Current state: Accepted
ISSUE: https://github.com/milvus-io/milvus/issues/15604
PRs:
Keywords: bulk load, import
Released: with Milvus 2.1
Authors:
Summary
Import data by a shortcut to get better performance compared with insert().
Motivation
- client calls insert() to transfer data to Milvus proxy node
- proxy split the data in do multiple parts according to sharding number of the collection
- proxy construct a message packet for each part, and send the message packets into Pulsar service
- data nodes pull the message packets from the Pulsar service, each data node pull a packet
- data nodes persist data into segment when flush() action is triggered
Typically, it cost several hours to insert one billion entities with 128-dimensional vectors. Lots of time is wasted in two major areas: network transmission and Pulsar management.
We can see there are at least three times network transmission in the process: 1) client => proxy 2) proxy => pulsar 3) pulsar => data node
We need a new interface to do bulk load without network bandwidth wasting and skip the Pulsar management. Brief requirements of the new interface:
- import data from JSON format files. (first stage)
- import data from Numpy format files. (first stage)
- copy a collection within one Milvus 2.0 service. (second stage)
- copy a collection from one Milvus 2.0 service to another. (second stage)
- import data from Milvus 1.x to Milvus 2.0 (third stage)
- parquet/faiss files (TBD)
To reduce network transmission and skip Plusar management, the new interface will allow users to input the path of some data files(json, numpy, etc.) on MinIO/S3 storage, and let the data nodes directly read these files and parse them into segments. The internal logic of the process becomes:
1. client calls import() to pass some file paths to Milvus proxy node
2. proxy node passes the file paths to data coordinator
3. data coordinator pick a data node or multiple data node (according to the files count) to parse files, each file can be parsed to a segment or multiple segments.
Some points to consider:
- JSON format is flexible, ideally, the import API ought to parse user's JSON files without asking user to reformat the files according to a strict rule.
- Users can store scalar fields and vector fields in a JSON file, with row-based or column-based. The import() API can support both of them.
Store scalar fields and vector field data in a JSON file with row-based example:
{
"table": {
"rows": [
{"id": 1, "year": 2021, "vector": [1.0, 1.1, 1.2]},
{"id": 2, "year": 2022, "vector": [2.0, 2.1, 2.2]},
{"id": 3, "year": 2023, "vector": [3.0, 3.1, 3.2]}
]
}
}
Store scalar fields and vector field data in a JSON file with column-based example:
{
"table": {
"columns": [
"id": [1, 2, 3],
"year": [2021, 2022, 2023],
"vector": [
[1.0, 1.1, 1.2],
[2.0, 2.1, 2.2],
[3.0, 3.1, 3.2]
]
]
}
}
- Numpy file is a binary format, we only treat it as vector data. Each 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 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.
SDK Interfaces
Based on the several points, we choose a JSON object as a parameter of python import() API, the API declaration will be like this:
def import(options)
The parameter "options" is a JSON object which has the following format:
Note: to improve usability, we also can declare an ORM class to wrap this json object.
{
"data_source": { // required
"type": "Minio", // required
"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": "aaa" // 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", // field name mapping, key is the source field name, value is the target field name
"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
"chunks": [{ // required, chunk list, each chunk can be import as one segment or split into multiple segments
"files": [{ // required, files that provide data of a chunk
"path": "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, tell milvus how to parse this json file
"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", // field name mapping, tell milvus how to insert data to correct field, key is a json node path, value is a field name of the collection
"table.rows.year": "year",
"table.rows.vector": "vector"
}
}]
},
{
"files": [{
"path": "xxxx / xx.json",
"type": "column_based",
"fields_mapping": {
"table.columns.id": "uid",
"table.columns.year": "year",
"table.columns.vector": "vector"
}
}]
},
{
"files": [{
"file": "xxxx / xx.npy",
"type": "column_based",
"fields_mapping": {
"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 and database clone, not available in the first stage. It requires another new API export().
- "external_data": for importing data from user's files. Tell datanode where to read the data files and how to parse them.
How to pass this parameter in different situations:
Assume there is a collection named "TEST" with these fields:
{"uid":INT64, "year":INT32, "age":INT8, "embedding":FLOAT_VECTOR}
For the following situations:
User has some JSON files store data with the row-based format: file_1.json, file_2.json.
{ "data": { "rows": [ {"id": 1, "year": 2021, "vector": [1.0, 1.1, 1.2]}, {"id": 2, "year": 2022, "vector": [2.0, 2.1, 2.2]}, {"id": 3, "year": 2023, "vector": [3.0, 3.1, 3.2]} ] } }The "options" could be:
{ "data_source": { "type": "Minio", "address": "localhost:9000", "accesskey_id": "minioadmin", "accesskey_secret": "minioadmin", "use_ssl": false, "bucket_name": "mybucket" }, "external_data": { "target_collection": "TEST", "chunks": [{ "files": [{ "path": "xxxx/file_1.json", "type": "row_based", "fields_mapping": { "table.rows.id": "uid", "table.rows.year": "year", "table.rows.vector": "vector" } }] }, { "files": [{ "path": "xxxx/file_2.json", "type": "row_based", "fields_mapping": { "table.rows.id": "uid", "table.rows.year": "year", "table.rows.vector": "vector" } }] } ], "default_fields": { "age": 0 } } }User has some JSON files store data with the column-based format: file_1.json, file_2.json.
{ "table": { "columns": [ "id": [1, 2, 3], "year": [2021, 2022, 2023], "vector": [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], [3.0, 3.1, 3.2] ] ] } }The "options" could be:
{ "data_source": { "type": "Minio", "address": "localhost:9000", "accesskey_id": "minioadmin", "accesskey_secret": "minioadmin", "use_ssl": false, "bucket_name": "mybucket" }, "external_data": { "target_collection": "TEST", "chunks": [{ "files": [{ "path": "xxxx/file_1.json", "type": "column_based", "fields_mapping": { "table.rows.id": "uid", "table.rows.year": "year", "table.rows.vector": "vector" } }] }, { "files": [{ "path": "xxxx/file_2.json", "type": "column_based", "fields_mapping": { "table.rows.id": "uid", "table.rows.year": "year", "table.rows.vector": "vector" } }] } ], "default_fields": { "age": 0 } } }- User ha a JSON file store data with the column-based format: file_1.json, and a Numpy file store vectors data: file_2.npy
Note: for hybrid format files, we only allow inputting a pair of files to reduce the complexity.
The file_1.json:
{
"table": {
"columns": [
"id": [1, 2, 3],
"year": [2021, 2022, 2023],
"age": [23, 34, 21]
]
]
}
}
The "options" could be:
{
"data_source": {
"type": "Minio",
"address": "localhost:9000",
"accesskey_id": "minioadmin",
"accesskey_secret": "minioadmin",
"use_ssl": false,
"bucket_name": "mybucket"
},
"external_data": {
"target_collection": "TEST",
"files": [
{
"file": file_1.json,
"type": "column_based",
"fields_mapping": {
"data.columns.id": "uid",
"data.columns.year": "year",
"data.columns.age": "age",
}
},
{
"file": file_2.npy,
"type": "column_based",
"fields_mapping": {
"embedding": "embedding",
}
}
]
}
}
The "options" for other SDK is not JSON object. For Java SDk, a declaration could be:
public class ImportParam {
private MinioDataSource data_source;
private List<DataFile> external_files;
}
RPC Interfaces
The declaration of import API in RPC level:
rpc Import(ImportRequest) returns (MutationResult) {}
message ImportRequest {
common.MsgBase base = 1;
string options = 2;
}
message MutationResult {
common.Status status = 1;
schema.IDs IDs = 2; // return auto-id for insert/import, deleted id for delete
repeated uint32 succ_index = 3; // succeed indexes for insert
repeated uint32 err_index = 4; // error indexes for insert
bool acknowledged = 5;
int64 insert_cnt = 6;
int64 delete_cnt = 7;
int64 upsert_cnt = 8;
uint64 timestamp = 9;
}