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Insert Data

This topic describes how to insert data in Milvus via client.

You can also migrate data to Milvus with MilvusDM, an open-source tool designed specifically for importing and exporting data with Milvus.

Milvus 2.1 supports VARCHAR data type on scalar field. When building indexes for VARCHAR-type scalar fields, the default index type is dictionary tree.

The following example inserts 2,000 rows of randomly generated data as the example data (Milvus CLI example uses a pre-built, remote CSV file containing similar data). Real applications will likely use much higher dimensional vectors than the example. You can prepare your own data to replace the example.

Prepare data

First, prepare the data to insert. Data type of the data to insert must match the schema of the collection, otherwise Milvus will raise exception.

import random
data = [
  [i for i in range(2000)],
  [str(i) for i in range(2000)],
  [i for i in range(10000, 12000)],
  [[random.random() for _ in range(2)] for _ in range(2000)],
]
const data = Array.from({ length: 2000 }, (v,k) => ({
  "book_id": k,
  "word_count": k+10000,
  "book_intro": Array.from({ length: 2 }, () => Math.random()),
}));
bookIDs := make([]int64, 0, 2000)
wordCounts := make([]int64, 0, 2000)
bookIntros := make([][]float32, 0, 2000)
for i := 0; i < 2000; i++ {
	bookIDs = append(bookIDs, int64(i))
	wordCounts = append(wordCounts, int64(i+10000))
	v := make([]float32, 0, 2)
	for j := 0; j < 2; j++ {
		v = append(v, rand.Float32())
	}
	bookIntros = append(bookIntros, v)
}
idColumn := entity.NewColumnInt64("book_id", bookIDs)
wordColumn := entity.NewColumnInt64("word_count", wordCounts)
introColumn := entity.NewColumnFloatVector("book_intro", 2, bookIntros)
Random ran = new Random();
List<Long> book_id_array = new ArrayList<>();
List<Long> word_count_array = new ArrayList<>();
List<List<Float>> book_intro_array = new ArrayList<>();
for (long i = 0L; i < 2000; ++i) {
	book_id_array.add(i);
	word_count_array.add(i + 10000);
	List<Float> vector = new ArrayList<>();
	for (int k = 0; k < 2; ++k) {
		vector.add(ran.nextFloat());
	}
	book_intro_array.add(vector);
}
# Prepare your data in a CSV file. Milvus CLI only supports importing data from local or remote files.
# See the following step.

Insert data to Milvus

Insert the data to the collection.

By specifying partition_name, you can optionally decide to which partition to insert the data.

from pymilvus import Collection
collection = Collection("book")      # Get an existing collection.
mr = collection.insert(data)
const mr = await milvusClient.dataManager.insert({
  collection_name: "book",
  fields_data: data,
});
_, err = milvusClient.Insert(
	context.Background(), // ctx
	"book",               // CollectionName
	"",                   // partitionName
	idColumn,             // columnarData
	wordColumn,           // columnarData
	introColumn,          // columnarData
)
if err != nil {
	log.Fatal("failed to insert data:", err.Error())
}
List<InsertParam.Field> fields = new ArrayList<>();
fields.add(new InsertParam.Field("book_id", DataType.Int64, book_id_array));
fields.add(new InsertParam.Field("word_count", DataType.Int64, word_count_array));
fields.add(new InsertParam.Field("book_intro", DataType.FloatVector, book_intro_array));

InsertParam insertParam = InsertParam.newBuilder()
  .withCollectionName("book")
  .withPartitionName("novel")
  .withFields(fields)
  .build();
milvusClient.insert(insertParam);
import -c book 'https://raw.githubusercontent.com/milvus-io/milvus_cli/main/examples/user_guide/search.csv'
curl -X 'POST' \
  'http://localhost:9091/api/v1/entities' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "collection_name": "book",
  "fields_data": [
    {
      "field_name": "book_id",
      "type": 5,
      "field": [
        1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100
      ]
    },
    {
      "field_name": "word_count",
      "type": 5,
      "field": [
        1000,2000,3000,4000,5000,6000,7000,8000,9000,10000,11000,12000,13000,14000,15000,16000,17000,18000,19000,20000,21000,22000,23000,24000,25000,26000,27000,28000,29000,30000,31000,32000,33000,34000,35000,36000,37000,38000,39000,40000,41000,42000,43000,44000,45000,46000,47000,48000,49000,50000,51000,52000,53000,54000,55000,56000,57000,58000,59000,60000,61000,62000,63000,64000,65000,66000,67000,68000,69000,70000,71000,72000,73000,74000,75000,76000,77000,78000,79000,80000,81000,82000,83000,84000,85000,86000,87000,88000,89000,90000,91000,92000,93000,94000,95000,96000,97000,98000,99000,100000
      ]
    },
    {
      "field_name": "book_intro",
      "type": 101,
      "field": [
        [1,1],[2,1],[3,1],[4,1],[5,1],[6,1],[7,1],[8,1],[9,1],[10,1],[11,1],[12,1],[13,1],[14,1],[15,1],[16,1],[17,1],[18,1],[19,1],[20,1],[21,1],[22,1],[23,1],[24,1],[25,1],[26,1],[27,1],[28,1],[29,1],[30,1],[31,1],[32,1],[33,1],[34,1],[35,1],[36,1],[37,1],[38,1],[39,1],[40,1],[41,1],[42,1],[43,1],[44,1],[45,1],[46,1],[47,1],[48,1],[49,1],[50,1],[51,1],[52,1],[53,1],[54,1],[55,1],[56,1],[57,1],[58,1],[59,1],[60,1],[61,1],[62,1],[63,1],[64,1],[65,1],[66,1],[67,1],[68,1],[69,1],[70,1],[71,1],[72,1],[73,1],[74,1],[75,1],[76,1],[77,1],[78,1],[79,1],[80,1],[81,1],[82,1],[83,1],[84,1],[85,1],[86,1],[87,1],[88,1],[89,1],[90,1],[91,1],[92,1],[93,1],[94,1],[95,1],[96,1],[97,1],[98,1],[99,1],[100,1]
      ]
    }
  ],
  "num_rows": 100
}'
Output:
{
  "status":{},
  "IDs":{
    "IdField":{
      "IntId":{"data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100]
      }
    }
  },
  "succ_index":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99],
  "insert_cnt":100,
  "timestamp":434262073374408706
}
Parameter Description
data Data to insert into Milvus.
partition_name (optional) Name of the partition to insert data into.
Parameter Description
collection_name Name of the collection to insert data into.
partition_name (optional) Name of the partition to insert data into.
fields_data Data to insert into Milvus.
Parameter Description
ctx Context to control API invocation process.
CollectionName Name of the collection to insert data into.
partitionName Name of the partition to insert data into. Data will be inserted in the default partition if left blank.
columnarData Data to insert into each field.
Parameter Description
fieldName Name of the field to insert data into.
DataType Data type of the field to insert data into.
data Data to insert into each field.
CollectionName Name of the collection to insert data into.
PartitionName (optional) Name of the partition to insert data into.
Option Description
-c Name of the collection to insert data into.
-p (Optional) Name of the partition to insert data into.
Parameter Description Option
collection_name Name of the collection to insert data into. N/A
fields_data Data to insert into Milvus. N/A
field_name Name of the field to insert data into. N/A
type Data type of the field to insert data into. Enums:
1: "Bool",
2: "Int8",
3: "Int16",
4: "Int32",
5: "Int64",
10: "Float",
11: "Double",
20: "String",
21: "VarChar",
100: "BinaryVector",
101: "FloatVector",
field The data of one column to be inserted. N/A
num_rows Number of rows to be inserted. The number should be the same as the length of each field array. N/A

Limits

Feature Maximum limit
Dimensions of a vector 32,768

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