Milvus Connector

Connect Flink SQL jobs running on Ververica to Milvus to persist vector and scalar data with upsert semantics based on a primary key.

Prerequisites

• A running Milvus 2.4+ deployment with a reachable network address (host and port).
• A database and collection must already exist in Milvus.
• The collection schema must have:
  • One primary key field of type String or Integer, with AUTO_ID disabled.
  • One or more vector fields (e.g., FloatVector) with a known dimension.
• Credentials (userName, password) if authentication is enabled on your Milvus instance.

---

Overview

The Milvus connector for Flink SQL provides a sink to write streaming or batch data into a Milvus collection. It is ideal for real-time AI and vector-search use cases.

• Upsert Semantics: The sink processes changelog data, performing an upsert for INSERT and UPDATE_AFTER records and a delete for DELETE records.
• Buffered Writes: Data is buffered and written in batches to optimize for throughput.
• Streaming or Batch: The connector supports both bounded (batch) and unbounded (streaming) write modes.

Syntax and Example

The following example shows how to define a Flink table that sinks to an existing Milvus collection and then insert data into it.

-- Define a sink into an existing Milvus collection
CREATE TABLE milvus_sink (
  id  BIGINT NOT NULL,
  name STRING,
  age  INT,
  vec  ARRAY<FLOAT>,               -- maps to a FloatVector field in Milvus
  PRIMARY KEY (id) NOT ENFORCED
) WITH (
  'connector'                  = 'milvus',
  'endpoint'                   = 'milvus-dev.my.company',  -- or IP
  'port'                       = '19530',
  'userName'                   = 'root',
  'password'                   = 'Milvus',
  'databaseName'               = 'default',
  'collectionName'             = 'people',
  -- tune buffering for throughput vs. latency
  'sink.buffer-flush.max-rows' = '200',
  'sink.buffer-flush.interval' = '3s'
);

-- Example bounded source (datagen) and insert
CREATE TEMPORARY TABLE gen (
  id BIGINT NOT NULL,
  name STRING,
  age INT,
  PRIMARY KEY (id) NOT ENFORCED
) WITH (
  'connector' = 'datagen',
  'number-of-rows' = '3',
  'fields.id.kind'  = 'sequence',
  'fields.id.start' = '1',
  'fields.id.end'   = '3'
);

INSERT INTO milvus_sink
SELECT id, name, age,
       ARRAY[CAST(0.1 AS FLOAT), CAST(0.2 AS FLOAT), CAST(0.3 AS FLOAT)] AS vec
FROM gen;

note

The vector dimension in your ARRAY<FLOAT> data must exactly match the dimension of the corresponding vector field in your Milvus collection's schema (3 in this example).

Feeding Deletes

To delete a record in Milvus, your source must be a changelog source (like upsert-kafka with a changelog-json format) that can emit DELETE records. The sink will then publish a DELETE event for the given primary key.

CREATE TEMPORARY TABLE tombstones_kafka (
  id  BIGINT NOT NULL,
  name STRING,
  age  INT,
  vec  ARRAY<FLOAT>,
  PRIMARY KEY (id) NOT ENFORCED
) WITH (
  'connector'     = 'upsert-kafka',
  'topic'         = 'people-changelog',
  'properties.bootstrap.servers' = 'kafka-broker:9092',
  'key.format'    = 'json',
  'value.format'  = 'changelog-json',
  'value.fields-include' = 'ALL'
);

INSERT INTO MilvusPeople
SELECT * FROM tombstones_kafka;

Partitioning

The connector supports writing to a specific Milvus partition.

Static Partitioning

To write all data from the sink into a single, fixed partition, specify its name using the partitionName option.

CREATE TABLE MilvusSink (...) WITH (
  'connector'     = 'milvus',
  'partitionName' = 'my_partition',
  ...
);

Partition Key Routing

If your Milvus collection is configured with a partition key, you can enable automatic routing. The connector will use the value from the corresponding field in your Flink data to route each record to the correct partition.

CREATE TABLE MilvusSink (...) WITH (
  'connector'            = 'milvus',
  'partitionKey.enabled' = 'true',
  ...
);

WITH options

Option	Required	Default	Description
connector	Yes	–	Must be 'milvus'.
endpoint	Yes	–	The hostname or IP address of the Milvus instance.
port	No	19530	The gRPC port for the Milvus instance.
userName	Yes	–	The username for Milvus authentication.
password	Yes	–	The password for Milvus authentication.
databaseName	Yes	–	The target database name in Milvus.
collectionName	Yes	–	The target collection name in Milvus.
partitionName	No	–	The name of a fixed partition to write to.
partitionKey.enabled	No	false	Set to true to use the collection’s defined partition key for routing.
sink.buffer-flush.max-rows	No	200	The maximum number of records to buffer before flushing. 0 disables this.
sink.buffer-flush.interval	No	3s	The maximum time between flushes. 0s disables this.
sink.max-retries	No	3	The number of times to retry on write errors.
sink.parallelism	No	planner	Manually overrides the parallelism of the sink operator.
sink.ignoreDelete	No	false	If true, the sink will ignore DELETE records from the changelog source.

note

All WITH option names use camelCase (e.g., databaseName, collectionName). Ensure you use these names exactly.

Operational Tips & Limits

• Sink-only: The Milvus connector currently only supports writing data (sink). It cannot be used as a source or for lookup joins.
• Deletes Require Changelogs: To delete records in Milvus, you must use a source that produces changelog data with DELETE operations.
• Indexing for Queries: To query data immediately after writing, you must first create a vector index on your collection and then load the collection into memory. This is a Milvus requirement.
• Network Accessibility: Ensure that your Ververica deployment's network configuration (e.g., VPC, Security Groups, Firewalls) allows outbound traffic to your Milvus instance's host and port. Additionally, configure your Milvus instance's firewall or security group to allow inbound traffic from your Ververica deployment.

Milvus Setup for Testing

This section shows how to get a Milvus instance running with Docker and how to perform basic operations using the Python client. This is useful for local development and for following the examples in a test environment.

Step 1: Set Up Milvus with Docker

This example uses an Ubuntu 22.04 environment.

1. Install Docker and Docker Compose

Connect to your host machine with SSH and run the following commands to install the necessary packages.

sudo apt-get update -y
sudo apt-get install -y ca-certificates curl gnupg lsb-release

sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL [https://download.docker.com/linux/ubuntu/gpg](https://download.docker.com/linux/ubuntu/gpg) \
  | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
  [https://download.docker.com/linux/ubuntu](https://download.docker.com/linux/ubuntu) \
  $(. /etc/os-release; echo "$VERSION_CODENAME") stable" \
  | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

sudo apt-get update -y
sudo apt-get install -y docker-ce docker-ce-cli containerd.io \
  docker-buildx-plugin docker-compose-plugin

sudo systemctl enable --now docker
sudo usermod -aG docker $USER

note

You may need to log out and log back in for the user group changes to take effect.

**2. Create a docker-compose.yaml file

This file defines the Milvus service and its dependencies (etcd and MinIO).

version: '3.8'

services:
  etcd:
    image: quay.io/coreos/etcd:v3.5.11
    container_name: etcd
    restart: unless-stopped
    command: >
      /usr/local/bin/etcd
      -advertise-client-urls http://etcd:2379
      -listen-client-urls [http://0.0.0.0:2379](http://0.0.0.0:2379)
      -data-dir /etcd-data
    volumes:
      - etcd_data:/etcd-data

  minio:
    image: minio/minio:latest
    container_name: minio
    restart: unless-stopped
    environment:
      MINIO_ROOT_USER: minioadmin
      MINIO_ROOT_PASSWORD: minioadmin
    command: server /data --console-address ":9001"
    ports:
      - "9000:9000"
      - "9001:9001"
    volumes:
      - minio_data:/data

  milvus:
    image: milvusdb/milvus:v2.4.10
    container_name: milvus
    restart: unless-stopped
    environment:
      ETCD_ENDPOINTS: etcd:2379
      MINIO_ADDRESS: minio:9000
    command: ["milvus", "run", "standalone"]
    depends_on:
      - etcd
      - minio
    ports:
      - "19530:19530"
      - "9091:9091"
    volumes:
      - milvus_data:/var/lib/milvus

volumes:
  etcd_data:
  minio_data:
  milvus_data:

*3. Start the Services

From the same directory, run the following command.

pip install pymilvus

You can verify that the services are running with docker ps.

Step 3: Prepare Collection and Query with Python

You can interact with your Milvus instance using the pymilvus client.

##1. Install the client:

pip install pymilvus

##2. Run the Python Script:

The following script connects to Milvus, creates a collection (if it doesn't exist), creates an index, and loads the collection into memory so it's ready for queries.

from pymilvus import (
    connections, utility, Collection, FieldSchema, CollectionSchema, DataType
)

# --- Connection Details ---
HOST = "localhost"  # Use your EC2 host IP if running remotely
PORT = "19530"
COLLECTION_NAME = "people_demo"
VECTOR_FIELD = "vec"
DIMENSION = 3

# --- Connect to Milvus ---
connections.connect("default", host=HOST, port=PORT)
print("Connected to Milvus.")

# --- Create collection if it's missing ---
if COLLECTION_NAME not in utility.list_collections():
    schema = CollectionSchema(
        fields=[
            FieldSchema(name="id",   dtype=DataType.INT64, is_primary=True, auto_id=False),
            FieldSchema(name="name", dtype=DataType.VARCHAR, max_length=255),
            FieldSchema(name="age",  dtype=DataType.INT32),
            FieldSchema(name=VECTOR_FIELD, dtype=DataType.FLOAT_VECTOR, dim=DIMENSION),
        ],
        description="People demo collection"
    )
    Collection(name=COLLECTION_NAME, schema=schema)
    print(f"Created collection: {COLLECTION_NAME}")
else:
    print(f"Collection '{COLLECTION_NAME}' already exists.")

col = Collection(COLLECTION_NAME)

# --- Create a vector index if one doesn't exist ---
if not col.has_index():
    print("Creating index on vector field...")
    col.create_index(
        field_name=VECTOR_FIELD,
        index_params={"index_type": "AUTOINDEX", "metric_type": "L2"}
    )
    print("Waiting for index to build...")
    utility.wait_for_index_building_complete(COLLECTION_NAME)
    print("Index is ready.")

# --- Load collection and query ---
print("Loading collection into memory...")
col.load()
print(f"Collection loaded. Number of entities: {col.num_entities}")

# Example: Query for records after inserting from Flink
rows = col.query(expr="id >= 1", output_fields=["id","name","age"])
print("Query results:")
print(rows)

Troubleshooting

• "Missing required options...": Verify that all WITH option names use the correct camelCase format (e.g., databaseName).

• "Index not found" or "Collection not loaded": Ensure you have created a vector index on your collection and have called the .load() command before querying.

• "Dimension mismatch": The length of the vector in your Flink ARRAY<FLOAT> data must exactly match the dim specified for the vector field in your Milvus collection schema.