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