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def import(collection_name,  files, row_based, partition_name=None, options=None)

• collection_name:  the target collection name  (required)
• partition_name: target partition name  (optional)
• row_based: a boolean to specify row-based or column-based
• files: a list of files with row-based format or a dict of files with column-based format  format files (required)
  row-based files:   ["file_1.json", "file_2.json"]
  column-based files:  {"id": "id["file_1.json", "vectors": "embeddings.npy"}]
  
  
• options: extra options in JSON format, for example: the MinIO/S3 bucket where the files come from  (optional)
  {"bucket": "mybucket"}
• return a list of task ids

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            Note: the "state" could be "pending", "started"parsing, "downloaded", "persistedparsed", "indexingpersisted", "completed", "failed"

Pre-defined format for import files

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{
  "rows":[
    {"uid": 101, "vector": [1.1, 1.2, 1.3, 1.4]},
    {"uid": 102, "vector": [2.1, 2.2, 2.3, 2.4]},
    {"uid": 103, "vector": [3.1, 3.2, 3.3, 3.4]},
    {"uid": 104, "vector": [4.1, 4.2, 4.3, 4.4]},
    {"uid": 105, "vector": [5.1, 5.2, 5.3, 5.4]},
  ]
}

Call import() to import the file:

import(collection_name="test", files=[row_based = true, files=["file_1.json"])

(2) Column-based data file, each  a JSON file represents a column. We require the keyword "values" as a key of the field data.In this case, there are two fields, so we create 2 JSON files:contains multiple columns.
file_1.json for the "uid" field and "vector" field:

{
  "valuesuid": [101, 102, 103, 104, 105]
}

file_2.json for the "vector" field:

{
  "values":   "vector": [[1.1, 1.2, 1.3, 1.4], [2.1, 2.2, 2.3, 2.4], [3.1, 3.2, 3.3, 3.4], [4.1, 4.2, 4.3, 4.4], [5.1, 5.2, 5.3, 5.4]]
}

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import(collection_name="test", row_based=false, files={"uid": ["file_1.json", "vector": "file_2.json"}])

We also support store vectors in a Numpy file, letwe require the numpy file's say the "name is equal to the filed name. Let's say the "vector" field is stored in vector.npy, the "uid" field is stored in "file_21.npyjson", then we can call import():

import(collection_name="test", row_based=false, files={"uid": ["file_1.json", "vector": "file_2.npy"}])

Error handling

The Import():

Note: for column-based, we don't support multiple json files, all columns should be stored in one json file. If user use a numpy file to store vector field, then other scalar fileds should be stored in one json file.

Error handling

The Import():

• Return Return error "Collection doesn't exist" if the target collection doesn't exist
• Return error "Party Partition doesn't exist" if the target partition doesn't exist
• Return error "Bucket doesn't exist" if the target bucket doesn't exist
• Return error "File list is empty" if the row-based files list is empty
• ImportTask pending list has limit size, if a new import request exceed the limit size, return error "Import task queue max size is xxx, currently there are xx pending tasks. Not able to execute this request with x tasks."

The get_import_state():

• Return error "File xxx doesn't exist" if could not open the file. 
• All fields must be presented, otherwise, return the error "The field xxx is not provided"
• For For row-based files, all fields must be presented, otherwise, return the error "The field xxx is not provided"json files, return "not a valid row-based json format, the key rows not found" if could not find the "rows" node
• For column-based files, each field must correspond to a file, otherwiseif a vector field is duplicated in numpy file and json file, return the error "The field xxx is not provided"

The get_import_state():

• duplicated
• Return error "File xxx doesn't exist" if could not open the file. json parse error: xxxxx" if encounter illegal json format
• The row count of each field must be equal, otherwise, return the error "Inconsistent row count between field xxx and xxx". (all segments generated by this file will be abandoned)
• If a vector dimension doesn't equal to field schema, return the error "Incorrect vector dimension for field xxx". (all segments generated by this file will be abandoned)
• If a data file size exceed 1GB, return error "Data file size must be less than 1GB"
• If an import task is no response for more than 6 hours, it will be marked as failed
• If datanode is crashed or restarted, the import task on it will be marked as failed

2. Proxy RPC Interfaces

service MilvusService {
  rpc Import(ImportRequest) returns (ImportResponse) {}
  rpc GetImportState(GetImportStateRequest) returns (GetImportStateResponse) {}
}

message ImportRequest {
  string collection_name = 1;                // target collection
  string partition_name = 2;                 // target partition
  bool row_based = 3;                        // the file is row-based or column-based
  repeated string files = 4;                 // file paths to be imported
  repeated common.KeyValuePair options = 5;  // import options, bucket, etc.
}

message ImportResponse {
  common.Status status = 1;
  repeated int64 tasks = 2;  // id array of import tasks
}

message GetImportStateRequest {
  int64 task = 1;  // id of an import task
}

enum ImportState {
    ImportPending = 0;
    ImportFailed = 1;
    ImportDownloaded = 2;
    ImportParsed = 3;
    ImportPersisted = 4;
    ImportCompleted = 5;
}

message GetImportStateResponse {
  common.Status status = 1;
  ImportState state = 2;                   // is this import task finished or not
  int64 row_count = 3;                     // if the task is finished, this value is how many rows are imported. if the task is not finished, this value is how many rows are parsed. return 0 if failed.
  repeated int64 id_list = 4;              // auto generated ids if the primary key is autoid
  repeated common.KeyValuePair infos = 5;  // more informations about the task, progress percent, file path, failed reason, etc.
}

The call chain of import worflow:

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3. Rootcoord RPC interfaces

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service RootCoord {
  rpc Import(milvus.ImportRequest) returns (milvus.ImportResponse) {}
  rpc GetImportState(milvus.GetImportStateRequest) returns (milvus.GetImportStateResponse) {}
  rpc ReportImport(ImportResult) returns (common.Status) {}
}

message ImportResult {
  common.Status status = 1;
  int64 task_id = 2;                       // id of the task
  common.ImportState state = 3;            // state of the task
  repeated int64 segments = 4;             // id array of new sealed segments
  repeated int64 auto_ids = 5;             // auto-generated ids for auto-id primary key
  int64 row_count = 6;                     // how many rows are imported by this task
  repeated common.KeyValuePair infos = 7;  // more informations about the task, file path, failed reason, etc.
}

The call chain of import worflow:

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4. Datacoord RPC interfaces

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service DataCoord {
  rpc Import(ImportTask) returns (ImportTaskResponse) {}
}

message ImportTask {
  common.Status status = 1;
  string collection_name = 2;                // target collection
  string partition_name = 3;                 // target partition
  bool row_based = 4;                        // the file is row-based or column-based
  int64 task_id = 5;                         // id of the task
  repeated string files = 6;                 // file paths to be imported
  repeated common.KeyValuePair infos = 7;    // more informations about the task, bucket, etc.
}

message ImportTaskResponse {
  common.Status status = 1;
  int64 datanode_id = 2;                     // which datanode takes this task
}

The relationship between ImportRequest and ImportTask:

For row-based request, the RootCoord splits the request into multiple ImportTask, each json file is a ImportTask.

For column-based request, all files will be regarded as one ImportTask.

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5. Datanode interfaces

The declaration of import API in datanode RPC:

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To achieve this property, the segments shall be marked as "LoadingImporting" state and be invisible before the whole loading procedure completes.

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