插入、更新和删除

本指南将引导你完成集合中的数据操作，包括插入、更新和删除等操作。

开始之前

你已经安装了你选择的 SDK。安装 SDK，请参阅安装 SDK。
你已经创建了一个集合。创建集合，请参阅管理集合。
若要插入大量数据，请使用数据导入 (opens in a new tab)。

概述

在 Milvus 集合的上下文中，实体是集合中的一个单独的、可识别的实例。它代表着一个特定类别的独特成员，可以是图书馆中的一本书，基因组中的一个基因，或者任何其他可识别的实体。

集合中的实体共享一组通用的属性，称为 schema，用于描述每个实体必须遵循的结构，包括字段名称、数据类型和任何其他约束。

成功将实体插入到集合中需要提供的数据应包含目标集合的所有 schema 定义的字段。另外，如果你启用了动态字段，还可以包含非 schema 定义的字段。有关详细信息，请参阅启用动态字段。

本页上的代码片段使用新的 MilvusClient (Python)与 Milvus 进行交互。其他语言的新 MilvusClient SDK 将在未来的更新中发布。

准备工作

下面的代码片段重用了现有代码，用于与 Milvus 集群建立连接并快速设置一个集合。

from pymilvus import MilvusClient
 
# 1. 设置一个 Milvus 客户端
client = MilvusClient(
    uri="http://localhost:19530"
)
 
# 2. 创建一个集合
client.create_collection(
    collection_name="quick_setup",
    dimension=5,
    metric_type="IP"
)

插入实体

要插入实体，你需要将数据组织为一个字典列表，其中每个字典表示一个实体。每个字典都包含与目标集合中的预定义和动态字段相对应的键。

# 3. 插入一些数据
data=[
    {"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682"},
    {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025"},
    {"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781"},
    {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298"},
    {"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794"},
    {"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222"},
    {"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392"},
    {"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510"},
    {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381"},
    {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976"}
]
 
res = client.insert(
    collection_name="quick_setup",
    data=data
)
 
print(res)
 
# 输出
#
# {
#     "insert_count": 10,
#     "ids": [
#         0,
#         1,
#         2,
#         3,
#         4,
#         5,
#         6,
#         7,
#         8,
#         9
#     ]
# }

插入分区

要将数据插入到特定分区中，你可以在插入请求中指定分区的名称，示例如下：

# 4. 向特定分区插入更多数据
data=[
    {"id": 10, "vector": [-0.5570353903748935, -0.8997887893201304, -0.7123782431855732, -0.6298990746450119, 0.6699215060604258], "color": "red_1202"},
    {"id": 11, "vector": [0.6319019033373907, 0.6821488267878275, 0.8552303045704168, 0.36929791364943054, -0.14152860714878068], "color": "blue_4150"},
    {"id": 12, "vector": [0.9483947484855766, -0.32294203351925344, 0.9759290319978025, 0.8262982148666174, -0.8351194181285713], "color": "orange_4590"},
    {"id": 13, "vector": [-0.5449109892498731, 0.043511240563786524, -0.25105249484790804, -0.012030655265886425, -0.0010987671273892108], "color": "pink_9619"},
    {"id": 14, "vector": [0.6603339372951424, -0.10866551787442225, -0.9435597754324891, 0.8230244263466688, -0.7986720938400362], "color": "orange_4863"},
    {"id": 15, "vector": [-0.8825129181091456, -0.9204557711667729, -0.935350065513425, 0.5484069690287079, 0.24448151140671204], "color": "orange_7984"},
    {"id": 16, "vector": [0.6285586391568163, 0.5389064528263487, -0.3163366239905099, 0.22036279378888013, 0.15077052220816167], "color": "blue_9010"},
    {"id": 17, "vector": [-0.20151825016059233, -0.905239387635804, 0.6749305353372479, -0.7324272081377843, -0.33007998971889263], "color": "blue_4521"},
    {"id": 18, "vector": [0.2432286610792349, 0.01785636564206139, -0.651356982731391, -0.35848148851027895, -0.7387383128324057], "color": "orange_2529"},
    {"id": 19, "vector": [0.055512329053363674, 0.7100266349039421, 0.4956956543575197, 0.24541352586717702, 0.4209030729923515], "color": "red_9437"}
]
 
client.create_partition(
    collection_name="quick_setup",
    partition_name="partitionA"
)
 
res = client.insert(
    collection_name="quick_setup",
    data=data,
    partition_name="partitionA"
)
 
print(res)
 
# 输出
#
# {
#     "insert_count": 10,
#     "ids": [
#         10,
#         11,
#         12,
#         13,
#         14,
#         15,
#         16,
#         17,
#         18,
#         19
#     ]
# }

输出是一个包含受影响实体统计信息的字典。有关分区操作的详细信息，请参阅管理分区。

更新实体

Upserting data（更新插入数据）是 update（更新）和 insert（插入）操作的组合。在 Milvus 中，upsert 操作根据实体的主键在集合中是否已存在来执行数据级操作，具体如下：

如果实体的主键已经存在于集合中，则会覆盖现有实体。
如果主键在集合中不存在，则会插入一个新实体。

# 5. Upsert some data
data=[
    {"id": 0, "vector": [-0.619954382375778, 0.4479436794798608, -0.17493894838751745, -0.4248030059917294, -0.8648452746018911], "color": "black_9898"},
    {"id": 1, "vector": [0.4762662251462588, -0.6942502138717026, -0.4490002642657902, -0.628696575798281, 0.9660395877041965], "color": "red_7319"},
    {"id": 2, "vector": [-0.8864122635045097, 0.9260170474445351, 0.801326976181461, 0.6383943392381306, 0.7563037341572827], "color": "white_6465"},
    {"id": 3, "vector": [0.14594326235891586, -0.3775407299900644, -0.3765479013078812, 0.20612075380355122, 0.4902678929632145], "color": "orange_7580"},
    {"id": 4, "vector": [0.4548498669607359, -0.887610217681605, 0.5655081329910452, 0.19220509387904117, 0.016513983433433577], "color": "red_3314"},
    {"id": 5, "vector": [0.11755001847051827, -0.7295149788999611, 0.2608115847524266, -0.1719167007897875, 0.7417611743754855], "color": "black_9955"},
    {"id": 6, "vector": [0.9363032158314308, 0.030699901477745373, 0.8365910312319647, 0.7823840208444011, 0.2625222076909237], "color": "yellow_2461"},
    {"id": 7, "vector": [0.0754823906014721, -0.6390658668265143, 0.5610517334334937, -0.8986261118798251, 0.9372056764266794], "color": "white_5015"},
    {"id": 8, "vector": [-0.3038434006935904, 0.1279149203380523, 0.503958664270957, -0.2622661156746988, 0.7407627307791929], "color": "purple_6414"},
    {"id": 9, "vector": [-0.7125086947677588, -0.8050968321012257, -0.32608864121785786, 0.3255654958645424, 0.26227968923834233], "color": "brown_7231"}
]
 
res = client.upsert(
    collection_name='quick_setup',
    data=data
)
 
print(res)
 
# Output
#
# {
#     "upsert_count": 10
# }

Upsert data in partitions（在分区中更新插入数据）

要将数据插入到特定分区中，你可以在插入请求中指定分区的名称，如下所示：

# 6. Upsert data in partitions
data=[
    {"id": 10, "vector": [0.06998888224297328, 0.8582816610326578, -0.9657938677934292, 0.6527905683627726, -0.8668460657158576], "color": "black_3651"},
    {"id": 11, "vector": [0.6060703043917468, -0.3765080534566074, -0.7710758854987239, 0.36993888322346136, 0.5507513364206531], "color": "grey_2049"},
    {"id": 12, "vector": [-0.9041813104515337, -0.9610546012461163, 0.20033003106083358, 0.11842506351635174, 0.8327356724591011], "color": "blue_6168"},
    {"id": 13, "vector": [0.3202914977909075, -0.7279137773695252, -0.04747830871620273, 0.8266053056909548, 0.8277957187455489], "color": "blue_1672"},
    {"id": 14, "vector": [0.2975811497890859, 0.2946936202691086, 0.5399463833894609, 0.8385334966677529, -0.4450543984655133], "color": "pink_1601"},
    {"id": 15, "vector": [-0.04697464305600074, -0.08509022265734134, 0.9067184632552001, -0.2281912685064822, -0.9747503428652762], "color": "yellow_9925"},
    {"id": 16, "vector": [-0.9363075919673911, -0.8153981031085669, 0.7943039120490902, -0.2093886809842529, 0.0771191335807897], "color": "orange_9872"},
    {"id": 17, "vector": [-0.050451522820639916, 0.18931572752321935, 0.7522886192190488, -0.9071793089474034, 0.6032647330692296], "color": "red_6450"},
    {"id": 18, "vector": [-0.9181544231141592, 0.6700755998126806, -0.014174674636136642, 0.6325780463623432, -0.49662222164032976], "color": "purple_7392"},
    {"id": 19, "vector": [0.11426945899602536, 0.6089190684002581, -0.5842735738352236, 0.057050610092692855, -0.035163433018196244], "color": "pink_4996"}
]
 
res = client.upsert(
    collection_name="quick_setup",
    data=data,
    partition_name="partitionA"
)
 
print(res)
 
# Output
#
# {
#     "upsert_count": 10
# }

输出是一个包含受影响实体统计信息的字典。有关分区操作的详细信息，请参考管理分区。

删除实体

If an entity is no longer needed, you can delete it from the collection. Milvus offers two ways for you to identify the entities to delete.

Delete entities by filter.

# 7. 删除实体
res = client.delete(
    collection_name="quick_setup",
    filter="id in [4,5,6]"
)
 
print(res)
 
# 输出
#
# {
#     "delete_count": 3
# }

Delete entities by IDs.

The following snippets demonstrate how to delete entities by IDs from a specific partition. It also works if you leave the partition name unspecified.

res = client.delete(
    collection_name="quick_setup",
    ids=[18, 19],
    partition_name="partitionA"
)
 
print(res)
 
# 输出
#
# {
#     "delete_count": 2
# }

For details on how to use filter expressions, refer to 获取和标量查询.

导入数据(Import_data)负载均衡(Load_balance)