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 # JobQueue and MI Scripting Toolkit

 Last update: 16.07.2025 

## Introduction

This example demonstrates how to use the Granta MI JobQueue library with the Scripting Toolkit. The JobQueue library replaces the AsyncJobQueue component previously available within the MI Scripting Toolkit, and allows interaction with the Granta MI Job Queue via Python.

The JobQueue library supports import and export operations. This example demonstrates how to use the MI Scripting Toolkit to define the records exported to Excel using an Excel template.

This example does not cover all aspects of the JobQueue package. Full API documentation and additional examples are available [here](https://jobqueue.grantami.docs.pyansys.com).

**Info:**

Running this notebook requires the `ansys-grantami-jobqueue` package. See the 'PyGranta Dependencies' section of the 'Example Notebooks' documentation page for more details.



## Define the records to export

First create an MI Scripting Toolkit session.

```python
from GRANTA_MIScriptingToolkit import granta as mpy

SERVICE_LAYER_URL = "http://my.server.name/mi_servicelayer"
mi = mpy.connect(SERVICE_LAYER_URL, autologon=True)

```

Next, access all records in a specific tree location. We will need the database key later, so store it in a constant.

```python
DB_KEY = "MI_Training"
db = mi.get_db(db_key=DB_KEY)
tensile_test_data = db.get_table("Tensile Test Data")

records = tensile_test_data.get_records_from_path(
    starting_node=None,
    tree_path=["High Alloy Steels", "AMS 6520", "Plate", "300°F"]
)
records

```

*Previous cell output:*

```output
[<record long name: mts-615723>,
 <record long name: mts-615733>,
 <record long name: mts-615743>,
 <record long name: mts-615753>,
 <record long name: mts-615763>,
 <record long name: mts-615773>]

```

## Create an 'ansys-grantami-jobqueue' client

Use the `Connection` class to connect to create a JobQueue client.

```python
from ansys.grantami.jobqueue import Connection, ExcelExportJobRequest, ExportRecord

api_client = Connection(SERVICE_LAYER_URL).with_autologon().connect()
api_client

```

*Previous cell output:*

```output
<jobqueueapiclient: url="http://my.server.name/mi_servicelayer&quot;">

```

## Export the records to Excel

All JobQueue operations are performed by first creating a `JobRequest` object, and then submitting that object to the server.

An `ExcelExportJobRequest` object requires the following:

- A name and description
- The path to an Excel export template, provided as either a string or a `pathlib.Path` object
- A list of `ExportRecord` objects, which contain a record history identity and optionally a record version
- The database key for the provided `ExportRecord` objects

First, create the list of `ExportRecord` objects from the list of `Record` objects.

```python
export_records = [ExportRecord(r.history_identity) for r in records]
export_records

```

*Previous cell output:*

```output
[ExportRecord(record_history_identity=120726, record_version=None),
 ExportRecord(record_history_identity=120727, record_version=None),
 ExportRecord(record_history_identity=120728, record_version=None),
 ExportRecord(record_history_identity=120729, record_version=None),
 ExportRecord(record_history_identity=120730, record_version=None),
 ExportRecord(record_history_identity=120731, record_version=None)]

```

Next, create the Excel export request.

```python
from pathlib import Path

request = ExcelExportJobRequest(
    name="Example export",
    description="A demonstration of how to export data to Excel using Granta MI JobQueue",
    template_file=Path("supporting_files/23_Export_Template.xlsx"),
    database_key=DB_KEY,
    records=export_records,
)
request

```

*Previous cell output:*

```output
<excelexportjobrequest: name:>

```

Finally, use the `JobQueue.create_job_and_wait` method to submit the `ExcelExportJobRequest`object to the server. This method returns once the job is complete.

```python
job = api_client.create_job_and_wait(job_request=request)
job

```

*Previous cell output:*

```output
<asyncjob: name: status:>

```

## Access the exported data

Once the job is complete, the list of files generated as part of the operation can be accessed via the `output_file_names` property.

```python
job.output_file_names

```

*Previous cell output:*

```output
['ExportedRecords-Tensile Test Data.xlsx', 'Example export.log']

```

The binary content of an individual file can be accessed with the `get_file_content` method, or alternatively the file can be saved to disk with the `download_file` method. See the JobQueue API documentation for a demonstration of these methods.

```python
api_client.delete_jobs([job])

```

&lt;/asyncjob:&gt;&lt;/excelexportjobrequest:&gt;&lt;/jobqueueapiclient:&gt;