l2')
# return xb.tolist()
def gen_binary_vectors(num, dim):
raw_vectors = []
binary_vectors = []
for i in range(num):
raw_vector = [random.randint(0, 1) for i in range(dim)]
raw_vectors.append(raw_vector)
binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
return raw_vectors, binary_vectors
def gen_binary_sub_vectors(vectors, length):
raw_vectors = []
binary_vectors = []
dim = len(vectors[0])
for i in range(length):
raw_vector = [0 for i in range(dim)]
vector = vectors[i]
for index, j in enumerate(vector):
if j == 1:
raw_vector[index] = 1
raw_vectors.append(raw_vector)
binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
return raw_vectors, binary_vectors
def gen_binary_super_vectors(vectors, length):
raw_vectors = []
binary_vectors = []
dim = len(vectors[0])
for i in range(length):
cnt_1 = np.count_nonzero(vectors[i])
# raw_vector = [0 for i in range(dim)] ???
raw_vector = [1 for i in range(dim)]
raw_vectors.append(raw_vector)
binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
return raw_vectors, binary_vectors
def gen_int_attr(row_num):
return [random.randint(0, 255) for _ in range(row_num)]
def gen_float_attr(row_num):
return [random.uniform(0, 255) for _ in range(row_num)]
def gen_unique_str(str_value=None):
prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8))
return "test_" + prefix if str_value is None else str_value + "_" + prefix
def gen_primary_field():
return {"name": gen_unique_str(), "type": DataType.INT64, "is_primary": True}
def gen_single_filter_fields():
fields = []
for data_type in DataType:
if data_type in [DataType.INT32, DataType.INT64, DataType.FLOAT, DataType.DOUBLE]:
fields.append({"name": data_type.name, "type": data_type})
return fields
def gen_single_vector_fields():
fields = []
for data_type in [DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR]:
field = {"name": data_type.name, "type": data_type, "params": {"dim": default_dim}}
fields.append(field)
return fields
def gen_default_fields(auto_id=True):
default_fields = {
"fields": [
{"name": "int64", "type": DataType.INT64, "is_primary": True},
{"name": "float", "type": DataType.FLOAT},
{"name": default_float_vec_field_name, "type": DataType.FLOAT_VECTOR, "params": {"dim": default_dim}},
],
"segment_row_limit": default_segment_row_limit,
}
return default_fields
def gen_binary_default_fields(auto_id=True):
default_fields = {
"fields": [
{"name": "int64", "type": DataType.INT64, "is_primary": True},
{"name": "float", "type": DataType.FLOAT},
{"name": default_binary_vec_field_name, "type": DataType.BINARY_VECTOR, "params": {"dim": default_dim}}
],
"segment_row_limit": default_segment_row_limit,
"auto_id": auto_id
}
return default_fields
def gen_entities(nb, is_normal=False):
vectors = gen_vectors(nb, default_dim, is_normal)
entities = [
{"name": "int64", "type": DataType.INT64, "values": [i for i in range(nb)]},
{"name": "float", "type": DataType.FLOAT, "values": [float(i) for i in range(nb)]},
{"name": default_float_vec_field_name, "type": DataType.FLOAT_VECTOR, "values": vectors}
]
return entities
def gen_entities_new(nb, is_normal=False):
vectors = gen_vectors(nb, default_dim, is_normal)
entities = [
{"name": "int64", "values": [i for i in range(nb)]},
{"name": "float", "values": [float(i) for i in range(nb)]},
{"name": default_float_vec_field_name, "values": vectors}
]
return entities
def gen_entities_rows(nb, is_normal=False, _id=True):
vectors = gen_vectors(nb, default_dim, is_normal)
entities = []
if not _id:
for i in range(nb):
entity = {
"_id": i,
"int64": i,
"float": float(i),
default_float_vec_field_name: vectors[i]
}
entities.append(entity)
else:
for i in range(nb):
entity = {
"int64": i,
"float": float(i),
default_float_vec_field_name: vectors[i]
}
entities.append(entity)
return entities
def gen_binary_entities(nb):
raw_vectors, vectors = gen_binary_vectors(nb, default_dim)
entities = [
{"name": "int64", "type": DataType.INT64, "values": [i for i in range(nb)]},
{"name": "float", "type": DataType.FLOAT, "values": [float(i) for i in range(nb)]},
{"name": default_binary_vec_field_name, "type": DataType.BINARY_VECTOR, "values": vectors}
]
return raw_vectors, entities
def gen_binary_entities_new(nb):
raw_vectors, vectors = gen_binary_vectors(nb, default_dim)
entities = [
{"name": "int64", "values": [i for i in range(nb)]},
{"name": "float", "values": [float(i) for i in range(nb)]},
{"name": default_binary_vec_field_name, "values": vectors}
]
return raw_vectors, entities
def gen_binary_entities_rows(nb, _id=True):
raw_vectors, vectors = gen_binary_vectors(nb, default_dim)
entities = []
if not _id:
for i in range(nb):
entity = {
"_id": i,
"int64": i,
"float": float(i),
default_binary_vec_field_name: vectors[i]
}
entities.append(entity)
else:
for i in range(nb):
entity = {
"int64": i,
"float": float(i),
default_binary_vec_field_name: vectors[i]
}
entities.append(entity)
return raw_vectors, entities
def gen_entities_by_fields(fields, nb, dim, ids=None):
entities = []
for field in fields:
if field.get("is_primary", False) and ids:
field_value = ids
elif field["type"] in [DataType.INT32, DataType.INT64]:
field_value = [1 for i in range(nb)]
elif field["type"] in [DataType.FLOAT, DataType.DOUBLE]:
field_value = [3.0 for i in range(nb)]
elif field["type"] == DataType.BINARY_VECTOR:
field_value = gen_binary_vectors(nb, dim)[1]
elif field["type"] == DataType.FLOAT_VECTOR:
field_value = gen_vectors(nb, dim)
field.update({"values": field_value})
entities.append(field)
return entities
def assert_equal_entity(a, b):
pass
def gen_query_vectors(field_name, entities, top_k, nq, search_params={"nprobe": 10}, rand_vector=False,
metric_type="L2", replace_vecs=None):
if rand_vector is True:
dimension = len(entities[-1]["values"][0])
query_vectors = gen_vectors(nq, dimension)
else:
query_vectors = entities[-1]["values"][:nq]
if replace_vecs:
query_vectors = replace_vecs
must_param = {"vector": {field_name: {"topk": top_k, "query": query_vectors, "params": search_params}}}
must_param["vector"][field_name]["metric_type"] = metric_type
query = {
"bool": {
"must": [must_param]
}
}
return query, query_vectors
def update_query_expr(src_query, keep_old=True, expr=None):
tmp_query = copy.deepcopy(src_query)
if expr is not None:
tmp_query["bool"].update(expr)
if keep_old is not True:
tmp_query["bool"].pop("must")
return tmp_query
def gen_default_vector_expr(default_query):
return default_query["bool"]["must"][0]
def gen_default_term_expr(keyword="term", field="int64", values=None):
if values is None:
values = [i for i in range(default_nb // 2)]
expr = {keyword: {field: {"values": values}}}
return expr
def update_term_expr(src_term, terms):
tmp_term = copy.deepcopy(src_term)
for term in terms:
tmp_term["term"].update(term)
return tmp_term
def gen_default_range_expr(keyword="range", field="int64", ranges=None):
if ranges is None:
ranges = {"GT": 1, "LT": default_nb // 2}
expr = {keyword: {field: ranges}}
return expr
def update_range_expr(src_range, ranges):
tmp_range = copy.deepcopy(src_range)
for range in ranges:
tmp_range["range"].update(range)
return tmp_range
def gen_invalid_range():
range = [
{"range": 1},
{"range": {}},
{"range": []},
{"range": {"range": {"int64": {"GT": 0, "LT": default_nb // 2}}}}
]
return range
def gen_valid_ranges():
ranges = [
{"GT": 0, "LT": default_nb // 2},
{"GT": default_nb // 2, "LT": default_nb * 2},
{"GT": 0},
{"LT": default_nb},
{"GT": -1, "LT": default_top_k},
]
return ranges
def gen_invalid_term():
terms = [
{"term": 1},
{"term": []},
{"term": {}},
{"term": {"term": {"int64": {"values": [i for i in range(default_nb // 2)]}}}}
]
return terms
def add_field_default(default_fields, type=DataType.INT64, field_name=None):
tmp_fields = copy.deepcopy(default_fields)
if field_name is None:
field_name = gen_unique_str()
field = {
"name": field_name,
"type": type
}
tmp_fields["fields"].append(field)
return tmp_fields
def add_field(entities, field_name=None):
nb = len(entities[0]["values"])
tmp_entities = copy.deepcopy(entities)
if field_name is None:
field_name = gen_unique_str()
field = {
"name": field_name,
"type": DataType.INT64,
"values": [i for i in range(nb)]
}
tmp_entities.append(field)
return tmp_entities
def add_vector_field(entities, is_normal=False):
nb = len(entities[0]["values"])
vectors = gen_vectors(nb, default_dim, is_normal)
field = {
"name": gen_unique_str(),
"type": DataType.FLOAT_VECTOR,
"values": vectors
}
entities.append(field)
return entities
# def update_fields_metric_type(fields, metric_type):
# tmp_fields = copy.deepcopy(fields)
# if metric_type in ["L2", "IP"]:
# tmp_fields["fields"][-1]["type"] = DataType.FLOAT_VECTOR
# else:
# tmp_fields["fields"][-1]["type"] = DataType.BINARY_VECTOR
# tmp_fields["fields"][-1]["params"]["metric_type"] = metric_type
# return tmp_fields
def remove_field(entities):
del entities[0]
return entities
def remove_vector_field(entities):
del entities[-1]
return entities
def update_field_name(entities, old_name, new_name):
tmp_entities = copy.deepcopy(entities)
for item in tmp_entities:
if item["name"] == old_name:
item["name"] = new_name
return tmp_entities
def update_field_type(entities, old_name, new_name):
tmp_entities = copy.deepcopy(entities)
for item in tmp_entities:
if item["name"] == old_name:
item["type"] = new_name
return tmp_entities
def update_field_value(entities, old_type, new_value):
tmp_entities = copy.deepcopy(entities)
for item in tmp_entities:
if item["type"] == old_type:
for index, value in enumerate(item["values"]):
item["values"][index] = new_value
return tmp_entities
def update_field_name_row(entities, old_name, new_name):
tmp_entities = copy.deepcopy(entities)
for item in tmp_entities:
if old_name in item:
item[new_name] = item[old_name]
item.pop(old_name)
else:
raise Exception("Field %s not in field" % old_name)
return tmp_entities
def update_field_type_row(entities, old_name, new_name):
tmp_entities = copy.deepcopy(entities)
for item in tmp_entities:
if old_name in item:
item["type"] = new_name
return tmp_entities
def add_vector_field(nb, dimension=default_dim):
field_name = gen_unique_str()
field = {
"name": field_name,
"type": DataType.FLOAT_VECTOR,
"values": gen_vectors(nb, dimension)
}
return field_name
def gen_segment_row_limits():
sizes = [
1024,
4096
]
return sizes
def gen_invalid_ips():
ips = [
# "255.0.0.0",
# "255.255.0.0",
# "255.255.255.0",
# "255.255.255.255",
"127.0.0",
# "123.0.0.2",
"12-s",
" ",
"12 s",
"BB。A",
" siede ",
"(mn)",
"中文",
"a".join("a" for _ in range(256))
]
return ips
def gen_invalid_uris():
ip = None
uris = [
" ",
"中文",
# invalid protocol
# "tc://%s:%s" % (ip, port),
# "tcp%s:%s" % (ip, port),
# # invalid port
# "tcp://%s:100000" % ip,
# "tcp://%s: " % ip,
# "tcp://%s:19540" % ip,
# "tcp://%s:-1" % ip,
# "tcp://%s:string" % ip,
# invalid ip
"tcp:// :19530",
# "tcp://123.0.0.1:%s" % port,
"tcp://127.0.0:19530",
# "tcp://255.0.0.0:%s" % port,
# "tcp://255.255.0.0:%s" % port,
# "tcp://255.255.255.0:%s" % port,
# "tcp://255.255.255.255:%s" % port,
"tcp://\n:19530",
]
return uris
def gen_invalid_strs():
strings = [
1,
[1],
None,
"12-s",
# " ",
# "",
# None,
"12 s",
"(mn)",
"中文",
"a".join("a" for i in range(256))
]
return strings
def gen_invalid_field_types():
field_types = [
# 1,
"=c",
# 0,
None,
"",
"a".join("a" for i in range(256))
]
return field_types
def gen_invalid_metric_types():
metric_types = [
1,
"=c",
0,
None,
"",
"a".join("a" for i in range(256))
]
return metric_types
# TODO:
def gen_invalid_ints():
int_values = [
# 1.0,
None,
[1, 2, 3],
" ",
"",
-1,
"String",
"=c",
"中文",
"a".join("a" for i in range(256))
]
return int_values
def gen_invalid_params():
params = [
9999999999,
-1,
# None,
[1, 2, 3],
" ",
"",
"String",
"中文"
]
return params
def gen_invalid_vectors():
invalid_vectors = [
"1*2",
[],
[1],
[1, 2],
[" "],
['a'],
[None],
None,
(1, 2),
{"a": 1},
" ",
"",
"String",
" siede ",
"中文",
"a".join("a" for i in range(256))
]
return invalid_vectors
def gen_invaild_search_params():
invalid_search_key = 100
search_params = []
for index_type in all_index_types:
if index_type == "FLAT":
continue
search_params.append({"index_type": index_type, "search_params": {"invalid_key": invalid_search_key}})
if index_type in delete_support():
for nprobe in gen_invalid_params():
ivf_search_params = {"index_type": index_type, "search_params": {"nprobe": nprobe}}
search_params.append(ivf_search_params)
elif index_type in ["HNSW", "RHNSW_PQ", "RHNSW_SQ"]:
for ef in gen_invalid_params():
hnsw_search_param = {"index_type": index_type, "search_params": {"ef": ef}}
search_params.append(hnsw_search_param)
elif index_type == "NSG":
for search_length in gen_invalid_params():
nsg_search_param = {"index_type": index_type, "search_params": {"search_length": search_length}}
search_params.append(nsg_search_param)
search_params.append({"index_type": index_type, "search_params": {"invalid_key": 100}})
elif index_type == "ANNOY":
for search_k in gen_invalid_params():
if isinstance(search_k, int):
continue
annoy_search_param = {"index_type": index_type, "search_params": {"search_k": search_k}}
search_params.append(annoy_search_param)
return search_params
def gen_invalid_index():
index_params = []
for index_type in gen_invalid_strs():
index_param = {"index_type": index_type, "params": {"nlist": 1024}}
index_params.append(index_param)
for nlist in gen_invalid_params():
index_param = {"index_type": "IVF_FLAT", "params": {"nlist": nlist}}
index_params.append(index_param)
for M in gen_invalid_params():
index_param = {"index_type": "HNSW", "params": {"M": M, "efConstruction": 100}}
index_param = {"index_type": "RHNSW_PQ", "params": {"M": M, "efConstruction": 100}}
index_param = {"index_type": "RHNSW_SQ", "params": {"M": M, "efConstruction": 100}}
index_params.append(index_param)
for efConstruction in gen_invalid_params():
index_param = {"index_type": "HNSW", "params": {"M": 16, "efConstruction": efConstruction}}
index_param = {"index_type": "RHNSW_PQ", "params": {"M": 16, "efConstruction": efConstruction}}
index_param = {"index_type": "RHNSW_SQ", "params": {"M": 16, "efConstruction": efConstruction}}
index_params.append(index_param)
for search_length in gen_invalid_params():
index_param = {"index_type": "NSG",
"params": {"search_length": search_length, "out_degree": 40, "candidate_pool_size": 50,
"knng": 100}}
index_params.append(index_param)
for out_degree in gen_invalid_params():
index_param = {"index_type": "NSG",
"params": {"search_length": 100, "out_degree": out_degree, "candidate_pool_size": 50,
"knng": 100}}
index_params.append(index_param)
for candidate_pool_size in gen_invalid_params():
index_param = {"index_type": "NSG", "params": {"search_length": 100, "out_degree": 40,
"candidate_pool_size": candidate_pool_size,
"knng": 100}}
index_params.append(index_param)
index_params.append({"index_type": "IVF_FLAT", "params": {"invalid_key": 1024}})
index_params.append({"index_type": "HNSW", "params": {"invalid_key": 16, "efConstruction": 100}})
index_params.append({"index_type": "RHNSW_PQ", "params": {"invalid_key": 16, "efConstruction": 100}})
index_params.append({"index_type": "RHNSW_SQ", "params": {"invalid_key": 16, "efConstruction": 100}})
index_params.append({"index_type": "NSG",
"params": {"invalid_key": 100, "out_degree": 40, "candidate_pool_size": 300,
"knng": 100}})
for invalid_n_trees in gen_invalid_params():
index_params.append({"index_type": "ANNOY", "params": {"n_trees": invalid_n_trees}})
return index_params
def gen_index():
nlists = [1, 1024, 16384]
pq_ms = [128, 64, 32, 16, 8, 4]
Ms = [5, 24, 48]
efConstructions = [100, 300, 500]
search_lengths = [10, 100, 300]
out_degrees = [5, 40, 300]
candidate_pool_sizes = [50, 100, 300]
knngs = [5, 100, 300]
index_params = []
for index_type in all_index_types:
if index_type in ["FLAT", "BIN_FLAT", "BIN_IVF_FLAT"]:
index_params.append({"index_type": index_type, "index_param": {"nlist": 1024}})
elif index_type in ["IVF_FLAT", "IVF_SQ8", "IVF_SQ8_HYBRID"]:
ivf_params = [{"index_type": index_type, "index_param": {"nlist": nlist}} \
for nlist in nlists]
index_params.extend(ivf_params)
elif index_type == "IVF_PQ":
IVFPQ_params = [{"index_type": index_type, "index_param": {"nlist": nlist, "m": m}} \
for nlist in nlists \
for m in pq_ms]
index_params.extend(IVFPQ_params)
elif index_type in ["HNSW", "RHNSW_SQ", "RHNSW_PQ"]:
hnsw_params = [{"index_type": index_type, "index_param": {"M": M, "efConstruction": efConstruction}} \
for M in Ms \
for efConstruction in efConstructions]
index_params.extend(hnsw_params)
elif index_type == "NSG":
nsg_params = [{"index_type": index_type,
"index_param": {"search_length": search_length, "out_degree": out_degree,
"candidate_pool_size": candidate_pool_size, "knng": knng}} \
for search_length in search_lengths \
for out_degree in out_degrees \
for candidate_pool_size in candidate_pool_sizes \
for knng in knngs]
index_params.extend(nsg_params)
return index_params
def gen_simple_index():
index_params = []
for i in range(len(all_index_types)):
if all_index_types[i] in binary_support():
continue
dic = {"index_type": all_index_types[i], "metric_type": "L2"}
dic.update({"params": default_index_params[i]})
index_params.append(dic)
return index_params
def gen_binary_index():
index_params = []
for i in range(len(all_index_types)):
if all_index_types[i] in binary_support():
dic = {"index_type": all_index_types[i]}
dic.update({"params": default_index_params[i]})
index_params.append(dic)
return index_params
def gen_normal_expressions():
expressions = [
"int64 > 0",
"int64 > 0 && int64 < 2021", # range
"int64 == 0 || int64 == 1 || int64 == 2 || int64 == 3", # term
]
return expressions
def get_search_param(index_type, metric_type="L2"):
search_params = {"metric_type": metric_type}
if index_type in ivf() or index_type in binary_support():
search_params.update({"nprobe": 64})
elif index_type in ["HNSW", "RHNSW_FLAT", "RHNSW_SQ", "RHNSW_PQ"]:
search_params.update({"ef": 64})
elif index_type == "NSG":
search_params.update({"search_length": 100})
elif index_type == "ANNOY":
search_params.update({"search_k": 1000})
else:
logging.getLogger().error("Invalid index_type.")
raise Exception("Invalid index_type.")
return search_params
def assert_equal_vector(v1, v2):
if len(v1) != len(v2):
assert False
for i in range(len(v1)):
assert abs(v1[i] - v2[i]) < epsilon
def restart_server(helm_release_name):
res = True
timeout = 120
from kubernetes import client, config
client.rest.logger.setLevel(logging.WARNING)
# service_name = "%s.%s.svc.cluster.local" % (helm_release_name, namespace)
config.load_kube_config()
v1 = client.CoreV1Api()
pod_name = None
# config_map_names = v1.list_namespaced_config_map(namespace, pretty='true')
# body = {"replicas": 0}
pods = v1.list_namespaced_pod(namespace)
for i in pods.items:
if i.metadata.name.find(helm_release_name) != -1 and i.metadata.name.find("mysql") == -1:
pod_name = i.metadata.name
break
# v1.patch_namespaced_config_map(config_map_name, namespace, body, pretty='true')
# status_res = v1.read_namespaced_service_status(helm_release_name, namespace, pretty='true')
logging.getLogger().debug("Pod name: %s" % pod_name)
if pod_name is not None:
try:
v1.delete_namespaced_pod(pod_name, namespace)
except Exception as e:
logging.error(str(e))
logging.error("Exception when calling CoreV1Api->delete_namespaced_pod")
res = False
return res
logging.error("Sleep 10s after pod deleted")
time.sleep(10)
# check if restart successfully
pods = v1.list_namespaced_pod(namespace)
for i in pods.items:
pod_name_tmp = i.metadata.name
logging.error(pod_name_tmp)
if pod_name_tmp == pod_name:
continue
elif pod_name_tmp.find(helm_release_name) == -1 or pod_name_tmp.find("mysql") != -1:
continue
else:
status_res = v1.read_namespaced_pod_status(pod_name_tmp, namespace, pretty='true')
logging.error(status_res.status.phase)
start_time = time.time()
ready_break = False
while time.time() - start_time <= timeout:
logging.error(time.time())
status_res = v1.read_namespaced_pod_status(pod_name_tmp, namespace, pretty='true')
if status_res.status.phase == "Running":
logging.error("Already running")
ready_break = True
time.sleep(10)
break
else:
time.sleep(1)
if time.time() - start_time > timeout:
logging.error("Restart pod: %s timeout" % pod_name_tmp)
res = False
return res
if ready_break:
break
else:
raise Exception("Pod: %s not found" % pod_name)
follow = True
pretty = True
previous = True # bool | Return previous terminated container logs. Defaults to false. (optional)
since_seconds = 56 # int | A relative time in seconds before the current time from which to show logs. If this value precedes the time a pod was started, only logs since the pod start will be returned. If this value is in the future, no logs will be returned. Only one of sinceSeconds or sinceTime may be specified. (optional)
timestamps = True # bool | If true, add an RFC3339 or RFC3339Nano timestamp at the beginning of every line of log output. Defaults to false. (optional)
container = "milvus"
# start_time = time.time()
# while time.time() - start_time <= timeout:
# try:
# api_response = v1.read_namespaced_pod_log(pod_name_tmp, namespace, container=container, follow=follow,
# pretty=pretty, previous=previous, since_seconds=since_seconds,
# timestamps=timestamps)
# logging.error(api_response)
# return res
# except Exception as e:
# logging.error("Exception when calling CoreV1Api->read_namespaced_pod_log: %s\n" % e)
# # waiting for server start
# time.sleep(5)
# # res = False
# # return res
# if time.time() - start_time > timeout:
# logging.error("Restart pod: %s timeout" % pod_name_tmp)
# res = False
return res
def compare_list_elements(_first, _second):
if not isinstance(_first, list) or not isinstance(_second, list) or len(_first) != len(_second):
return False
for ele in _first:
if ele not in _second:
return False
return True
class MyThread(threading.Thread):
def __init__(self, target, args=()):
threading.Thread.__init__(self, target=target, args=args)
def run(self):
self.exc = None
try:
super(MyThread, self).run()
except BaseException as e:
self.exc = e
logging.error(traceback.format_exc())
def join(self):
super(MyThread, self).join()
if self.exc:
raise self.exc
class CaseLabel:
tags_smoke = "smoke"
L1 = "L1"
L2 = "L2"
L3 = "L3"
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