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Version: User Guides (Cloud)

Insert, Upsert & Delete

This guide walks you through the data manipulation operations within a collection, including insertion, upsertion, and deletion.

Before you start​

  • You have installed the SDK of your choice. To install an SDK, refer to Install SDKs.

  • You have created a collection. To create a collection, refer to Manage Collections (SDKs).

  • To insert a large volume of data, you are advised to use Data Import.

Overview​

An entity, within the context of Zilliz Cloud collections, is a singular, identifiable instance within a collection. It represents a distinct member of a particular class, be it a book in a library, a gene in a genome, or any other identifiable entity.

Entities within a collection share a common set of attributes, termed schema, outlining the structure that each entity must adhere to, including field names, data types, and any other constraints.

Successful insertion of entities into a collection requires that the provided data should contain all the schema-defined fields of the target collection. Additionally, you can also include non-schema-defined fields only if you have enabled the dynamic field. For details, refer to Enable Dynamic Field.

To learn more about the concepts of the schema and entities, refer to Schema Explained and Cluster, Collection & Entities.

Preparations​

The code snippet below repurposes the existing code to establish a connection to a Zilliz Cloud cluster and quickly set up a collection.

from pymilvus import MilvusClient

CLUSTER_ENDPOINT = "YOUR_CLUSTER_ENDPOINT"
TOKEN = "YOUR_CLUSTER_TOKEN"

# 1. Set up a Milvus client
client = MilvusClient(
    uri=CLUSTER_ENDPOINT,
    token=TOKEN 
)

# 2. Create a collection
client.create_collection(
    collection_name="quick_setup",
    dimension=5, # The dimension value should be an integer greater than 1
    metric_type="IP"
)
📘Notes

The collection generated in the above code contains only two fields: id (as the primary key) and vector (as the vector field), with auto_id and enable_dynamic_field settings enabled by default. When inserting data,

  • You do not need to include id in the data to be inserted, because the primary field automatically increments as data is inserted.

  • Non-schema-defined fields will be saved as key-value pairs in a reserved JSON field named $meta.

Insert entities​

To insert entities, you need to organize the data into a list of dictionaries, where each dictionary represents an entity. Each dictionary contains the keys corresponding to both pre-defined and dynamic fields in the target collection.

# 3. Insert some data
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)

# Output
#
# {
#     "insert_count": 10,
#     "ids": [
#         0,
#         1,
#         2,
#         3,
#         4,
#         5,
#         6,
#         7,
#         8,
#         9
#     ]
# }

Insert into partitions​

To insert data into a specific partition, you can specify the name of the partition in the insert request as follows:

# 4. Insert some more data into a specific partition
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)

# Output
#
# {
#     "insert_count": 10,
#     "ids": [
#         10,
#         11,
#         12,
#         13,
#         14,
#         15,
#         16,
#         17,
#         18,
#         19
#     ]
# }

The output is a dictionary containing the statistics on the affected entities. For details on partition operations, refer to Manage Partitions.

Upsert entities​

Upserting data is a combination of update and insert operations. In Zilliz Cloud, an upsert operation performs a data-level action to either insert or update an entity based on whether its primary key already exists in a collection. Specifically:

  • If the primary key of the entity already exists in the collection, the existing entity will be overwritten.

  • If the primary key does not exist in the collection, a new entity will be inserted.

📘Notes

  • Upsert operations will not update the primary keys.

  • Upsert operations does not support collections with autoID enabled.

# 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​

To upsert data into a specific partition, you can specify the name of the partition in the insert request as follows:

# 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
# }

The output is a dictionary containing the statistics on the affected entities. For details on partition operations, refer to Manage Partitions.

Delete entities​

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

  • Delete entities by filter.

    📘Notes

    When using filter expressions to delete entities, ensure the collection has been loaded. Otherwise, Zilliz Cloud will return an error.

    # 7. Delete entities
    res = client.delete(
        collection_name="quick_setup",
        filter="id in [4,5,6]"
    )

    print(res)

    # Output
    #
    # {
    #     "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)

    # Output
    #
    # {
    #     "delete_count": 2
    # }

    For details on how to use filter expressions, refer to Get & Scalar Query.

  • Delete entities by partition name.

    If you want to delete entities from a specific partition, you can specify the partition name with the partition_name parameter in the delete() method. The following example deletes entities from partitionA that have a color starting with blue.

    res = client.delete(
        collection_name='quick_setup',
        partition_name='partitionA',
        filter='color like "blue%"'
    )

    print("Entities deleted from partitionA: ", res['delete_count'])

    # Output:
    # Entities deleted from partitionA:  3