utils.gen_entities() - Code Metrics - Inspection of "Fix master unittest (#2980)" - milvus-io/milvus - Measure and Improve Code Quality continuously with Scrutinizer

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created 2020-07-23 06:08 UTC

utils.gen_entities() A

↳ Parent: utils

Complexity

Conditions

Size

Total Lines	9
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	8
nop	2
dl	0
loc	9
rs	10
c	0
b	0
f	0

import os
import sys
import random
import pdb
import string
import struct
import logging
import time, datetime
import copy
import numpy as np
from sklearn import preprocessing
from milvus import Milvus, IndexType, MetricType, DataType

port = 19530
epsilon = 0.000001
default_flush_interval = 1
big_flush_interval = 1000
dimension = 128
segment_size = 10

# TODO:
all_index_types = [
    "FLAT",
    "IVF_FLAT",
    "IVF_SQ8",
    "IVF_SQ8_HYBRID",
    "IVF_PQ",
    "HNSW",
    # "NSG",
    "ANNOY",
    "BIN_FLAT",
    "BIN_IVF_FLAT"
]


default_index_params = [
    {"nlist": 1024},
    {"nlist": 1024},
    {"nlist": 1024},
    {"nlist": 1024},
    {"nlist": 1024, "m": 16},
    {"M": 48, "efConstruction": 500},
    # {"search_length": 50, "out_degree": 40, "candidate_pool_size": 100, "knng": 50},
    {"n_trees": 4},
    {"nlist": 1024},
    {"nlist": 1024}
]


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 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 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=False):
    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 = [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_single_filter_fields():
    fields = []
    for data_type in DataType:
        if data_type in [DataType.INT8, DataType.INT16, DataType.INT32, DataType.INT64, DataType.FLOAT, DataType.DOUBLE]:
            fields.append({"field": data_type.name, "type": data_type})
    return fields


def gen_single_vector_fields():
    fields = []
    for metric_type in ['HAMMING', 'IP', 'JACCARD', 'L2', 'SUBSTRUCTURE', 'SUPERSTRUCTURE', 'TANIMOTO']:
        for data_type in [DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR]:
            if metric_type in ["L2", "IP"] and data_type == DataType.BINARY_VECTOR:
                continue
            if metric_type not in ["L2", "IP"] and data_type == DataType.FLOAT_VECTOR:
                continue
            field = {"field": data_type.name, "type": data_type, "params": {"metric_type": metric_type, "dimension": dimension}}
            fields.append(field)
    return fields


def gen_default_fields():
    default_fields = {
        "fields": [
            {"field": "int8", "type": DataType.INT8},
            {"field": "int64", "type": DataType.INT64},
            {"field": "float", "type": DataType.FLOAT},
            {"field": "vector", "type": DataType.FLOAT_VECTOR, "params": {"metric_type": "L2", "dimension": dimension}}
        ],
        "segment_size": segment_size
    }
    return default_fields


def gen_entities(nb, is_normal=False):
    vectors = gen_vectors(nb, dimension, is_normal)
    entities = [
        {"field": "int8", "type": DataType.INT8, "values": [1 for i in range(nb)]},
        {"field": "int64", "type": DataType.INT64, "values": [2 for i in range(nb)]},
        {"field": "float", "type": DataType.FLOAT, "values": [3.0 for i in range(nb)]},
        {"field": "vector", "type": DataType.FLOAT_VECTOR, "values": vectors}
    ]
    return entities


def gen_binary_entities(nb):
    raw_vectors, vectors = gen_binary_vectors(nb, dimension)
    entities = [
        {"field": "int8", "type": DataType.INT8, "values": [1 for i in range(nb)]},
        {"field": "int64", "type": DataType.INT64, "values": [2 for i in range(nb)]},
        {"field": "float", "type": DataType.FLOAT, "values": [3.0 for i in range(nb)]},
        {"field": "binary_vector", "type": DataType.BINARY_VECTOR, "values": vectors}
    ]
    return raw_vectors, entities


def gen_entities_by_fields(fields, nb, dimension):
    entities = []
    for field in fields:
        if field["type"] in [DataType.INT8, DataType.INT16, 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, dimension)[1]
        elif field["type"] == DataType.FLOAT_VECTOR:
            field_value = gen_vectors(nb, dimension)
        field.update({"values": field_value})

        entities.append(field)
    return entities


def assert_equal_entity(a, b):
    pass


def gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params={"nprobe": 10}):
    query_vectors = entities[-1]["values"][:nq]
    query = {
        "bool": {
            "must": [
                {"vector": {field_name: {"topk": top_k, "query": query_vectors, "params": search_params}}}
            ]
        }
    }
    return query, query_vectors


def gen_query_vectors_rand_entities(field_name, entities, top_k, nq, search_params={"nprobe": 10}):
    dimension = len(entities[-1]["values"][0])
    query_vectors = gen_vectors(nq, dimension)
    query = {
        "bool": {
            "must": [
                {"vector": {field_name: {"topk": top_k, "query": query_vectors, "params": search_params}}}
            ]
        }
    }
    return query, query_vectors



def add_field(entities):
    nb = len(entities[0]["values"])
    field = {
        "field": gen_unique_str(), 
        "type": DataType.INT8, 
        "values": [1 for i in range(nb)]
    }
    entities.append(field)
    return entities


def add_vector_field(entities, is_normal=False):
    nb = len(entities[0]["values"])
    vectors = gen_vectors(nb, dimension, is_normal)
    field = {
        "field": 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):
    for item in entities:
        if item["field"] == old_name:
            item["field"] = new_name
    return entities


def update_field_type(entities, old_name, new_name):
    for item in entities:
        if item["field"] == old_name:
            item["type"] = new_name
    return entities


def update_field_value(entities, old_type, new_value):
    for item in entities:
        if item["type"] == old_type:
            for i in item["values"]:
                item["values"][i] = new_value
    return entities


def add_vector_field(nb, dimension=dimension):
    field_name = gen_unique_str()
    field = {
        "field": field_name,
        "type": DataType.FLOAT_VECTOR,
        "values": gen_vectors(nb, dimension)
    }
    return field_name
        

def gen_segment_sizes():
    sizes = [
            1,
            2,
            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",
            "BB。A",
            "c|c",
            " siede ",
            "(mn)",
            "pip+",
            "=c",
            "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():
    top_ks = [
            # 1.0,
            None,
            "stringg",
            [1,2,3],
            (1,2),
            {"a": 1},
            " ",
            "",
            "String",
            "12-s",
            "BB。A",
            " siede ",
            "(mn)",
            "pip+",
            "=c",
            "中文",
            "a".join("a" for i in range(256))
    ]
    return top_ks


def gen_invalid_params():
    params = [
            9999999999,
            -1,
            # None,
            [1,2,3],
            (1,2),
            {"a": 1},
            " ",
            "",
            "String",
            "12-s",
            "BB。A",
            " siede ",
            "(mn)",
            "pip+",
            "=c",
            "中文"
    ]
    return params


def gen_invalid_vectors():
    invalid_vectors = [
            "1*2",
            [],
            [1],
            [1,2],
            [" "],
            ['a'],
            [None],
            None,
            (1,2),
            {"a": 1},
            " ",
            "",
            "String",
            "12-s",
            "BB。A",
            " siede ",
            "(mn)",
            "pip+",
            "=c",
            "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 == "HNSW":
            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_params.append(index_param)
    for efConstruction in gen_invalid_params():
        index_param = {"index_type": "HNSW", "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": "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 == "HNSW":
            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]}
        dic.update(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(default_index_params[i])
            index_params.append(dic)
    return index_params


def get_search_param(index_type):
    if index_type in ivf() or index_type in binary_support():
        return {"nprobe": 32}
    elif index_type == "HNSW":
        return {"ef": 64}
    elif index_type == "NSG":
        return {"search_length": 100}
    elif index_type == "ANNOY":
        return {"search_k": 100}
    else:
        logging.getLogger().info("Invalid index_type.")


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)

    namespace = "milvus"
    # 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')
    # print(status_res)
    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
        time.sleep(5)
        # check if restart successfully
        pods = v1.list_namespaced_pod(namespace)
        for i in pods.items:
            pod_name_tmp = i.metadata.name
            if pod_name_tmp.find(helm_release_name) != -1:
                logging.debug(pod_name_tmp)
                start_time = time.time()
                while time.time() - start_time > timeout:
                    status_res = v1.read_namespaced_pod_status(pod_name_tmp, namespace, pretty='true')
                    if status_res.status.phase == "Running":
                        break
                    time.sleep(1)
                if time.time() - start_time > timeout:
                    logging.error("Restart pod: %s timeout" % pod_name_tmp)
                    res = False
                    return res
    else:
        logging.error("Pod: %s not found" % helm_release_name)
        res = False
    return res


1			import os
2			import sys
3			import random
4			import pdb
5			import string
6			import struct
7			import logging
8			import time, datetime
9			import copy
10			import numpy as np
11			from sklearn import preprocessing
12			from milvus import Milvus, IndexType, MetricType, DataType
13
14			port = 19530
15			epsilon = 0.000001
16			default_flush_interval = 1
17			big_flush_interval = 1000
18			dimension = 128
19			segment_size = 10
20
21			# TODO:
22			all_index_types = [
23			"FLAT",
24			"IVF_FLAT",
25			"IVF_SQ8",
26			"IVF_SQ8_HYBRID",
27			"IVF_PQ",
28			"HNSW",
29			# "NSG",
30			"ANNOY",
31			"BIN_FLAT",
32			"BIN_IVF_FLAT"
33			]
34
35
36			default_index_params = [
37			{"nlist": 1024},
38			{"nlist": 1024},
39			{"nlist": 1024},
40			{"nlist": 1024},
41			{"nlist": 1024, "m": 16},
42			{"M": 48, "efConstruction": 500},
43			# {"search_length": 50, "out_degree": 40, "candidate_pool_size": 100, "knng": 50},
44			{"n_trees": 4},
45			{"nlist": 1024},
46			{"nlist": 1024}
47			]
48
49
50			def index_cpu_not_support():
51			return ["IVF_SQ8_HYBRID"]
52
53
54			def binary_support():
55			return ["BIN_FLAT", "BIN_IVF_FLAT"]
56
57
58			def delete_support():
59			return ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_SQ8_HYBRID", "IVF_PQ"]
60
61
62			def ivf():
63			return ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_SQ8_HYBRID", "IVF_PQ"]
64
65
66			def l2(x, y):
67			return np.linalg.norm(np.array(x) - np.array(y))
68
69
70			def ip(x, y):
71			return np.inner(np.array(x), np.array(y))
72
73
74			def jaccard(x, y):
75			x = np.asarray(x, np.bool)
76			y = np.asarray(y, np.bool)
77			return 1 - np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum())
78
79
80			def hamming(x, y):
81			x = np.asarray(x, np.bool)
82			y = np.asarray(y, np.bool)
83			return np.bitwise_xor(x, y).sum()
84
85
86			def tanimoto(x, y):
87			x = np.asarray(x, np.bool)
88			y = np.asarray(y, np.bool)
89			return -np.log2(np.double(np.bitwise_and(x, y).sum()) / np.double(np.bitwise_or(x, y).sum()))
90
91
92			def substructure(x, y):
93			x = np.asarray(x, np.bool)
94			y = np.asarray(y, np.bool)
95			return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(y)
96
97
98			def superstructure(x, y):
99			x = np.asarray(x, np.bool)
100			y = np.asarray(y, np.bool)
101			return 1 - np.double(np.bitwise_and(x, y).sum()) / np.count_nonzero(x)
102
103
104			def get_milvus(host, port, uri=None, handler=None, **kwargs):
105			if handler is None:
106			handler = "GRPC"
107			try_connect = kwargs.get("try_connect", True)
108			if uri is not None:
109			milvus = Milvus(uri=uri, handler=handler, try_connect=try_connect)
110			else:
111			milvus = Milvus(host=host, port=port, handler=handler, try_connect=try_connect)
112			return milvus
113
114
115			def disable_flush(connect):
116			connect.set_config("storage", "auto_flush_interval", big_flush_interval)
117
118
119			def enable_flush(connect):
120			# reset auto_flush_interval=1
121			connect.set_config("storage", "auto_flush_interval", default_flush_interval)
122			config_value = connect.get_config("storage", "auto_flush_interval")
123			assert config_value == str(default_flush_interval)
124
125
126			def gen_inaccuracy(num):
127			return num / 255.0
128
129
130			def gen_vectors(num, dim, is_normal=False):
131			vectors = [[random.random() for _ in range(dim)] for _ in range(num)]
132			vectors = preprocessing.normalize(vectors, axis=1, norm='l2')
133			return vectors.tolist()
134
135
136			# def gen_vectors(num, dim, seed=np.random.RandomState(1234), is_normal=False):
137			# xb = seed.rand(num, dim).astype("float32")
138			# xb = preprocessing.normalize(xb, axis=1, norm='l2')
139			# return xb.tolist()
140
141
142			def gen_binary_vectors(num, dim):
143			raw_vectors = []
144			binary_vectors = []
145			for i in range(num):
146			raw_vector = [random.randint(0, 1) for i in range(dim)]
147			raw_vectors.append(raw_vector)
148			binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
149			return raw_vectors, binary_vectors
150
151
152			def gen_binary_sub_vectors(vectors, length):
153			raw_vectors = []
154			binary_vectors = []
155			dim = len(vectors[0])
156			for i in range(length):
157			raw_vector = [0 for i in range(dim)]
158			vector = vectors[i]
159			for index, j in enumerate(vector):
160			if j == 1:
161			raw_vector[index] = 1
162			raw_vectors.append(raw_vector)
163			binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
164			return raw_vectors, binary_vectors
165
166
167			def gen_binary_super_vectors(vectors, length):
168			raw_vectors = []
169			binary_vectors = []
170			dim = len(vectors[0])
171			for i in range(length):
172			cnt_1 = np.count_nonzero(vectors[i])
173			raw_vector = [1 for i in range(dim)]
174			raw_vectors.append(raw_vector)
175			binary_vectors.append(bytes(np.packbits(raw_vector, axis=-1).tolist()))
176			return raw_vectors, binary_vectors
177
178
179			def gen_int_attr(row_num):
180			return [random.randint(0, 255) for _ in range(row_num)]
181
182
183			def gen_float_attr(row_num):
184			return [random.uniform(0, 255) for _ in range(row_num)]
185
186
187			def gen_unique_str(str_value=None):
188			prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8))
189			return "test_" + prefix if str_value is None else str_value + "_" + prefix
190
191
192			def gen_single_filter_fields():
193			fields = []
194			for data_type in DataType:
195			if data_type in [DataType.INT8, DataType.INT16, DataType.INT32, DataType.INT64, DataType.FLOAT, DataType.DOUBLE]:
196			fields.append({"field": data_type.name, "type": data_type})
197			return fields
198
199
200			def gen_single_vector_fields():
201			fields = []
202			for metric_type in ['HAMMING', 'IP', 'JACCARD', 'L2', 'SUBSTRUCTURE', 'SUPERSTRUCTURE', 'TANIMOTO']:
203			for data_type in [DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR]:
204			if metric_type in ["L2", "IP"] and data_type == DataType.BINARY_VECTOR:
205			continue
206			if metric_type not in ["L2", "IP"] and data_type == DataType.FLOAT_VECTOR:
207			continue
208			field = {"field": data_type.name, "type": data_type, "params": {"metric_type": metric_type, "dimension": dimension}}
209			fields.append(field)
210			return fields
211
212
213			def gen_default_fields():
214			default_fields = {
215			"fields": [
216			{"field": "int8", "type": DataType.INT8},
217			{"field": "int64", "type": DataType.INT64},
218			{"field": "float", "type": DataType.FLOAT},
219			{"field": "vector", "type": DataType.FLOAT_VECTOR, "params": {"metric_type": "L2", "dimension": dimension}}
220			],
221			"segment_size": segment_size
222			}
223			return default_fields
224
225
226			def gen_entities(nb, is_normal=False):
227			vectors = gen_vectors(nb, dimension, is_normal)
228			entities = [
229			{"field": "int8", "type": DataType.INT8, "values": [1 for i in range(nb)]},
230			{"field": "int64", "type": DataType.INT64, "values": [2 for i in range(nb)]},
231			{"field": "float", "type": DataType.FLOAT, "values": [3.0 for i in range(nb)]},
232			{"field": "vector", "type": DataType.FLOAT_VECTOR, "values": vectors}
233			]
234			return entities
235
236
237			def gen_binary_entities(nb):
238			raw_vectors, vectors = gen_binary_vectors(nb, dimension)
239			entities = [
240			{"field": "int8", "type": DataType.INT8, "values": [1 for i in range(nb)]},
241			{"field": "int64", "type": DataType.INT64, "values": [2 for i in range(nb)]},
242			{"field": "float", "type": DataType.FLOAT, "values": [3.0 for i in range(nb)]},
243			{"field": "binary_vector", "type": DataType.BINARY_VECTOR, "values": vectors}
244			]
245			return raw_vectors, entities
246
247
248			def gen_entities_by_fields(fields, nb, dimension):
249			entities = []
250			for field in fields:
251			if field["type"] in [DataType.INT8, DataType.INT16, DataType.INT32, DataType.INT64]:
252			field_value = [1 for i in range(nb)]
253			elif field["type"] in [DataType.FLOAT, DataType.DOUBLE]:
254			field_value = [3.0 for i in range(nb)]
255			elif field["type"] == DataType.BINARY_VECTOR:
256			field_value = gen_binary_vectors(nb, dimension)[1]
257			elif field["type"] == DataType.FLOAT_VECTOR:
258			field_value = gen_vectors(nb, dimension)
259			field.update({"values": field_value})
			0 ignored issues – show introduced 2020-07-23 02:40 UTC by Report Bug Copy Issue Report The variable `field_value` does not seem to be defined for all execution paths. Loading history...
260			entities.append(field)
261			return entities
262
263
264			def assert_equal_entity(a, b):
265			pass
266
267
268			def gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params={"nprobe": 10}):
269			query_vectors = entities[-1]["values"][:nq]
270			query = {
271			"bool": {
272			"must": [
273			{"vector": {field_name: {"topk": top_k, "query": query_vectors, "params": search_params}}}
274			]
275			}
276			}
277			return query, query_vectors
278
279
280			def gen_query_vectors_rand_entities(field_name, entities, top_k, nq, search_params={"nprobe": 10}):
281			dimension = len(entities[-1]["values"][0])
282			query_vectors = gen_vectors(nq, dimension)
283			query = {
284			"bool": {
285			"must": [
286			{"vector": {field_name: {"topk": top_k, "query": query_vectors, "params": search_params}}}
287			]
288			}
289			}
290			return query, query_vectors
291
292
293
294			def add_field(entities):
295			nb = len(entities[0]["values"])
296			field = {
297			"field": gen_unique_str(),
298			"type": DataType.INT8,
299			"values": [1 for i in range(nb)]
300			}
301			entities.append(field)
302			return entities
303
304
305			def add_vector_field(entities, is_normal=False):
306			nb = len(entities[0]["values"])
307			vectors = gen_vectors(nb, dimension, is_normal)
308			field = {
309			"field": gen_unique_str(),
310			"type": DataType.FLOAT_VECTOR,
311			"values": vectors
312			}
313			entities.append(field)
314			return entities
315
316
317			def update_fields_metric_type(fields, metric_type):
318			tmp_fields = copy.deepcopy(fields)
319			if metric_type in ["L2", "IP"]:
320			tmp_fields["fields"][-1]["type"] = DataType.FLOAT_VECTOR
321			else:
322			tmp_fields["fields"][-1]["type"] = DataType.BINARY_VECTOR
323			tmp_fields["fields"][-1]["params"]["metric_type"] = metric_type
324			return tmp_fields
325
326
327			def remove_field(entities):
328			del entities[0]
329			return entities
330
331
332			def remove_vector_field(entities):
333			del entities[-1]
334			return entities
335
336
337			def update_field_name(entities, old_name, new_name):
338			for item in entities:
339			if item["field"] == old_name:
340			item["field"] = new_name
341			return entities
342
343
344			def update_field_type(entities, old_name, new_name):
345			for item in entities:
346			if item["field"] == old_name:
347			item["type"] = new_name
348			return entities
349
350
351			def update_field_value(entities, old_type, new_value):
352			for item in entities:
353			if item["type"] == old_type:
354			for i in item["values"]:
355			item["values"][i] = new_value
356			return entities
357
358
359			def add_vector_field(nb, dimension=dimension):
360			field_name = gen_unique_str()
361			field = {
362			"field": field_name,
363			"type": DataType.FLOAT_VECTOR,
364			"values": gen_vectors(nb, dimension)
365			}
366			return field_name
367
368
369			def gen_segment_sizes():
370			sizes = [
371			1,
372			2,
373			1024,
374			4096
375			]
376			return sizes
377
378
379			def gen_invalid_ips():
380			ips = [
381			# "255.0.0.0",
382			# "255.255.0.0",
383			# "255.255.255.0",
384			# "255.255.255.255",
385			"127.0.0",
386			# "123.0.0.2",
387			"12-s",
388			" ",
389			"12 s",
390			"BB。A",
391			" siede ",
392			"(mn)",
393			"中文",
394			"a".join("a" for _ in range(256))
395			]
396			return ips
397
398
399			def gen_invalid_uris():
400			ip = None
401			uris = [
402			" ",
403			"中文",
404			# invalid protocol
405			# "tc://%s:%s" % (ip, port),
406			# "tcp%s:%s" % (ip, port),
407
408			# # invalid port
409			# "tcp://%s:100000" % ip,
410			# "tcp://%s: " % ip,
411			# "tcp://%s:19540" % ip,
412			# "tcp://%s:-1" % ip,
413			# "tcp://%s:string" % ip,
414
415			# invalid ip
416			"tcp:// :19530",
417			# "tcp://123.0.0.1:%s" % port,
418			"tcp://127.0.0:19530",
419			# "tcp://255.0.0.0:%s" % port,
420			# "tcp://255.255.0.0:%s" % port,
421			# "tcp://255.255.255.0:%s" % port,
422			# "tcp://255.255.255.255:%s" % port,
423			"tcp://\n:19530",
424			]
425			return uris
426
427
428			def gen_invalid_strs():
429			strings = [
430			1,
431			[1],
432			None,
433			"12-s",
434			" ",
435			# "",
436			# None,
437			"12 s",
438			"BB。A",
439			"c\|c",
440			" siede ",
441			"(mn)",
442			"pip+",
443			"=c",
444			"中文",
445			"a".join("a" for i in range(256))
446			]
447			return strings
448
449
450			def gen_invalid_field_types():
451			field_types = [
452			# 1,
453			"=c",
454			# 0,
455			None,
456			"",
457			"a".join("a" for i in range(256))
458			]
459			return field_types
460
461
462			def gen_invalid_metric_types():
463			metric_types = [
464			1,
465			"=c",
466			0,
467			None,
468			"",
469			"a".join("a" for i in range(256))
470			]
471			return metric_types
472
473
474			# TODO:
475			def gen_invalid_ints():
476			top_ks = [
477			# 1.0,
478			None,
479			"stringg",
480			[1,2,3],
481			(1,2),
482			{"a": 1},
483			" ",
484			"",
485			"String",
486			"12-s",
487			"BB。A",
488			" siede ",
489			"(mn)",
490			"pip+",
491			"=c",
492			"中文",
493			"a".join("a" for i in range(256))
494			]
495			return top_ks
496
497
498			def gen_invalid_params():
499			params = [
500			9999999999,
501			-1,
502			# None,
503			[1,2,3],
504			(1,2),
505			{"a": 1},
506			" ",
507			"",
508			"String",
509			"12-s",
510			"BB。A",
511			" siede ",
512			"(mn)",
513			"pip+",
514			"=c",
515			"中文"
516			]
517			return params
518
519
520			def gen_invalid_vectors():
521			invalid_vectors = [
522			"1*2",
523			[],
524			[1],
525			[1,2],
526			[" "],
527			['a'],
528			[None],
529			None,
530			(1,2),
531			{"a": 1},
532			" ",
533			"",
534			"String",
535			"12-s",
536			"BB。A",
537			" siede ",
538			"(mn)",
539			"pip+",
540			"=c",
541			"中文",
542			"a".join("a" for i in range(256))
543			]
544			return invalid_vectors
545
546
547			def gen_invaild_search_params():
548			invalid_search_key = 100
549			search_params = []
550			for index_type in all_index_types:
551			if index_type == "FLAT":
552			continue
553			search_params.append({"index_type": index_type, "search_params": {"invalid_key": invalid_search_key}})
554			if index_type in delete_support():
555			for nprobe in gen_invalid_params():
556			ivf_search_params = {"index_type": index_type, "search_params": {"nprobe": nprobe}}
557			search_params.append(ivf_search_params)
558			elif index_type == "HNSW":
559			for ef in gen_invalid_params():
560			hnsw_search_param = {"index_type": index_type, "search_params": {"ef": ef}}
561			search_params.append(hnsw_search_param)
562			elif index_type == "NSG":
563			for search_length in gen_invalid_params():
564			nsg_search_param = {"index_type": index_type, "search_params": {"search_length": search_length}}
565			search_params.append(nsg_search_param)
566			search_params.append({"index_type": index_type, "search_params": {"invalid_key": 100}})
567			elif index_type == "ANNOY":
568			for search_k in gen_invalid_params():
569			if isinstance(search_k, int):
570			continue
571			annoy_search_param = {"index_type": index_type, "search_params": {"search_k": search_k}}
572			search_params.append(annoy_search_param)
573			return search_params
574
575
576			def gen_invalid_index():
577			index_params = []
578			for index_type in gen_invalid_strs():
579			index_param = {"index_type": index_type, "params": {"nlist": 1024}}
580			index_params.append(index_param)
581			for nlist in gen_invalid_params():
582			index_param = {"index_type": "IVF_FLAT", "params": {"nlist": nlist}}
583			index_params.append(index_param)
584			for M in gen_invalid_params():
585			index_param = {"index_type": "HNSW", "params": {"M": M, "efConstruction": 100}}
586			index_params.append(index_param)
587			for efConstruction in gen_invalid_params():
588			index_param = {"index_type": "HNSW", "params": {"M": 16, "efConstruction": efConstruction}}
589			index_params.append(index_param)
590			for search_length in gen_invalid_params():
591			index_param = {"index_type": "NSG",
592			"params": {"search_length": search_length, "out_degree": 40, "candidate_pool_size": 50,
593			"knng": 100}}
594			index_params.append(index_param)
595			for out_degree in gen_invalid_params():
596			index_param = {"index_type": "NSG",
597			"params": {"search_length": 100, "out_degree": out_degree, "candidate_pool_size": 50,
598			"knng": 100}}
599			index_params.append(index_param)
600			for candidate_pool_size in gen_invalid_params():
601			index_param = {"index_type": "NSG", "params": {"search_length": 100, "out_degree": 40,
602			"candidate_pool_size": candidate_pool_size,
603			"knng": 100}}
604			index_params.append(index_param)
605			index_params.append({"index_type": "IVF_FLAT", "params": {"invalid_key": 1024}})
606			index_params.append({"index_type": "HNSW", "params": {"invalid_key": 16, "efConstruction": 100}})
607			index_params.append({"index_type": "NSG",
608			"params": {"invalid_key": 100, "out_degree": 40, "candidate_pool_size": 300,
609			"knng": 100}})
610			for invalid_n_trees in gen_invalid_params():
611			index_params.append({"index_type": "ANNOY", "params": {"n_trees": invalid_n_trees}})
612
613			return index_params
614
615
616			def gen_index():
617			nlists = [1, 1024, 16384]
618			pq_ms = [128, 64, 32, 16, 8, 4]
619			Ms = [5, 24, 48]
620			efConstructions = [100, 300, 500]
621			search_lengths = [10, 100, 300]
622			out_degrees = [5, 40, 300]
623			candidate_pool_sizes = [50, 100, 300]
624			knngs = [5, 100, 300]
625
626			index_params = []
627			for index_type in all_index_types:
628			if index_type in ["FLAT", "BIN_FLAT", "BIN_IVF_FLAT"]:
629			index_params.append({"index_type": index_type, "index_param": {"nlist": 1024}})
630			elif index_type in ["IVF_FLAT", "IVF_SQ8", "IVF_SQ8_HYBRID"]:
631			ivf_params = [{"index_type": index_type, "index_param": {"nlist": nlist}} \
632			for nlist in nlists]
633			index_params.extend(ivf_params)
634			elif index_type == "IVF_PQ":
635			IVFPQ_params = [{"index_type": index_type, "index_param": {"nlist": nlist, "m": m}} \
636			for nlist in nlists \
637			for m in pq_ms]
638			index_params.extend(IVFPQ_params)
639			elif index_type == "HNSW":
640			hnsw_params = [{"index_type": index_type, "index_param": {"M": M, "efConstruction": efConstruction}} \
641			for M in Ms \
642			for efConstruction in efConstructions]
643			index_params.extend(hnsw_params)
644			elif index_type == "NSG":
645			nsg_params = [{"index_type": index_type,
646			"index_param": {"search_length": search_length, "out_degree": out_degree,
647			"candidate_pool_size": candidate_pool_size, "knng": knng}} \
648			for search_length in search_lengths \
649			for out_degree in out_degrees \
650			for candidate_pool_size in candidate_pool_sizes \
651			for knng in knngs]
652			index_params.extend(nsg_params)
653
654			return index_params
655
656
657			def gen_simple_index():
658			index_params = []
659			for i in range(len(all_index_types)):
660			if all_index_types[i] in binary_support():
661			continue
662			dic = {"index_type": all_index_types[i]}
663			dic.update(default_index_params[i])
664			index_params.append(dic)
665			return index_params
666
667
668			def gen_binary_index():
669			index_params = []
670			for i in range(len(all_index_types)):
671			if all_index_types[i] in binary_support():
672			dic = {"index_type": all_index_types[i]}
673			dic.update(default_index_params[i])
674			index_params.append(dic)
675			return index_params
676
677
678			def get_search_param(index_type):
679			if index_type in ivf() or index_type in binary_support():
680			return {"nprobe": 32}
681			elif index_type == "HNSW":
682			return {"ef": 64}
683			elif index_type == "NSG":
684			return {"search_length": 100}
685			elif index_type == "ANNOY":
686			return {"search_k": 100}
687			else:
688			logging.getLogger().info("Invalid index_type.")
689
690
691			def assert_equal_vector(v1, v2):
692			if len(v1) != len(v2):
693			assert False
694			for i in range(len(v1)):
695			assert abs(v1[i] - v2[i]) < epsilon
696
697
698			def restart_server(helm_release_name):
699			res = True
700			timeout = 120
701			from kubernetes import client, config
702			client.rest.logger.setLevel(logging.WARNING)
703
704			namespace = "milvus"
705			# service_name = "%s.%s.svc.cluster.local" % (helm_release_name, namespace)
706			config.load_kube_config()
707			v1 = client.CoreV1Api()
708			pod_name = None
709			# config_map_names = v1.list_namespaced_config_map(namespace, pretty='true')
710			# body = {"replicas": 0}
711			pods = v1.list_namespaced_pod(namespace)
712			for i in pods.items:
713			if i.metadata.name.find(helm_release_name) != -1 and i.metadata.name.find("mysql") == -1:
714			pod_name = i.metadata.name
715			break
716			# v1.patch_namespaced_config_map(config_map_name, namespace, body, pretty='true')
717			# status_res = v1.read_namespaced_service_status(helm_release_name, namespace, pretty='true')
718			# print(status_res)
719			if pod_name is not None:
720			try:
721			v1.delete_namespaced_pod(pod_name, namespace)
722			except Exception as e:
723			logging.error(str(e))
724			logging.error("Exception when calling CoreV1Api->delete_namespaced_pod")
725			res = False
726			return res
727			time.sleep(5)
728			# check if restart successfully
729			pods = v1.list_namespaced_pod(namespace)
730			for i in pods.items:
731			pod_name_tmp = i.metadata.name
732			if pod_name_tmp.find(helm_release_name) != -1:
733			logging.debug(pod_name_tmp)
734			start_time = time.time()
735			while time.time() - start_time > timeout:
736			status_res = v1.read_namespaced_pod_status(pod_name_tmp, namespace, pretty='true')
737			if status_res.status.phase == "Running":
738			break
739			time.sleep(1)
740			if time.time() - start_time > timeout:
741			logging.error("Restart pod: %s timeout" % pod_name_tmp)
742			res = False
743			return res
744			else:
745			logging.error("Pod: %s not found" % helm_release_name)
746			res = False
747			return res
748

milvus-io / milvus

Push — master ( fd4969...54df52 )

utils.gen_entities() A

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