从Milvus迁移DashVector

本文档演示如何从Milvus将Collection数据全量导出，并适配迁移至DashVector。方案的主要流程包括：

1. 首先，升级Milvus版本，目前Milvus只有在最新版本(v.2.3.x)中支持全量导出
2. 其次，将Milvus Collection的Schema信息和数据信息导出到具体的文件中
3. 最后，以导出的文件作为输入来构建DashVector Collection并数据导入

下面，将详细阐述迁移方案的具体操作细节。

1. Milvus升级2.3.x版本

本文中，我们将借助Milvus的query_iterator来全量导出数据（query接口无法导出完整数据），由于该接口目前只在v2.3.x版本中支持，所以在导出数据前，需要先将Milvus版本升级到该版本。Milvus版本升级的详细操作参考Milvus用户文档。

注意：在进行Milvus Upgrade时需要注意数据的备份安全问题。

2. Milvus全量数据导出

数据的导出包含Schema以及数据记录，Schema主要用于完备地定义Collection，数据记录对应于每个Partition下的全量数据，这两部分涵盖了需要导出的全部数据。下文展示如何将单个Milvus Collection全量导出。

2.1. Schema导出

DashVector和Milvus在Schema的设计上有一些区别，DashVector向用户透出的接口非常简单，Milvus则更加详尽。从Milvus迁移DashVector时会涉及到部分Schema参数的删除（例如Collection的index_param参数），只会保留DashVector构建Collection的必要参数，以下为一个Schema转换的简单示例（其中，Collection已有的数据参考Milvus示例代码写入）。

python示例：

from pymilvus import (
    connections,
    utility,
    Collection,
    DataType
)
import os
import json
from pathlib import Path

fmt = "\n=== {:30} ===\n"

print(fmt.format("start connecting to Milvus"))
host = os.environ.get('MILVUS_HOST', "localhost")
print(fmt.format(f"Milvus host: {host}"))
connections.connect("default", host=host, port="19530")

metrics_map = {
  'COSINE': 'cosine',
  'L2': 'euclidean',
  'IP': 'dotproduct',
}

dtype_map = {
  DataType.BOOL: 'bool',
  DataType.INT8: 'int',
  DataType.INT16: 'int',
  DataType.INT32: 'int',
  DataType.INT64: 'int',

  DataType.FLOAT: 'float',
  DataType.DOUBLE: 'float',

  DataType.STRING: 'str',
  DataType.VARCHAR: 'str',
}

def load_collection(collection_name: str) -> Collection:
  has = utility.has_collection(collection_name)
  print(f"Does collection hello_milvus exist in Milvus: {has}")
  if not has:
    return None

  collection = Collection(collection_name)      
  collection.load()
  
  return collection
  
def export_collection_schema(collection, file: str):
  schema = collection.schema.to_dict()
  index = collection.indexes[0].to_dict()
  
  export_schema = dict()
  
  milvus_metric_type = index['index_param']['metric_type']
  try:
    export_schema['metrics'] = metrics_map[milvus_metric_type]
  except:
    raise Exception(f"milvus metrics_type{milvus_metric_type} not supported")
  export_schema['fields_schema'] = {}
  
  for field in schema['fields']:
    if 'is_primary' in field and field['is_primary']:
      continue
    
    if field['name'] == index['field']:
      # vector
      if field['type'] == DataType.FLOAT_VECTOR:
        export_schema['dtype'] = 'float'
        export_schema['dimension'] = field['params']['dim']
      else:
        raise Exception(f"milvus dtype{field['type']} not supported yet")
    else:
      try:
        # non-vector
        export_schema['fields_schema'][field['name']] = dtype_map[field['type']]
      except:
        raise Exception(f"milvus dtype{field['type']} not supported yet")
  
  with open(file, 'w') as file:
    json.dump(export_schema, file, indent=4)  
  
if __name__ == "__main__":
  collection_name = "YOUR_MILVUS_COLLECTION_NAME"
  collection = load_collection(collection_name)
  
  dump_path_str = collection_name+'.dump'
  dump_path = Path(dump_path_str)
  dump_path.mkdir(parents=True, exist_ok=True)
  
  schema_file = dump_path_str + "/schema.json"
  export_collection_schema(collection, schema_file)

JSON示例：

{
    "metrics": "euclidean",
    "fields_schema": {
        "random": "float",
        "var": "str"
    },
    "dtype": "float",
    "dimension": 8
}

2.2. Data导出

DashVector和Milvus在设计上都有Partition的概念，所以向量以及其他数据进行导出时，需要注意按照Partition粒度进行导出。此外，DashVector的主键类型为str，而Milvus设计其为自定义类型，所以在导出时需要考虑主键类型的转换。以下为一个基于query_iterator接口导出的简单代码示例：

from pymilvus import (
    connections,
    utility,
    Collection,
    DataType
)
import os
import json
import numpy as np
from pathlib import Path

fmt = "\n=== {:30} ===\n"

print(fmt.format("start connecting to Milvus"))
host = os.environ.get('MILVUS_HOST', "localhost")
print(fmt.format(f"Milvus host: {host}"))
connections.connect("default", host=host, port="19530")
pk = "pk"
vector_field_name = "vector"

def load_collection(collection_name: str) -> Collection:
  has = utility.has_collection(collection_name)
  print(f"Does collection hello_milvus exist in Milvus: {has}")
  if not has:
    return None

  collection = Collection(collection_name)      
  collection.load()
  
  return collection
  
def export_partition_data(collection, partition_name, file: str):
  batch_size = 10
  output_fields=["pk", "random", "var", "embeddings"]
  query_iter = collection.query_iterator(
    batch_size=batch_size,
    output_fields = output_fields,
    partition_names=[partition_name]
  )
  
  export_file = open(file, 'w')
  
  while True:
    docs = query_iter.next()
    if len(docs) == 0:
      # close the iterator
      query_iter.close()
      break
    for doc in docs:
      new_doc = {}
      new_doc_fields = {}
      for k, v in doc.items():
        if k == pk:
          # primary key
          new_doc['pk'] = str(v)
        elif k == vector_field_name:
          new_doc['vector'] = [float(k) for k in v]
        else:
          new_doc_fields[k] = v
      new_doc['fields'] = new_doc_fields
      json.dump(new_doc, export_file)
      export_file.write('\n')
      
  export_file.close()
 
if __name__ == "__main__":
  collection_name = "YOUR_MILVUS_COLLECTION_NAME"
  collection = load_collection(collection_name)
  
  pk = collection.schema.primary_field.name
  vector_field_name = collection.indexes[0].field_name
  
  dump_path_str = collection_name+'.dump'
  dump_path = Path(dump_path_str)
  dump_path.mkdir(parents=True, exist_ok=True)

  for partition in collection.partitions:
    partition_name = partition.name
    if partition_name == '_default':
      export_path = dump_path_str + '/default.txt'
    else:
      export_path = dump_path_str + '/' + partition_name + ".txt"
    export_partition_data(collection, partition_name, export_path)

3. 将数据导入DashVector

3.1. 创建Cluster

参考DashVector官方用户手册构建Cluster。

3.2. 创建Collection

根据2.1章节中导出的Schema信息以及参考Dashvector官方用户手册来创建Collection。下面的示例代码会根据2.1章节中导出的schema.json来创建一个DashVector的Collection。

from dashvector import Client, DashVectorException

from pydantic import BaseModel
from typing import Dict, Type
import json

dtype_convert = {
  'int': int,
  'float': float,
  'bool': bool,
  'str': str
}

class Schema(BaseModel):
  metrics: str
  dtype: Type
  dimension: int
  fields_schema: Dict[str, Type]
  
  @classmethod
  def from_dict(cls, json_data):
    metrics = json_data['metrics']
    dtype = dtype_convert[json_data['dtype']]
    dimension = json_data['dimension']
    fields_schema = {k: dtype_convert[v] for k, v in json_data['fields_schema'].items()}
    return cls(metrics=metrics, dtype=dtype, dimension=dimension, fields_schema=fields_schema)

def read_schema(schema_path) -> Schema:
  with open(schema_path) as file:
    json_data = json.loads(file.read())
    
  return Schema.from_dict(json_data)

if __name__ == "__main__":
  milvus_dump_path = f"{YOUR_MILVUS_COLLECTION_NAME}.dump"
  milvus_dump_scheme_path = milvus_dump_path + "/schema.json"
  schema = read_schema(milvus_dump_scheme_path)
  
  client = dashvector.Client(
    api_key='YOUR_API_KEY',
    endpoint='YOUR_CLUSTER_ENDPOINT'
  )
  
  # create collection
  rsp = client.create(name="YOUR_DASHVECTOR_COLLECTION_NAME", 
                      dimension=schema.dimension, 
                      metric=schema.metrics, 
                      dtype=schema.dtype,
                      fields_schema=schema.fields_schema)
  if not rsp:
      raise DashVectorException(rsp.code, reason=rsp.message)

3.3. 导入Data

根据2.2章节中导出的数据以及参考DashVector官方用户手册来批量插入Doc。下面的示例代码会依次解析各个Partition导出的数据，然后依次创建DashVector下的Partition并导入数据。

from dashvector import Client, DashVectorException, Doc

from pydantic import BaseModel
from typing import Dict, Type
import json
import glob
from pathlib import Path

def insert_data(collection, partition_name, partition_file):
  if partition_name != 'default':
    rsp = collection.create_partition(partition_name)
    if not rsp:
        raise DashVectorException(rsp.code, reason=rsp.message)
    
  with open(partition_file) as f:
    for line in f:
      if line.strip():
        json_data = json.loads(line)
        rsp = collection.insert(
          [
            Doc(id=json_data['pk'], vector=json_data['vector'], fields=json_data['fields'])
          ]
        )
        if not rsp:
          raise DashVectorException(rsp.code, reason=rsp.message)  

if __name__ == "__main__":
  milvus_dump_path = f"{YOUR_MILVUS_COLLECTION_NAME}.dump"

  client = dashvector.Client(
    api_key='YOUR_API_KEY',
    endpoint='YOUR_CLUSTER_ENDPOINT'
  )
  
  # create collection
  collection = client.get("YOUR_DASHVECTOR_COLLECTION_NAME")
  
  partition_files = glob.glob(milvus_dump_path+'/*.txt', recursive=False)
  for partition_file in partition_files:
    # create partition
    partition_name = Path(partition_file).stem
    insert_data(collection, partition_name, partition_file)