Issue: when pyTigerGraph runs loading job

Drivers and Connectors pyTigerGraph
1

An error occurred When I run the following code to load my data into the database

directory = r'load_data/GSOurban/'
for dataFile in os.scandir(directory):
    if dataFile.path.endswith(".csv"):
        fileName = dataFile.name.split('.')[0]
        print(fileName)
        results = conn.uploadFile(dataFile, fileTag=fileName, jobName='load_vertex')
        print(json.dumps(results, indent=2))

The error information is as follows

image
image1515×315 32.2 KB

NewConsumer is my file name

vertex is defined as follows

CREATE VERTEX Consumer(
  PRIMARY_ID id STRING,
  Code STRING,
  Phases INT,
  NVolt_kV FLOAT,
  DemP_kW FLOAT,
  DemQ_kVAr FLOAT,
  Pinst_kW FLOAT,
  Qinst_kVAr FLOAT,
  Yearly_kWh FLOAT,
  NumCust INT,
  longitude FLOAT,
  latitude FLOAT)

and the content of the file is like this

image
image2035×138 11.7 KB

this is load job code

DEFINE FILENAME NewConsumer;
LOAD NewConsumer TO VERTEX Consumer VALUES($0, $0, $1, $2, $3, $4, $7, $8, $9, $10, $11, $12)
USING header="true", separator=",";

I have carefully checked the file name, variable name, column name and column index number, and it seems that there is no problem

Several files in the folder have been loaded successfully, but the newconsumer prompts failure. I don’t know what the error message means. I want to know how to solve this error。
who can help me :sob:

2

@kwonder

REST-10005 : There was an exception when processing results.
Error Codes Documentation

We’ve confirmed it’s due to limitations on file size with data loading with the TigerGraph system configs. We’re actively working on an implementation that will remove all data loading limitations which should be ready for testing in the next few days. Would you be interested in testing this capability?

1 Like
3

@Jon_Herke
Thank you for your reply!
I have avoided this problem by using conn.upsertvertexdataframe() , but if there are new features or functions, I am very happy to experience them

4

@kwonder With the new data loading method you should have no size restrictions with a 5x+ multitude of performance increase due to loading via multi parallel processing. I’ll reply here once we’ve finished up the testing.

1 Like