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Migration
You can follow this guide to easily migrate data from/to Milvus with following options:
You can save data in Milvus as HDF5 files using MilvusDM.
1. Download M2H.yaml:
wget https://raw.githubusercontent.com/milvus-io/milvus-tools/main/yamls/M2H.yaml
2. Set parameters:
-
source_milvus_path
: working directory of Milvus -
mysql_parameter
: MySQL settings for Milvus (if MySQL is not used, set this parameter as '') -
source_collection
: names of the collection and its partitions in Milvus -
data_dir
: directory to save HDF5 files
Example:
M2H:
milvus_version: 2.x
source_milvus_path: '/home/user/milvus'
mysql_parameter:
host: '127.0.0.1'
user: 'root'
port: 3306
password: '123456'
database: 'milvus'
source_collection: # specify the 'partition_1' and 'partition_2' partitions of the 'test' collection.
test:
- 'partition_1'
- 'partition_2'
data_dir: '/home/user/data'
3. Run MilvusDM:
$ milvusdm --yaml M2H.yaml
Sample Code:
- Read the data under milvus/db on your local drive, and retrieve vectors and their corresponding IDs from Milvus according to the metadata of the specified collection or partitions:
collection_parameter, version = milvus_meta.get_collection_info(collection_name)
r_vectors, r_ids, r_rows = milvusdb.read_milvus_file(self.milvus_meta, collection_name, partition_tag)
- Save the retrieved data as HDF5 files:
data_save.save_yaml(collection_name, partition_tag, collection_parameter, version, save_hdf5_name)
You can migrate HDF5 files to Milvus using MilvusDM.
1. Download H2M.yaml:
wget https://raw.githubusercontent.com/milvus-io/milvus-tools/main/yamls/H2M.yaml
2. Set parameters:
-
data_path
: path to the HDF5 file -
data_dir
: directory of the HDF5 files -
dest_host
: Milvus server address -
dest_port
: Milvus server port -
mode
: mode of migration-
Skip
: skip data migration if the specified collection or partition already exists -
Append
: append data if the specified collection or partition already exists -
Overwrite
: delete existing data before insertion if the specified collection or partition already exists
-
-
dest_collection_name
: name of the collection to import data to -
dest_partition_name
: name of the partition to import data to -
collection_parameter
: collection-specific information such as vector dimension, index file size, and similarity metric
Note:
Set either data_path or data_dir. Do not set both. Use data_path to specify multiple file paths, or data_dir to specify the directory holding your HDF5 files.
Example:
H2M:
milvus-version: 2.x
data_path:
- /Users/zilliz/float_1.h5
- /Users/zilliz/float_2.h5
data_dir:
dest_host: '127.0.0.1'
dest_port: 19530
mode: 'overwrite' # 'skip/append/overwrite'
dest_collection_name: 'test_float'
dest_partition_name: 'partition_1'
collection_parameter:
dimension: 128
index_file_size: 1024
metric_type: 'L2'
3. Run MilvusDM:
$ milvusdm --yaml H2M.yaml
Sample Code:
- Read the HDF5 files to retrieve vectors and their corresponding IDs:
vectors, ids = self.file.read_hdf5_data()
- Insert the retrieved data into Milvus:
ids = insert_milvus.insert_data(vectors, self.c_name, self.c_param, self.mode, ids,self.p_name)
You can migrate data from Faiss to Milvus using MilvusDM.
1. Download F2M.yaml:
wget https://raw.githubusercontent.com/milvus-io/milvus-tools/main/yamls/F2M.yaml
2. Set parameters:
-
data_path
: path to the data in Faiss -
dest_host
: Milvus server address -
dest_port
: Milvus server port -
mode
: mode of migration-
Skip
: skip data migration if the specified collection or partition already exists -
Append
: append data if the specified collection or partition already exists -
Overwrite
: delete existing data before insertion if the specified collection or partition already exists
-
-
dest_collection_name
: name of the collection to import data to -
dest_partition_name
: name of the partition to import data to -
collection_parameter
: Collection-specific information such as vector dimension, index file size, and similarity metric
Example:
F2M:
milvus_version: 2.x
data_path: '/home/data/faiss.index'
dest_host: '127.0.0.1'
dest_port: 19530
mode: 'append' # 'skip/append/overwrite'
dest_collection_name: 'test'
dest_partition_name: ''
collection_parameter:
dimension: 256
index_file_size: 1024
metric_type: 'L2'
3. Run MilvusDM:
$ milvusdm --yaml F2M.yaml
Sample Code:
- Read Faiss data files to retrieve vectors and their corresponding IDs:
ids, vectors = faiss_data.read_faiss_data()
- Insert the retrieved data into Milvus:
insert_milvus.insert_data(vectors, self.dest_collection_name, self.collection_parameter, self.mode, ids, self.dest_partition_name)
You can use MilvusDM for Milvus version migration.
Note:
MilvusDM does not support migrating data from Milvus 2.0 standalone to Milvus 2.0 cluster.
1. Download M2M.yaml:
wget https://raw.githubusercontent.com/milvus-io/milvus-tools/main/yamls/M2M.yaml
2. Set parameters:
-
source_milvus_path
: working directory of the source Milvus -
mysql_parameter
: MySQL settings for the source Milvus (if MySQL is not used, set this parameter as '') -
source_collection
: names of the collection and its partitions in the source Milvus -
dest_host
: target Milvus server address -
dest_port
: target Milvus server port -
mode
: mode of migration-
Skip
: skip data migration if the specified collection or partition already exists -
Append
: append data if the specified collection or partition already exists -
Overwrite
: delete existing data before insertion if the specified collection or partition already exists.
-
Example:
M2M:
milvus_version: 2.x
source_milvus_path: '/home/user/milvus'
mysql_parameter:
host: '127.0.0.1'
user: 'root'
port: 3306
password: '123456'
database: 'milvus'
source_collection:
test:
- 'partition_1'
- 'partition_2'
dest_host: '127.0.0.1'
dest_port: 19530
mode: 'skip' # 'skip/append/overwrite'
3. Run MilvusDM:
$ milvusdm --yaml M2M.yaml
Sample Code:
- Read the data under milvus/db on your local drive, and retrieve vectors and their corresponding IDs from the source Milvus according to the metadata of the specified collections or partitions:
collection_parameter, _ = milvus_meta.get_collection_info(collection_name)
r_vectors, r_ids, r_rows = milvusdb.read_milvus_file(self.milvus_meta, collection_name, partition_tag)
- Insert the retrieved vectors and the corresponding IDs into the target Milvus:
milvus_insert.insert_data(r_vectors, collection_name, collection_parameter, self.mode, r_ids, partition_tag)