vdk-ipython 0.2.1190994517

Ipython extension for VDK

vdk-ipython

Ipython extension for VDK

This extension introduces a magic command for Jupyter. The command enables the user to load job_input for his current data job and use it freely while working with Jupyter.

See more about magic commands: https://ipython.readthedocs.io/en/stable/interactive/magics.html

Installation

To use the extension it must be firstly installed with pip as a python package.

pip install vdk-ipython

Usage

Then to load the extension in Jupyter the user should use:

%reload_ext vdk.plugin.ipython

Data Job Python coding cells

And to load the VDK (Job Control object):

%reload_VDK

The %reload_VDK magic can be used with arguments:

Argument	Description
--path	the path of the data job. Usually you want to leave the default (the directory of Notebook file)
--name	the name of the data job. Usually you want to leave the default (the directory name of the Notebook file)
--arguments	Arguments (in json format) to be passed to the job
--log-level-vdk	The log level of the VDK logs

Data Job SQL Cells

You can also specify %%vdksql cell magic to convert cell into SQL cell which will using Job Input Managed Connection

%vdksql
select * from my_table

The output of cell will be a table. If ipyaggrid is installed then the table would ipyaggrid type of table which allows filtering, search and other cool things

Example

The output of this example is "myjob"

%reload_ext vdk.plugin.ipython

%reload_VDK --name=myjob

response = requests.get("https://jsonplaceholder.typicode.com/todos/1")

job_input.send_object_for_ingestion(
    payload=response.json(), destination_table="placeholder_todo"
)

%%vdksql
select * from placeholder_todo
where completed = True

Ingesting data with %%vdkingest

%%vdkingest

# Data Source Configuration
[sources.yourSourceId]
## Data Source Name. Installed dta sources can be seen using vdk data-sources --list
name = "<data-source-name>"
## The singer tap we will use
config = {
    ## Set the configuration for the data source.
    ## You can see what config options are supported with vdk data-sources --config <data-source-name>
}

[sources.yourSourceId_2]
# repeat this for as many sources you want
# ...

# Data Destination Configuration.
## Ingestion methods and targets are the same one as those accepted by send_object_for_ingestion
## See https://github.com/vmware/versatile-data-kit/blob/main/projects/vdk-core/src/vdk/api/job_input.py#L183
[destinations.yourDestinationId]
## the only required parameter is method
method = "<method-name>"
## Optionally specify target
## target =

[destinations.yourDestinationId_2]
# repeat this for as many destinations you want
# ...

# Data Flows from Source to Destination
[[flows]]
from = "yourSourceId"
to = "yourDestinationId"

[[flows]]
from = "yourSourceId_2"
to = "yourDestinationId_2"

Complete the full self-paced tutorial at https://bit.ly/vdk-ingest

Build and testing

pip install -r requirements.txt
pip install -e .
pytest

In VDK repo ../build-plugin.sh script can be used also.

Note about the CICD:

.plugin-ci.yaml is needed only for plugins part of Versatile Data Kit Plugin repo.

The CI/CD is separated in two stages, a build stage and a release stage. The build stage is made up of a few jobs, all which inherit from the same job configuration and only differ in the Python version they use (3.7, 3.8, 3.9, 3.10 and 3.11). They run according to rules, which are ordered in a way such that changes to a plugin's directory trigger the plugin CI, but changes to a different plugin does not.