<font style="color:rgb(24, 24, 24);">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：</font>
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。
<h1 id="Jgv8L">Milvus升级2.3.x版本</h1>
本文中，我们将借助Milvus的[query_iterator](https://milvus.io/docs/with_iterators.md)来全量导出数据（[query](https://milvus.io/docs/query.md)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](https://milvus.io/docs/upgrade_milvus_standalone-operator.md)。
:::info
注意：在进行Milvus Upgrade时需要注意数据的备份安全问题。
:::
<h1 id="oRmlt">Milvus全量数据导出</h1>
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。
<h2 id="bb950">Schema导出</h2>
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](https://raw.githubusercontent.com/zilliztech/milvus-backup/main/example/prepare_data.py)写入）。
```python
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)
```
以下是一个可用于创建DashVector Collection的schema文件示例。
```json
{
"metrics": "euclidean",
"fields_schema": {
"random": "float",
"var": "str"
},
"dtype": "float",
"dimension": 8
}
```
<h2 id="XBXQa">Data导出</h2>
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](https://milvus.io/docs/with_iterators.md)接口导出的简单代码示例：
```python
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)
```
上述示例代码会将Milvus Collection的各个Partition分别进行数据导出，导出后的文件结构如下图所示：
```latex
# collection_name = hello_milvus
hello_milvus.dump/
├── default.txt
└── schema.json
```
<h1 id="xtXQm">将数据导入DashVector</h1>
<h2 id="Z0YPA">创建Cluster</h2>
参考DashVector官方[用户手册](https://help.aliyun.com/document_detail/2631966.html?spm=a2c4g.2631965.0.0.33485425aqhYvz)构建Cluster。
<h2 id="VEdmA">创建Collection</h2>
根据2.1章节中导出的Schema信息以及参考Dashvector官方[用户手册](https://help.aliyun.com/document_detail/2568085.html?spm=a2c4g.2631966.0.0.153c1afcNYc6rW)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。
```python
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)
```
<h2 id="PiA4M">导入Data</h2>
根据2.2章节中导出的数据以及参考DashVector官方[用户手册](https://help.aliyun.com/document_detail/2510249.html?spm=a2c4g.2510248.0.0.49ef7738NuI0kM#aa59e950508ld)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。
```python
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)
```
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