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Typically, it cost several hours to insert one billion entities with 128-dimensional vectors. We need a new interface to do bulk load for the following purposes:
- import data from json JSON format files. (first stage)
- import data from numpy Numpy format files. (first stage)
- copy a collection within on Milvus 2.0 service. (second stage)
- copy a collection from one Milvus 2.0 server 0 service to another. (second stage)
- import data from Milvus 1.x to Milvus 2.0 (third stage)
- parquet/faiss files (TBD)
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- Numpy file is a binary format, we only treat it as vector data. Each numpy Numpy file represents a vector field.
- Transferring a large file from client to server proxy to datanode is time-consume work and occupies too much network bandwidth, we will ask user users to upload data files to MinIO/S3 where the datanode can access directly. Let the datanode read and parse files from MinIO/S3.
- Users may store scalar fields and vector fields in different format files. For example, store scalar fields in JSON files and store vector fields in Numpy files.
- The parameter of import API is easy to expand in future
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| Code Block |
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{
"data_source": { // required
"type": "Miniominio", // required, currently only support "minio"/"s3"
"address": "localhost:9000", // optional, milvus server will use its minio setting if without this value
"accesskey_id": "minioadmin", // optional, milvus server will use its minio setting if without this value
"accesskey_secret": "minioadmin", // optional, milvus server will use its minio setting if without this value
"use_ssl": false, // optional, milvus server will use its minio setting if without this value
"bucket_name": "mybucket" // optional, milvus server will use its minio setting if without this value
},
"internal_data": { // optional, external_data or internal_data. (external files include json, npy, etc. internal files are exported by milvus)
"path": "xxx/xxx/xx", // relative path to the source storage where store the exported data
"collections_mapping": { // optional, give a new name to collection during importing.
"aaa": "bbb",
"ccc": "ddd"
}
},
"external_data": { // optional, external_data or internal_data. (external files include json, npy, etc. internal files are exported by milvus)
"target_collection": "xxx", // target collection name
"files": [ // required
{
"file": xxxx/xx.json, // required, relative path under the storage source defined by DataSource, currently support json/npy
"type": "row_based", // required, row_based or column_based
"fields_mapping": { // optional, specify the target fields which should be imported. Milvus will import all fields if this list is empty.
"table.rows.id": "uid",
"table.rows.year": "year",
"table.rows.vector": "vector",
}
},
{
"file": xxxx/xx.json, // required, relative path under the storage source defined by DataSource, currently support json/npy
"type": "column_based", // required, row_based or column_based
"fields_mapping": { // optional, specify the target fields which should be imported. Milvus will import all fields if this list is empty.
"table.columns.id": "uid",
"table.columns.year": "year",
"table.columns.vector": "vector",
}
},
{
"file": xxxx/xx.npy, // required, relative path under the storage source defined by DataSource, currently support json/npy
"type": "column_based", // required, row_based or column_based
"fields_mapping": { // optional, specify the target fields which should be imported. Milvus will import all fields if this list is empty.
"vector": "vector",
}
}
],
"default_fields": { // optional, use default value to fill some fields
"age": 0,
"weight": 0.0
},
}
} |
Key fields of the JSON object:
- "data_source": contains the address and login methods of MinIO/S3. If the address and login methods are not provided, Milvus will use its MinIO/S3 configurations.
- "internal_data": reserved field for collection clone, not available in the first stage. It requires another API export().
- "external_data": for importing data from user's files. Let datanode where to read the data files and how to parse them.
RPC Interfaces
Internal machinery
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