docs/en/connectors/sink/Milvus.md
import ChangeLog from '../changelog/connector-milvus.md';
Milvus sink connector
This Milvus sink connector write data to Milvus or Zilliz Cloud, it has the following features:
| Milvus Data Type | SeaTunnel Data Type |
|---|---|
| INT8 | TINYINT |
| INT16 | SMALLINT |
| INT32 | INT |
| INT64 | BIGINT |
| FLOAT | FLOAT |
| DOUBLE | DOUBLE |
| BOOL | BOOLEAN |
| JSON | STRING |
| ARRAY | ARRAY |
| VARCHAR | STRING |
| FLOAT_VECTOR | FLOAT_VECTOR |
| BINARY_VECTOR | BINARY_VECTOR |
| FLOAT16_VECTOR | FLOAT16_VECTOR |
| BFLOAT16_VECTOR | BFLOAT16_VECTOR |
| SPARSE_FLOAT_VECTOR | SPARSE_FLOAT_VECTOR |
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
| url | String | Yes | - | The URL to connect to Milvus or Zilliz Cloud. |
| token | String | Yes | - | User:password |
| database | String | No | - | Write data to which database, default is source database. |
| schema_save_mode | enum | No | CREATE_SCHEMA_WHEN_NOT_EXIST | Auto create table when table not exist. |
| enable_auto_id | boolean | No | false | Primary key column enable autoId. |
| enable_upsert | boolean | No | false | Upsert data not insert. |
| enable_dynamic_field | boolean | No | true | Enable create table with dynamic field. |
| batch_size | int | No | 1000 | Write batch size. When the number of buffered records reaches batch_size or the time reaches checkpoint.interval, it will trigger a write flush |
| partition_key | String | No | Milvus partition key field | |
| create_index | boolean | No | false | Automatically create vector indexes for collection to improve query performance. |
| load_collection | boolean | No | false | Load collection into Milvus memory for immediate query availability. |
| collection_description | Map<String, String> | No | {} | Collection descriptions map where key is collection name and value is description. |
sink {
Milvus {
url = "http://127.0.0.1:19530"
token = "username:password"
batch_size = 1000
}
}
sink {
Milvus {
url = "http://127.0.0.1:19530"
token = "username:password"
batch_size = 1000
create_index = true
load_collection = true
collection_description = {
"user_vectors" = "User embedding vectors for recommendation"
"product_vectors" = "Product feature vectors for search"
}
}
}